WorldWideScience

Sample records for spatial factor analysis

  1. Spatial econometric analysis of factors influencing regional energy efficiency in China.

    Science.gov (United States)

    Song, Malin; Chen, Yu; An, Qingxian

    2018-05-01

    Increased environmental pollution and energy consumption caused by the country's rapid development has raised considerable public concern, and has become the focus of the government and public. This study employs the super-efficiency slack-based model-data envelopment analysis (SBM-DEA) to measure the total factor energy efficiency of 30 provinces in China. The estimation model for the spatial interaction intensity of regional total factor energy efficiency is based on Wilson's maximum entropy model. The model is used to analyze the factors that affect the potential value of total factor energy efficiency using spatial dynamic panel data for 30 provinces during 2000-2014. The study found that there are differences and spatial correlations of energy efficiency among provinces and regions in China. The energy efficiency in the eastern, central, and western regions fluctuated significantly, and was mainly because of significant energy efficiency impacts on influences of industrial structure, energy intensity, and technological progress. This research is of great significance to China's energy efficiency and regional coordinated development.

  2. Spatial Factor Analysis for Aerosol Optical Depth in Metropolises in China with Regard to Spatial Heterogeneity

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    Hui Shi

    2018-04-01

    Full Text Available A substantial number of studies have analyzed how driving factors impact aerosols, but they have been little concerned with the spatial heterogeneity of aerosols and the factors that impact aerosols. The spatial distributions of the aerosol optical depth (AOD retrieved by Moderate Resolution Imaging Spectrometer (MODIS data at 550-nm and 3-km resolution for three highly developed metropolises, the Beijing-Tianjin-Hebei (BTH region, the Yangtze River Delta (YRD, and the Pearl River Delta (PRD, in China during 2015 were analyzed. Different degrees of spatial heterogeneity of the AOD were found, which were indexed by Moran’s I index giving values of 0.940, 0.715, and 0.680 in BTH, YRD, and PRD, respectively. For the spatial heterogeneity, geographically weighted regression (GWR was employed to carry out a spatial factor analysis, where terrain, climate condition, urban development, and vegetation coverage were taken as the potential driving factors. The results of the GWR imply varying relationships between the AOD and the factors. The results were generally consistent with existing studies, but the results suggest the following: (1 Elevation increase would more likely lead to a strong negative impact on aerosols (with most of the coefficients ranging from −1.5~0 in the BTH, −1.5~0 in the YRD, and −1~0 in the PRD in the places with greater elevations where the R-squared values are always larger than 0.5. However, the variation of elevations cannot explain the variation of aerosols in the places with relatively low elevations (with R-squared values approximately 0.1, ranging from 0 to 0.3, and approximately 0.1 in the BTH, YRD, and PRD, such as urban areas in the BTH and YRD. (2 The density of the built-up areas made a strong and positive impact on aerosols in the urban areas of the BTH (R-squared larger than 0.5, while the R-squared dropped to 0.1 in the places far away from the urban areas. (3 The vegetation coverage led to a stronger

  3. Prevalence, risk factors and spatial analysis of liver fluke infections in Danish cattle herds

    DEFF Research Database (Denmark)

    Olsen, Abbey; Frankena, Klaas; Bødker, Rene

    2015-01-01

    fitted the observed spatial pattern. Results: During the investigated period (2011-2013), an increase in annual herd prevalence was noted (2011-25.6%; 2012-28.4%; 2013-29.3%). The spatial analysis suggested significant clustering of positive and negative herds. Presence of streams, wetlands and pastures...... on farms showed a significant association with the presence of infection in cattle herds. Buying animals from positive herds was a risk factor on conventional farms. Additionally, risk of being infected with Fasciola hepatica was higher in non-dairy herds of medium size (>= 30 and ... to dairy and large (>= 100) cattle herds. The observed spatial pattern could be reproduced by predictions of the risk factor model. Conclusions: This study showed an increase in annual herd level prevalence (2011 to 2013) indicating that an increasing proportion of herds are infected with Fasciola hepatica...

  4. Spatial factors as contextual qualifiers of information seeking

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    R. Savolainen

    2006-01-01

    Full Text Available Introduction. This paper investigates the ways in which spatial factors have been approached in information seeking studies. The main attention was focused on studies discussing information seeking on the level of source selection and use. Method. Conceptual analysis of about 100 articles and books thematizing spatial issues of information seeking. Due to research economy, the main attention was paid to studies on everyday life information seeking. Results. Three major viewpoints were identified with regard to the degree of objectivity of spatial factors. The objectifying approach conceives of spatial factors as external and entity-like qualifiers that primarly constrain information seeking. The realistic-pragmatic approach emphasizes the ways in which the availabilty of information sources in different places such as daily work environments orient information seeking. The perspectivist approach focuses on how people subjectively assess the significance of various sources by means of spatial constructs such as information horizons. Conclusion. Spatial factors are centrally important contextual qualifiers of information seeking. There is a need to further explore the potential of the above viewpoints by relating the spatial and temporal factors of information seeking.

  5. Spatial econometric analysis of China’s province-level industrial carbon productivity and its influencing factors

    International Nuclear Information System (INIS)

    Long, Ruyin; Shao, Tianxiang; Chen, Hong

    2016-01-01

    Highlights: • We evaluate the industrial carbon productivity of China’s provinces. • The regional disparity and clustering features exist simultaneously. • There is evident spatial dependence in regional industrial carbon productivity. • We employ spatial panel data models to examine the impact factors. • Spatial effects are found to be important in understanding industrial CO_2 emissions. - Abstract: This study measured the industrial carbon productivity of 30 provinces in China from 2005 to 2012 and examined the space–time characteristics and the main factors of China’s industrial carbon productivity using Moran’s I index and spatial panel data models. The empirical results indicate that there is significant positive spatial dependence and clustering characteristics in China’s province-level industrial carbon productivity. The spatial dependence may create biased estimated parameters in an ordinary least squares framework; according to the analysis of our spatial panel models, industrial energy efficiency, the opening degree, technological progress, and the industrial scale structure have significantly positive effects on industrial carbon productivity whereas per-capita GDP, the industrial energy consumption structure, and the industrial ownership structure exert a negative effect on industrial carbon productivity.

  6. SPATIAL VARIETY AND DISTRIBUTION OF TRADITIONAL MARKETS IN SURAKARTA AS POTENTIAL FACTORS IN IMPROVING SPATIAL-BASED MANAGEMENT

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    Istijabatul Aliyah

    2017-02-01

    Full Text Available Traditional markets function as trading place, socio-culture interaction, and recreation facility either in regional or urban scope. Distribution and variety of spatial condition influence traditional markets’ planning both physically and non-physically. Therefore, this research aimed to conduct a mapping of traditional markets’ spatial distribution and variety as potential factors to improve spatial-based management. Analysis methods including: (1 Mapping by employing Geographic Information System, (2 Category Based Analysis (CBA, and (3 Interactive Analysis were applied in Surakarta City as the research location. The result of this research signifies that spatial variety and distribution of traditional markets in Surakarta had similar pattern between one market to others; overlapping service function; specific commodity types in accordance with the market’s characteristics; diverse operating hours. Spatial variety and distribution could be potential factors to improve traditional market management as shopping service. This result was contrasted with Central Place Theory by Christaller and NÆss & Jensen’s research finding stating that distance became a key factor influencing accessibility to a number of activity facilities. Therefore, distance toward the service center is not considered as the main factor in traditional market management. The main factor in managing and controlling traditional markets’ development includes service function, commodity specification, and operating hour’s flexibility.

  7. Prevalence, risk factors and spatial analysis of liver fluke infections in Danish cattle herds

    DEFF Research Database (Denmark)

    Olsen, Abbey; Frankena, Klaas; Bødker, Rene

    2015-01-01

    Background: Fasciola hepatica, a trematode parasite (liver fluke), infects a wide range of host species causing fasciolosis. The disease is prevalent world-wide and causes considerable economic losses to the livestock industry. Fasciolosis is regarded as an emerging food-borne zoonosis. To promote...... awareness among farmers and to implement strategies to control the infection, this study examined the prevalence, spatial distribution and risk factors for Fasciola hepatica infection in Danish cattle herds. Methods: A retrospective population based study was performed using meat inspection data...... of approximately 1.5 million cattle slaughtered in the period 2011 to 2013. Annual cumulative prevalence of recorded liver fluke findings was calculated for each year. Global and local spatial cluster analysis was used to identify and map spatial patterns of Fasciola hepatica positive and negative herds to explore...

  8. Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis

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    Levy Jonathan I

    2007-05-01

    Full Text Available Abstract Background There has been growing interest among exposure assessors, epidemiologists, and policymakers in the concept of "hot spots", or more broadly, the "spatial extent" of impacts from traffic-related air pollutants. This review attempts to quantitatively synthesize findings about the spatial extent under various circumstances. Methods We include both the peer-reviewed literature and government reports, and focus on four significant air pollutants: carbon monoxide, benzene, nitrogen oxides, and particulate matter (including both ultrafine particle counts and fine particle mass. From the identified studies, we extracted information about significant factors that would be hypothesized to influence the spatial extent within the study, such as the study type (e.g., monitoring, air dispersion modeling, GIS-based epidemiological studies, focus on concentrations or health risks, pollutant under study, background concentration, emission rate, and meteorological factors, as well as the study's implicit or explicit definition of spatial extent. We supplement this meta-analysis with results from some illustrative atmospheric dispersion modeling. Results We found that pollutant characteristics and background concentrations best explained variability in previously published spatial extent estimates, with a modifying influence of local meteorology, once some extreme values based on health risk estimates were removed from the analysis. As hypothesized, inert pollutants with high background concentrations had the largest spatial extent (often demonstrating no significant gradient, and pollutants formed in near-source chemical reactions (e.g., nitrogen dioxide had a larger spatial extent than pollutants depleted in near-source chemical reactions or removed through coagulation processes (e.g., nitrogen oxide and ultrafine particles. Our illustrative dispersion model illustrated the complex interplay of spatial extent definitions, emission rates

  9. Diving into the consumer nutrition environment: A Bayesian spatial factor analysis of neighborhood restaurant environment.

    Science.gov (United States)

    Luan, Hui; Law, Jane; Lysy, Martin

    2018-02-01

    Neighborhood restaurant environment (NRE) plays a vital role in shaping residents' eating behaviors. While NRE 'healthfulness' is a multi-facet concept, most studies evaluate it based only on restaurant type, thus largely ignoring variations of in-restaurant features. In the few studies that do account for such features, healthfulness scores are simply averaged over accessible restaurants, thereby concealing any uncertainty that attributed to neighborhoods' size or spatial correlation. To address these limitations, this paper presents a Bayesian Spatial Factor Analysis for assessing NRE healthfulness in the city of Kitchener, Canada. Several in-restaurant characteristics are included. By treating NRE healthfulness as a spatially correlated latent variable, the adopted modeling approach can: (i) identify specific indicators most relevant to NRE healthfulness, (ii) provide healthfulness estimates for neighborhoods without accessible restaurants, and (iii) readily quantify uncertainties in the healthfulness index. Implications of the analysis for intervention program development and community food planning are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. [Habitat factor analysis for Torreya grandis cv. Merrillii based on spatial information technology].

    Science.gov (United States)

    Wang, Xiao-ming; Wang, Ke; Ao, Wei-jiu; Deng, Jin-song; Han, Ning; Zhu, Xiao-yun

    2008-11-01

    Torreya grandis cv. Merrillii, a tertiary survival plant, is a rare tree species of significant economic value and expands rapidly in China. Its special habitat factor analysis has the potential value to provide guide information for its planting, management, and sustainable development, because the suitable growth conditions for this tree species are special and strict. In this paper, the special habitat factors for T. grandis cv. Merrillii in its core region, i.e., in seven villages of Zhuji City, Zhejiang Province were analyzed with Principal Component Analysis (PCA) and a series of data, such as IKONOS image, Digital Elevation Model (DEM), and field survey data supported by the spatial information technology. The results showed that T. grandis cv. Merrillii exhibited high selectivity of environmental factors such as elevation, slope, and aspect. 96.22% of T. grandis cv. Merrillii trees were located at the elevation from 300 to 600 m, 97.52% of them were found to present on the areas whose slope was less than 300, and 74.43% of them distributed on sunny and half-sunny slopes. The results of PCA analysis indicated that the main environmental factors affecting the habitat of T. grandis cv. Merrillii were moisture, heat, and soil nutrients, and moisture might be one of the most important ecological factors for T. grandis cv. Merrillii due to the unique biological and ecological characteristics of the tree species.

  11. Climatic factors associated with amyotrophic lateral sclerosis: a spatial analysis from Taiwan.

    Science.gov (United States)

    Tsai, Ching-Piao; Tzu-Chi Lee, Charles

    2013-11-01

    Few studies have assessed the spatial association of amyotrophic lateral sclerosis (ALS) incidence in the world. The aim of this study was to identify the association of climatic factors and ALS incidence in Taiwan. A total of 1,434 subjects with the primary diagnosis of ALS between years 1997 and 2008 were identified in the national health insurance research database. The diagnosis was also verified by the national health insurance programme, which had issued and providing them with "serious disabling disease (SDD) certificates". Local indicators of spatial association were employed to investigate spatial clustering of age-standardised incidence ratios in the townships of the study area. Spatial regression was utilised to reveal any association of annual average climatic factors and ALS incidence for the 12-year study period. The climatic factors included the annual average time of sunlight exposure, average temperature, maximum temperature, minimum temperature, atmospheric pressure, rainfall, relative humidity and wind speed with spatial autocorrelation controlled. Significant correlations were only found for exposure to sunlight and rainfall and it was similar in both genders. The annual average of the former was found to be negatively correlated with ALS, while the latter was positively correlated with ALS incidence. While accepting that ALS is most probably multifactorial, it was concluded that sunlight deprivation and/or rainfall are associated to some degree with ALS incidence in Taiwan.

  12. Correlation analysis of lung cancer and urban spatial factor: based on survey in Shanghai.

    Science.gov (United States)

    Wang, Lan; Zhao, Xiaojing; Xu, Wangyue; Tang, Jian; Jiang, Xiji

    2016-09-01

    The density of particulate matter (PM) in mega-cities in China such as Beijing and Shanghai has exceeded basic standards for health in recent years. Human exposure to PMs has been identified as traceable and controllable factor among all complicated risk factors for lung cancer. While the improvement of air quality needs tremendous efforts and time, certain revision of PM's density might happen associated with the adjustment of built environment. It is also proved that urban built environment is directly relevant to respiratory disease. Studies have respectively explored the indoor and outdoor factors on respiratory diseases. More comprehensive spatial factors need to be analyzed to understand the cumulative effect of built environment upon respiratory system. This interdisciplinary study examines the impact of both indoor (including age of housing, interval after decoration, indoor humidity etc.) and outdoor spatial factors (including density, parking, green spaces etc.) on lung cancer. A survey of lung cancer patients and a control group has been conducted in 2014 and 2015. A total of 472 interviewees are randomly selected within a pool of local residents who have resided in Shanghai for more than 5 years. Data are collected including their socio-demographic factors, lifestyle factors, and external and internal residential area factors. Regression models are established based on collected data to analyze the associations between lung cancer and urban spatial factors. Regression models illustrate that lung cancer presents significantly associated with a number of spatial factors. Significant outdoor spatial factors include external traffic volume (P=0.003), main plant type (P=0.035 for trees) of internal green space, internal water body (P=0.027) and land use of surrounding blocks (P=0.005 for residential areas of 7-9 floors, P=0.000 for residential areas of 4-6 floors, P=0.006 for business/commercial areas over 10 floors, P=0.005 for business/commercial areas of

  13. Macroecological factors shape local-scale spatial patterns in agriculturalist settlements.

    Science.gov (United States)

    Tao, Tingting; Abades, Sebastián; Teng, Shuqing; Huang, Zheng Y X; Reino, Luís; Chen, Bin J W; Zhang, Yong; Xu, Chi; Svenning, Jens-Christian

    2017-11-15

    Macro-scale patterns of human systems ranging from population distribution to linguistic diversity have attracted recent attention, giving rise to the suggestion that macroecological rules shape the assembly of human societies. However, in which aspects the geography of our own species is shaped by macroecological factors remains poorly understood. Here, we provide a first demonstration that macroecological factors shape strong local-scale spatial patterns in human settlement systems, through an analysis of spatial patterns in agriculturalist settlements in eastern mainland China based on high-resolution Google Earth images. We used spatial point pattern analysis to show that settlement spatial patterns are characterized by over-dispersion at fine spatial scales (0.05-1.4 km), consistent with territory segregation, and clumping at coarser spatial scales beyond the over-dispersion signals, indicating territorial clustering. Statistical modelling shows that, at macroscales, potential evapotranspiration and topographic heterogeneity have negative effects on territory size, but positive effects on territorial clustering. These relationships are in line with predictions from territory theory for hunter-gatherers as well as for many animal species. Our results help to disentangle the complex interactions between intrinsic spatial processes in agriculturalist societies and external forcing by macroecological factors. While one may speculate that humans can escape ecological constraints because of unique abilities for environmental modification and globalized resource transportation, our work highlights that universal macroecological principles still shape the geography of current human agricultural societies. © 2017 The Author(s).

  14. Impact of neighbourhood land-cover in epiphytic lichen diversity: Analysis of multiple factors working at different spatial scales

    Energy Technology Data Exchange (ETDEWEB)

    Pinho, P.; Augusto, S.; Maguas, C. [Universidade de Lisboa, Faculdade de Ciencias, Centro de Ecologia e Biologia Vegetal (CEBV), 1749-016 Lisbon (Portugal); Pereira, M.J.; Soares, A. [Universidade Tecnica de Lisboa, Instituto Superior Tecnico, Centro de Recursos Naturais e Ambiente (CERENA) Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Branquinho, C. [Universidade de Lisboa, Faculdade de Ciencias, Centro de Ecologia e Biologia Vegetal (CEBV), 1749-016 Lisbon (Portugal); Universidade Atlantica, Antiga Fabrica da Polvora de Barcarena, 2745-615 Barcarena (Portugal)], E-mail: cmbranquinho@fc.ul.pt

    2008-01-15

    The objective of this work was to determine the impact of neighbourhood land-cover in epiphytic lichen diversity. We used geostatistics to analyse the spatial structure of lichen-indicators (number of lichen species and Lichen Diversity Value) and correlate them to land-cover considering different distances from the observed data. The results showed that lichen diversity was influenced by different environmental factors that act in the same territory but impact lichens at different distances from the source. The differences in the distance of influence of the several land-cover types seem to be related to the size of pollutants/particles that predominantly are dispersed by each land-cover type. We also showed that a local scale of analysis gives a deeper insight into the understanding of lichen richness and abundance in the region. This work highlighted the importance of a multiple spatial scale of analysis to deeply interpret the relation between lichen diversity and the underling environmental factors. - The interpretation of lichen-biodiversity data was improved by using analysis at different scales.

  15. Impact of neighbourhood land-cover in epiphytic lichen diversity: Analysis of multiple factors working at different spatial scales

    International Nuclear Information System (INIS)

    Pinho, P.; Augusto, S.; Maguas, C.; Pereira, M.J.; Soares, A.; Branquinho, C.

    2008-01-01

    The objective of this work was to determine the impact of neighbourhood land-cover in epiphytic lichen diversity. We used geostatistics to analyse the spatial structure of lichen-indicators (number of lichen species and Lichen Diversity Value) and correlate them to land-cover considering different distances from the observed data. The results showed that lichen diversity was influenced by different environmental factors that act in the same territory but impact lichens at different distances from the source. The differences in the distance of influence of the several land-cover types seem to be related to the size of pollutants/particles that predominantly are dispersed by each land-cover type. We also showed that a local scale of analysis gives a deeper insight into the understanding of lichen richness and abundance in the region. This work highlighted the importance of a multiple spatial scale of analysis to deeply interpret the relation between lichen diversity and the underling environmental factors. - The interpretation of lichen-biodiversity data was improved by using analysis at different scales

  16. Spatial distribution and risk factors of influenza in Jiangsu province, China, based on geographical information system

    Directory of Open Access Journals (Sweden)

    Jia-Cheng Zhang

    2014-05-01

    Full Text Available Influenza poses a constant, heavy burden on society. Recent research has focused on ecological factors associated with influenza incidence and has also studied influenza with respect to its geographic spread at different scales. This research explores the temporal and spatial parameters of influenza and identifies factors influencing its transmission. A spatial autocorrelation analysis, a spatial-temporal cluster analysis and a spatial regression analysis of influenza rates, carried out in Jiangsu province from 2004 to 2011, found that influenza rates to be spatially dependent in 2004, 2005, 2006 and 2008. South-western districts consistently revealed hotspots of high-incidence influenza. The regression analysis indicates that railways, rivers and lakes are important predictive environmental variables for influenza risk. A better understanding of the epidemic pattern and ecological factors associated with pandemic influenza should benefit public health officials with respect to prevention and controlling measures during future epidemics.

  17. Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes.

    Science.gov (United States)

    Baker, Jannah; White, Nicole; Mengersen, Kerrie

    2014-11-20

    Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences. We present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions. Choice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application. Selecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.

  18. Regional Convergence of Income: Spatial Analysis

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    Vera Ivanovna Ivanova

    2014-12-01

    Full Text Available Russia has a huge territory and a strong interregional heterogeneity, so we can assume that geographical factors have a significant impact on the pace of economic growth in Russian regions. Therefore the article is focused on the following issues: 1 correlation between comparative advantages of geographical location and differences in growth rates; 2 impact of more developed regions on their neighbors and 3 correlation between economic growth of regions and their spatial interaction. The article is devoted to the empirical analysis of regional per capita incomes from 1996 to 2012 and explores the dynamics of the spatial autocorrelation of regional development indicator. It is shown that there is a problem of measuring the intensity of spatial dependence: factor value of Moran’s index varies greatly depending on the choice of the matrix of distances. In addition, with the help of spatial econometrics the author tests the following hypotheses: 1 there is convergence between regions for a specified period; 2 the process of beta convergence is explained by the spatial arrangement of regions and 3 there is positive impact of market size on regional growth. The author empirically confirmed all three hypotheses

  19. The Spatial Econometric Analysis of China’s Banking Competition and Its Influential Factors

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    Ying Li

    2015-12-01

    Full Text Available This paper determines alternative indexes to measure banking competition from the perspective of industrial economics. Spatial correlation of competition in China’s banking environment is studied from the perspective of spatial economics. A spatial panel data model is built to make an empirical study of factors influencing banking competition. The results show that the global autocorrelation test index Moran’s I indicates that China’s banking competition has obvious spatial correlation characteristics and significant spatial clustering. The space LISA map indicates that banking competition in most provinces has the characteristics of spatial dependence, and only a few provinces have the characteristics of spatial heterogeneity. Human capital, economic growth, financial scale of development, and foreign direct investment all have a significant positive effect on improving the degree of banking competition. Government intervention has a significant negative impact on the degree of banking competition, while fixed asset investment has no significant impact on it.

  20. Spatial scale effects in environmental risk-factor modelling for diseases

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    Ram K. Raghavan

    2013-05-01

    Full Text Available Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD and Kansas Gap Analysis Program (KS GAP around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed.

  1. Analysis of Spatial Pattern and Influencing Factors of E-Commerce

    Science.gov (United States)

    Zhang, Y.; Chen, J.; Zhang, S.

    2017-09-01

    This paper aims to study the relationship between e-commerce development and geographical characteristics using data of e-commerce, economy, Internet, express delivery and population from 2011 to 2015. Moran's I model and GWR model are applied to analyze the spatial pattern of E-commerce and its influencing factors. There is a growth trend of e-commerce from west to east, and it is obvious to see that e-commerce development has a space-time clustering, especially around the Yangtze River delta. The comprehensive factors caculated through PCA are described as fundamental social productivity, resident living standard and population sex structure. The first two factors have positive correlation with e-commerce, and the intensity of effect increases yearly. However, the influence of population sex structure on the E-commerce development is not significant. Our results suggest that the clustering of e-commerce has a downward trend and the impact of driving factors on e-commerce is observably distinct from year to year in space.

  2. ANALYSIS OF SPATIAL PATTERN AND INFLUENCING FACTORS OF E-COMMERCE

    OpenAIRE

    Y. Zhang; J. Chen; S. Zhang

    2017-01-01

    This paper aims to study the relationship between e-commerce development and geographical characteristics using data of e-commerce, economy, Internet, express delivery and population from 2011 to 2015. Moran’s I model and GWR model are applied to analyze the spatial pattern of E-commerce and its influencing factors. There is a growth trend of e-commerce from west to east, and it is obvious to see that e-commerce development has a space-time clustering, especially around the Yangtze R...

  3. ANALYSIS OF SPATIAL PATTERN AND INFLUENCING FACTORS OF E-COMMERCE

    Directory of Open Access Journals (Sweden)

    Y. Zhang

    2017-09-01

    Full Text Available This paper aims to study the relationship between e-commerce development and geographical characteristics using data of e-commerce, economy, Internet, express delivery and population from 2011 to 2015. Moran’s I model and GWR model are applied to analyze the spatial pattern of E-commerce and its influencing factors. There is a growth trend of e-commerce from west to east, and it is obvious to see that e-commerce development has a space-time clustering, especially around the Yangtze River delta. The comprehensive factors caculated through PCA are described as fundamental social productivity, resident living standard and population sex structure. The first two factors have positive correlation with e-commerce, and the intensity of effect increases yearly. However, the influence of population sex structure on the E-commerce development is not significant. Our results suggest that the clustering of e-commerce has a downward trend and the impact of driving factors on e-commerce is observably distinct from year to year in space.

  4. A kernel version of spatial factor analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2009-01-01

    . Schölkopf et al. introduce kernel PCA. Shawe-Taylor and Cristianini is an excellent reference for kernel methods in general. Bishop and Press et al. describe kernel methods among many other subjects. Nielsen and Canty use kernel PCA to detect change in univariate airborne digital camera images. The kernel...... version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply kernel versions of PCA, maximum autocorrelation factor (MAF) analysis...

  5. Temporal-spatial variation and the influence factors of precipitation in Sichuan Province, China

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Precipitation is a key factor in the water cycle.At the same time,precipitation is the focus of study in meteorology and climatology,ecological environmental assessment,non-point source pollution and so on.Understanding the temporal-spatial variation and the corresponding factors of precipitation has become the object of hydrology and environmentology.Based on the annual precipitation data,we analyzed the spatial distribution of precipitation in Sichuan Province in China as well as the temporal-spatial variation and the corresponding influence factors involved.The results show that the amount of precipitation was abundant,but the spatial distribution was not consistent with it and the amount of precipitation gradually declined from the south-east to the north-west in Sichuan Province,China.Moreover,the spatial distribution was different throughout the years.The result of correlation analysis indicated that elevation,temperature and air pressure were three key factors affecting the amount and distribution of precipitation,and the correlation coefficients were -0.56,0.38 and 0.45 respectively.Notably,the relationship between the slope of topography and precipitation were significantly negative and the average correlation coefficient was -0.28.

  6. Spatial epidemiology of cancer: a review of data sources, methods and risk factors

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    Rita Roquette

    2017-05-01

    Full Text Available Cancer is a major concern among chronic diseases today. Spatial epidemiology plays a relevant role in this matter and we present here a review of this subject, including a discussion of the literature in terms of the level of geographic data aggregation, risk factors and methods used to analyse the spatial distribution of patterns and spatial clusters. For this purpose, we performed a websearch in the Pubmed and Web of Science databases including studies published between 1979 and 2015. We found 180 papers from 63 journals and noted that spatial epidemiology of cancer has been addressed with more emphasis during the last decade with research based on data mostly extracted from cancer registries and official mortality statistics. In general, the research questions present in the reviewed papers can be classified into three different sets: i analysis of spatial distribution of cancer and/or its temporal evolution; ii risk factors; iii development of data analysis methods and/or evaluation of results obtained from application of existing methods. This review is expected to help promote research in this area through the identification of relevant knowledge gaps. Cancer’s spatial epidemiology represents an important concern, mainly for public health policies design aimed to minimise the impact of chronic disease in specific populations.

  7. Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

    International Nuclear Information System (INIS)

    Keser, Saniye; Duzgun, Sebnem; Aksoy, Aysegul

    2012-01-01

    Highlights: ► Spatial autocorrelation exists in municipal solid waste generation rates for different provinces in Turkey. ► Traditional non-spatial regression models may not provide sufficient information for better solid waste management. ► Unemployment rate is a global variable that significantly impacts the waste generation rates in Turkey. ► Significances of global parameters may diminish at local scale for some provinces. ► GWR model can be used to create clusters of cities for solid waste management. - Abstract: In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation data is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global

  8. Perspectives on spatial data analysis

    CERN Document Server

    Rey, Sergio

    2010-01-01

    This book takes both a retrospective and prospective view of the field of spatial analysis by combining selected reprints of classic articles by Arthur Getis with current observations by leading experts in the field. Four main aspects are highlighted, dealing with spatial analysis, pattern analysis, local statistics as well as illustrative empirical applications. Researchers and students will gain an appreciation of Getis' methodological contributions to spatial analysis and the broad impact of the methods he has helped pioneer on an impressively broad array of disciplines including spatial epidemiology, demography, economics, and ecology. The volume is a compilation of high impact original contributions, as evidenced by citations, and the latest thinking on the field by leading scholars. This makes the book ideal for advanced seminars and courses in spatial analysis as well as a key resource for researchers seeking a comprehensive overview of recent advances and future directions in the field.

  9. Kernel parameter dependence in spatial factor analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2010-01-01

    kernel PCA. Shawe-Taylor and Cristianini [4] is an excellent reference for kernel methods in general. Bishop [5] and Press et al. [6] describe kernel methods among many other subjects. The kernel version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional...... feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply a kernel version of maximum autocorrelation factor (MAF) [7, 8] analysis to irregularly sampled stream sediment geochemistry data from South Greenland and illustrate the dependence...... of the kernel width. The 2,097 samples each covering on average 5 km2 are analyzed chemically for the content of 41 elements....

  10. Violence in public transportation: an approach based on spatial analysis.

    Science.gov (United States)

    Sousa, Daiane Castro Bittencourt de; Pitombo, Cira Souza; Rocha, Samille Santos; Salgueiro, Ana Rita; Delgado, Juan Pedro Moreno

    2017-12-11

    To carry out a spatial analysis of the occurrence of acts of violence (specifically robberies) in public transportation, identifying the regions of greater incidence, using geostatistics, and possible causes with the aid of a multicriteria analysis in the Geographic Information System. The unit of analysis is the traffic analysis zone of the survey named Origem-Destino, carried out in Salvador, state of Bahia, in 2013. The robberies recorded by the Department of Public Security of Bahia in 2013 were located and made compatible with the limits of the traffic analysis zones and, later, associated with the respective centroids. After determining the regions with the highest probability of robbery, we carried out a geographic analysis of the possible causes in the region with the highest robbery potential, considering the factors analyzed using a multicriteria analysis in a Geographic Information System environment. The execution of the two steps of this study allowed us to identify areas corresponding to the greater probability of occurrence of robberies in public transportation. In addition, the three most vulnerable road sections (Estrada da Liberdade, Rua Pero Vaz, and Avenida General San Martin) were identified in these areas. In these sections, the factors that most contribute with the potential for robbery in buses are: F1 - proximity to places that facilitate escape, F3 - great movement of persons, and F2 - absence of policing, respectively. Indicator Kriging (geostatistical estimation) can be used to construct a spatial probability surface, which can be a useful tool for the implementation of public policies. The multicriteria analysis in the Geographic Information System environment allowed us to understand the spatial factors related to the phenomenon under analysis.

  11. Professional analysis in spatial planning

    Directory of Open Access Journals (Sweden)

    Andrej Černe

    2005-12-01

    Full Text Available Spatial analysis contributes to accomplishment of the three basic aims of spatial planning: it is basic element for setting spatial policies, concepts and strategies, gives basic information to inhabitants, land owners, investors, planners and helps in performing spatial policies, strategies, plans, programmes and projects. Analysis in planning are generally devoted to: understand current circumstances and emerging conditions within planning decisions; determine priorities of open questions and their solutions; formulate general principles for further development.

  12. Spatial Data Mining for Estimating Cover Management Factor of Universal Soil Loss Equation

    Science.gov (United States)

    Tsai, F.; Lin, T. C.; Chiang, S. H.; Chen, W. W.

    2016-12-01

    Universal Soil Loss Equation (USLE) is a widely used mathematical model that describes long-term soil erosion processes. Among the six different soil erosion risk factors of USLE, the cover-management factor (C-factor) is related to land-cover/land-use. The value of C-factor ranges from 0.001 to 1, so it alone might cause a thousandfold difference in a soil erosion analysis using USLE. The traditional methods for the estimation of USLE C-factor include in situ experiments, soil physical parameter models, USLE look-up tables with land use maps, and regression models between vegetation indices and C-factors. However, these methods are either difficult or too expensive to implement in large areas. In addition, the values of C-factor obtained using these methods can not be updated frequently, either. To address this issue, this research developed a spatial data mining approach to estimate the values of C-factor with assorted spatial datasets for a multi-temporal (2004 to 2008) annual soil loss analysis of a reservoir watershed in northern Taiwan. The idea is to establish the relationship between the USLE C-factor and spatial data consisting of vegetation indices and texture features extracted from satellite images, soil and geology attributes, digital elevation model, road and river distribution etc. A decision tree classifier was used to rank influential conditional attributes in the preliminary data mining. Then, factor simplification and separation were considered to optimize the model and the random forest classifier was used to analyze 9 simplified factor groups. Experimental results indicate that the overall accuracy of the data mining model is about 79% with a kappa value of 0.76. The estimated soil erosion amounts in 2004-2008 according to the data mining results are about 50.39 - 74.57 ton/ha-year after applying the sediment delivery ratio and correction coefficient. Comparing with estimations calculated with C-factors from look-up tables, the soil erosion

  13. Environmental and spatial factors influencing the distribution of cladocerans in lakes across the central Canadian Arctic treeline region

    Directory of Open Access Journals (Sweden)

    John P. SMOL

    2010-02-01

    Full Text Available We examine the role of local environmental and spatial factors in explaining variation in the composition of cladoceran assemblages from surface sediments within a set of 50 lakes spanning a broad southwest to northeast transect across the central Canadian Arctic treeline region from Yellowknife (Northwest Territories to the northern boundary of the Thelon Game Sanctuary (Nunavut Territory. Within each lake, the cladoceran fauna was identified based on the subfossil exoskeletal remains preserved in recently deposited lake sediments. Physical and chemical limnological data were measured in August of 1996 and 1998. Spatial data were generated based on latitude and longitude using Principal Coordinates of Neighbors Matrices analysis (PCNM. The relationships between cladocerans and the measured environmental and spatial variables were examined using both unconstrained (Principal Components Analysis, PCA and constrained (Redundancy Analysis, RDA ordination techniques. Variance partitioning, based on partial RDAs, was used to identify the relative importance of significant environmental and spatial explanatory variables. Three environmental variables were identified as significantly influencing cladoceran community structure: surface water temperature, dissolved organic carbon (DOC, and total phosphorus (TP. Five PCNM-generated spatial variables were also significant in explaining cladoceran distributions. Variance partitioning attributed 14% of the variance in the distribution of Cladocera to spatial factors, an additional 10% to spatially-structured environmental variables, and 8% to environmental factors that were not spatially-structured. Within the central Canadian Arctic treeline region, spatial and other environmental processes had an important influence on the distribution of cladoceran communities. The strong influence of spatial factors was related to the large ecoclimatic gradient across treeline. The distribution patterns of cladocerans

  14. Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor

    OpenAIRE

    Liu Xin; Zhang Shaoliang; Zheng Pulin

    2015-01-01

    Spatial local outlier factor (SLOF) algorithm was adopted in this study for spatial outlier detection because of the limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data obtained in the carbon capture and storage (CCS) project, the K-Nearest Neighbour (KNN) graph was constructed using the latitude and longitude information of the monitoring points to identify the spatial neighbourhood of the monitoring points. Then ...

  15. Spatial analysis and planning under imprecision

    CERN Document Server

    Leung, Y

    1988-01-01

    The book deals with complexity, imprecision, human valuation, and uncertainty in spatial analysis and planning, providing a systematic exposure of a new philosophical and theoretical foundation for spatial analysis and planning under imprecision. Regional concepts and regionalization, spatial preference-utility-choice structures, spatial optimization with single and multiple objectives, dynamic spatial systems and their controls are analyzed in sequence.The analytical framework is based on fuzzy set theory. Basic concepts of fuzzy set theory are first discussed. Many numerical examples and emp

  16. Spatial analysis statistics, visualization, and computational methods

    CERN Document Server

    Oyana, Tonny J

    2015-01-01

    An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis-containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS-as well as detailed illustrations and numerous case studies. The book enables readers to: Identify types and characterize non-spatial and spatial data Demonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and results Construct testable hypotheses that require inferential statistical analysis Process spatial data, extract explanatory variables, conduct statisti...

  17. Characterization factors for terrestrial acidification at the global scale: a systematic analysis of spatial variability and uncertainty.

    Science.gov (United States)

    Roy, Pierre-Olivier; Azevedo, Ligia B; Margni, Manuele; van Zelm, Rosalie; Deschênes, Louise; Huijbregts, Mark A J

    2014-12-01

    Characterization factors (CFs) are used in life cycle assessment (LCA) to quantify the potential impact per unit of emission. CFs are obtained from a characterization model which assess the environmental mechanisms along the cause-effect chain linking an emission to its potential damage on a given area of protection, such as loss in ecosystem quality. Up to now, CFs for acidifying emissions did not cover the global scale and were only representative of their characterization model geographical scope. Consequently, current LCA practices implicitly assume that all emissions from a global supply chain occur within the continent referring to the characterization method geographical scope. This paper provides worldwide 2°×2.5° spatially-explicit CFs, representing the change in relative loss of terrestrial vascular plant species due to an emission change of nitrogen oxides (NOx), ammonia (NH3) and sulfur dioxide (SO2). We found that spatial variability in the CFs is much larger compared to statistical uncertainty (six orders of magnitude vs. two orders of magnitude). Spatial variability is mainly caused by the atmospheric fate factor and soil sensitivity factor, while the ecological effect factor is the dominant contributor to the statistical uncertainty. The CFs provided in our study allow the worldwide spatially explicit evaluation of life cycle impacts related to acidifying emissions. This opens the door to evaluate regional life cycle emissions of different products in a global economy. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Recent developments in spatial analysis spatial statistics, behavioural modelling, and computational intelligence

    CERN Document Server

    Getis, Arthur

    1997-01-01

    In recent years, spatial analysis has become an increasingly active field, as evidenced by the establishment of educational and research programs at many universities. Its popularity is due mainly to new technologies and the development of spatial data infrastructures. This book illustrates some recent developments in spatial analysis, behavioural modelling, and computational intelligence. World renown spatial analysts explain and demonstrate their new and insightful models and methods. The applications are in areas of societal interest such as the spread of infectious diseases, migration behaviour, and retail and agricultural location strategies. In addition, there is emphasis on the uses of new technologoies for the analysis of spatial data through the application of neural network concepts.

  19. Analysis of factors which limited the spatial variation of barley yield on the forest-steppe chernozems of Kursk region

    Science.gov (United States)

    Belik, Anton; Vasenev, Ivan; Jablonskikh, Lidia; Bozhko, Svetlana

    2017-04-01

    The crop yield is the most important indicator of the efficiency of agricultural production. It is the function that depends on a large number of groups of independent variables, such as the weather, soil fertility and overall culture agriculture. A huge number of combinations of these factors contribute to the formation of high spatial variety of crop yields within small areas, includes the slope agrolandscapes in Kursk region. Spatial variety of yield leads to a significant reduction in the efficiency of agriculture. In this connection, evaluation and analysis of the factors, which limits the yield of field crops is a very urgent proble in agroecology. The research was conducted in the period of 2003-2004 on a representative field. The typical and leached chernozems with the varying thickness and of erosion degree are dominated in soil cover. At the time of field research studied areas were busy by barley. The reseached soils have an average and increased fertility level. Chernozem typical full-face, and the leached contain an average of 4.5-6% humus, close to neutral pH, favorable values of physico-chemical parameters, medium and high content of nutrients. The eroded chernozems differs agrogenic marked declining in fertility parameters. The diversity of meso- and micro-relief in the fields and soil cover influence to significant spatial variety of fertility. For example the content of nutrients in the soil variation can be up to 5-fold level. High spatial heterogeneity of soils fertility ifluence to barley yield variety. During research on the productivity of the field varied in the range of 20-43 c/ha, and 7-44 c/ha (2004). Analysis of the factors, which limited the yield of barley, showed that the first priorities occupy unregulated characterises: slope angle and the classification of soils (subtype and race of chernozem and the difference in the degree of erosion), which determines the development of erosion processes and redistribution available to plants

  20. A spatial cluster analysis of tractor overturns in Kentucky from 1960 to 2002.

    Directory of Open Access Journals (Sweden)

    Daniel M Saman

    Full Text Available Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns.A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns.The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55 and 10 counties in eastern Kentucky (RR = 1.97. Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002 and a greater percent of total tractors with less than 40 horsepower by county (p<0.0001.This study reveals that geographic hotspots of tractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions with the intention of reducing tractor overturns in the highest risk counties in Kentucky.

  1. Multiscale Spatial Assessment of Determinant Factors of Land Use Change: Study at Urban Area of Yogyakarta

    Science.gov (United States)

    Susilo, Bowo

    2017-12-01

    Studies of land use change have been undertaken by different researchers using various methods. Among those methods, modelling is widely utilized. Modelling land use change required several components remarked as model variables. Those represent any conditions or factors which considered relevant or have some degree of correlation to the changes of land use. Variables which have significant correlation to land use change are referred as determinant factors or driving forces. Those factors as well as changes of land use are distributed across space and therefore referred as spatial determinant factors. The main objective of the research was to examine land use change and its determinant factors. Area and location of land use change were analysed based on three different years of land use maps, which are 1993, 2000 and 2007. Spatial and temporal analysis were performed which emphasize to the influence of scale to both of analysis’s. Urban area of Yogyakarta was selected as study area. Study area covered three different districts (kabupaten), involving 20 sub districts and totally consists of 74 villages. Result of this study shows that during 14 years periods (1993 to 2007), there were about 1,460 hectares of land use change had been taken place. Dominant type of land use change is agricultural to residential. The uses of different spatial and temporal scale in analysis were able to reveal different factors related to land use change. In general, factors influencing the quantities of land use change in the study area were population growth and the availability of land. The use of data with different spatial resolution can reveal the presence of various factors associated with the location of the change. Locations of land use change were influenced or determined by accessibility factors.

  2. A new methodology of spatial cross-correlation analysis.

    Science.gov (United States)

    Chen, Yanguang

    2015-01-01

    Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran's index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson's correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China's urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes.

  3. Analysis of spatial dynamic of epizootic process of bluetongue and its risk factors.

    Science.gov (United States)

    Bouchemla, Fayssal; Popova, Olga Mikhailovna; Agoltsov, Valerey Alexandrovich

    2017-10-01

    The study was undertaken to find out the spatial dynamic occurrence and patterns of the global spread of bluetongue (BT) disease for the period from 1996 to 2016, as well as the assessment of the risk of occurrence and its spread in 2017-2018. Outbreaks (serum samples were collected from clinically healthy as well as suspected animals in infected points) were confirmed and reported officially by veterinary departments which represent different geographical regions in the world to World Organization for Animal Health. These reports explained that ELISA and polymerase chain reaction were used to identify the BT disease, taking in the account number of infected, dead animals, and focus of BT infection in all susceptible animals from 1996 to 2016. Once conventional statistical population was defined (an observational study), we had classified data as well as possible to answer to our aim, using descriptive statistics methods, including the test of the relationship between different epizootiological indicators. The spatial dynamic study of BT's occurrence and its spread in the world over the two past decades was presented by different epizootic indicators. The given analysis includes assessment and measurement of risk factors. It was built too, regression models, and allowed to put different forecasts on the different epizootic indicators in the years 2017-2018 by the extrapolation method. We had also determined that, in 2017, BT continues to spread with the total expectancy of 3.4 focus of infection (number of diseased animals in a single unfavorable point) and mortality of about 26 %; these rates tend to decrease in 2018. At abused points by BT, up to 78.4% of animals are mixed (more than one type) and in 21.6% - uniform. By this way, the relative risk of the incidence of appearance-abused points in mixed households has 3.64, which might be considered higher for the BT dissemination. Moreover, between the enzootic index and other epizootiological indicators had

  4. Analysis of spatial dynamic of epizootic process of bluetongue and its risk factors

    Directory of Open Access Journals (Sweden)

    Fayssal Bouchemla

    2017-10-01

    Full Text Available Aim: The study was undertaken to find out the spatial dynamic occurrence and patterns of the global spread of bluetongue (BT disease for the period from 1996 to 2016, as well as the assessment of the risk of occurrence and its spread in 2017-2018. Materials and Methods: Outbreaks (serum samples were collected from clinically healthy as well as suspected animals in infected points were confirmed and reported officially by veterinary departments which represent different geographical regions in the world to World Organization for Animal Health. These reports explained that ELISA and polymerase chain reaction were used to identify the BT disease, taking in the account number of infected, dead animals, and focus of BT infection in all susceptible animals from 1996 to 2016. Once conventional statistical population was defined (an observational study, we had classified data as well as possible to answer to our aim, using descriptive statistics methods, including the test of the relationship between different epizootiological indicators. Results: The spatial dynamic study of BT's occurrence and its spread in the world over the two past decades was presented by different epizootic indicators. The given analysis includes assessment and measurement of risk factors. It was built too, regression models, and allowed to put different forecasts on the different epizootic indicators in the years 2017-2018 by the extrapolation method. We had also determined that, in 2017, BT continues to spread with the total expectancy of 3.4 focus of infection (number of diseased animals in a single unfavorable point and mortality of about 26 %; these rates tend to decrease in 2018. At abused points by BT, up to 78.4% of animals are mixed (more than one type and in 21.6% - uniform. By this way, the relative risk of the incidence of appearance-abused points in mixed households has 3.64, which might be considered higher for the BT dissemination. Moreover, between the enzootic

  5. Spatial analysis of hemorrhagic fever with renal syndrome in China

    Directory of Open Access Journals (Sweden)

    Yang Hong

    2006-04-01

    Full Text Available Abstract Background Hemorrhagic fever with renal syndrome (HFRS is endemic in many provinces with high incidence in mainland China, although integrated intervention measures including rodent control, environment management and vaccination have been implemented for over ten years. In this study, we conducted a geographic information system (GIS-based spatial analysis on distribution of HFRS cases for the whole country with an objective to inform priority areas for public health planning and resource allocation. Methods Annualized average incidence at a county level was calculated using HFRS cases reported during 1994–1998 in mainland China. GIS-based spatial analyses were conducted to detect spatial autocorrelation and clusters of HFRS incidence at the county level throughout the country. Results Spatial distribution of HFRS cases in mainland China from 1994 to 1998 was mapped at county level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of HFRS cases was nonrandom and clustered with a Moran's I = 0.5044 (p = 0.001. Spatial cluster analyses suggested that 26 and 39 areas were at increased risks of HFRS (p Conclusion The application of GIS, together with spatial statistical techniques, provide a means to quantify explicit HFRS risks and to further identify environmental factors responsible for the increasing disease risks. We demonstrate a new perspective of integrating such spatial analysis tools into the epidemiologic study and risk assessment of HFRS.

  6. Bovine respiratory syncytial virus and bovine coronavirus antibodies in bulk tank milk - risk factors and spatial analysis.

    Science.gov (United States)

    Toftaker, Ingrid; Sanchez, Javier; Stokstad, Maria; Nødtvedt, Ane

    2016-10-01

    Bovine respiratory syncytial virus (BRSV) and bovine coronavirus (BCoV) are considered widespread among cattle in Norway and worldwide. This cross-sectional study was conducted based on antibody-ELISA of bulk tank milk (BTM) from 1347 herds in two neighboring counties in western Norway. The study aims were to determine the seroprevalence at herd level, to evaluate risk factors for BRSV and BCoV seropositivity, and to assess how these factors were associated with the spatial distribution of positive herds. The overall prevalence of BRSV and BCoV positive herds in the region was 46.2% and 72.2%, respectively. Isopleth maps of the prevalence risk distribution showed large differences in prevalence risk across the study area, with the highest prevalence in the northern region. Common risk factors of importance for both viruses were herd size, geographic location, and proximity to neighbors. Seropositivity for one virus was associated with increased odds of seropositivity for the other virus. Purchase of livestock was an additional risk factor for BCoV seropositivity, included in the model as in-degree, which was defined as the number of incoming movements from individual herds, through animal purchase, over a period of five years. Local dependence and the contribution of risk factors to this effect were assessed using the residuals from two logistic regression models for each virus. One model contained only the x- and y- coordinates as predictors, the other had all significant predictors included. Spatial clusters of high values of residuals were detected using the normal model of the spatial scan statistic and visualized on maps. Adjusting for the risk factors in the final models had different impact on the spatial clusters for the two viruses: For BRSV the number of clusters was reduced from six to four, for BCoV the number of clusters remained the same, however the log-likelihood ratios changed notably. This indicates that geographical differences in proximity to

  7. A New Methodology of Spatial Cross-Correlation Analysis

    Science.gov (United States)

    Chen, Yanguang

    2015-01-01

    Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran’s index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson’s correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China’s urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes. PMID:25993120

  8. Individual and Interactive Effects of Socio-Ecological Factors on Dengue Fever at Fine Spatial Scale: A Geographical Detector-Based Analysis.

    Science.gov (United States)

    Cao, Zheng; Liu, Tao; Li, Xing; Wang, Jin; Lin, Hualiang; Chen, Lingling; Wu, Zhifeng; Ma, Wenjun

    2017-07-17

    Background : Large spatial heterogeneity was observed in the dengue fever outbreak in Guangzhou in 2014, however, the underlying reasons remain unknown. We examined whether socio-ecological factors affected the spatial distribution and their interactive effects. Methods : Moran's I was applied to first examine the spatial cluster of dengue fever in Guangzhou. Nine socio-ecological factors were chosen to represent the urbanization level, economy, accessibility, environment, and the weather of the 167 townships/streets in Guangzhou, and then the geographical detector was applied to analyze the individual and interactive effects of these factors on the dengue outbreak. Results : Four clusters of dengue fever were identified in Guangzhou in 2014, including one hot spot in the central area of Guangzhou and three cold spots in the suburban districts. For individual effects, the temperature ( q = 0.33) was the dominant factor of dengue fever, followed by precipitation ( q = 0.24), road density ( q = 0.24), and water body area ( q = 0.23). For the interactive effects, the combination of high precipitation, high temperature, and high road density might result in increased dengue fever incidence. Moreover, urban villages might be the dengue fever hot spots. Conclusions : Our study suggests that some socio-ecological factors might either separately or jointly influence the spatial distribution of dengue fever in Guangzhou.

  9. Spatial Data Analysis: Recommendations for Educational Infrastructure in Sindh

    Directory of Open Access Journals (Sweden)

    Abdul Aziz Ansari

    2017-06-01

    Full Text Available Analysing the Education infrastructure has become a crucial activity in imparting quality teaching and resources to students. Facilitations required in improving current education status and future schools is an important analytical component. This is best achieved through a Geographical Information System (GIS analysis of the spatial distribution of schools. In this work, we will execute GIS Analytics on the rural and urban school distributions in Sindh, Pakistan. Using a reliable dataset collected from an international survey team, GIS analysis is done with respect to: 1 school locations, 2 school facilities (water, sanitation, class rooms etc. and 3 student’s results. We will carry out analysis at district level by presenting several spatial results. Correlational analysis of highly influential factors, which may impact the educational performance will generate recommendations for planning and development in weak areas which will provide useful insights regarding effective utilization of resources and new locations to build future schools. The time series analysis will predict the future results which may be witnessed through keen observations and data collections.

  10. PREVALENCE OF THE HEPATITIS C VIRUS AMONG UNIVERSITY EMPLOYEES IN SÃO PAULO, SOUTHEASTERN BRAZIL: predictive factors and geoprocessing spatial analysis

    Directory of Open Access Journals (Sweden)

    Cássio Vieira de OLIVEIRA

    2015-03-01

    Full Text Available Background There are limited studies on the prevalence and risk factors associated with hepatitis C virus (HCV infection. Objective Identify the prevalence and risk factors for HCV infection in university employees of the state of São Paulo, Brazil. Methods Digital serological tests for anti-HCV have been performed in 3153 volunteers. For the application of digital testing was necessary to withdraw a drop of blood through a needlestick. The positive cases were performed for genotyping and RNA. Chi-square and Fisher’s exact test were used, with P-value 40 years, blood transfusion, injectable drugs, inhalable drugs (InDU, injectable Gluconergam®, glass syringes, tattoos, hemodialysis and sexual promiscuity. Age (P=0.01, OR 5.6, CI 1.4 to 22.8, InDU (P<0.0001, OR=96.8, CI 24.1 to 388.2, Gluconergam® (P=0.0009, OR=44.4, CI 4.7 to 412.7 and hemodialysis (P=0.0004, OR=90.1, CI 7.5 – 407.1 were independent predictors. Spatial analysis of the prevalence with socioeconomic indices, Gross Domestic Product and Human Development Index by the geoprocessing technique showed no positive correlation. Conclusions The prevalence of HCV infection was 0.7%. The independent risk factors for HCV infection were age, InDU, Gluconergan® and hemodialysis. There was no spatial correlation of HCV prevalence with local economic factors.

  11. Spatial Inequalities in the Incidence of Colorectal Cancer and Associated Factors in the Neighborhoods of Tehran, Iran: Bayesian Spatial Models

    Directory of Open Access Journals (Sweden)

    Kamyar Mansori

    2018-01-01

    Full Text Available Objectives The aim of this study was to determine the factors associated with the spatial distribution of the incidence of colorectal cancer (CRC in the neighborhoods of Tehran, Iran using Bayesian spatial models. Methods This ecological study was implemented in Tehran on the neighborhood level. Socioeconomic variables, risk factors, and health costs were extracted from the Equity Assessment Study conducted in Tehran. The data on CRC incidence were extracted from the Iranian population-based cancer registry. The Besag-York-Mollié (BYM model was used to identify factors associated with the spatial distribution of CRC incidence. The software programs OpenBUGS version 3.2.3, ArcGIS 10.3, and GeoDa were used for the analysis. Results The Moran index was statistically significant for all the variables studied (p<0.05. The BYM model showed that having a women head of household (median standardized incidence ratio [SIR], 1.63; 95% confidence interval [CI], 1.06 to 2.53, living in a rental house (median SIR, 0.82; 95% CI, 0.71 to 0.96, not consuming milk daily (median SIR, 0.71; 95% CI, 0.55 to 0.94 and having greater household health expenditures (median SIR, 1.34; 95% CI, 1.06 to 1.68 were associated with a statistically significant elevation in the SIR of CRC. The median (interquartile range and mean (standard deviation values of the SIR of CRC, with the inclusion of all the variables studied in the model, were 0.57 (1.01 and 1.05 (1.31, respectively. Conclusions Inequality was found in the spatial distribution of CRC incidence in Tehran on the neighborhood level. Paying attention to this inequality and the factors associated with it may be useful for resource allocation and developing preventive strategies in atrisk areas.

  12. Spatial distribution and influence factors of interprovincial terrestrial physical geographical names in China

    Science.gov (United States)

    Zhang, S.; Wang, Y.; Ju, H.

    2017-12-01

    The interprovincial terrestrial physical geographical entities are the key areas of regional integrated management. Based on toponomy dictionaries and different thematic maps, the attributes and the spatial extent of the interprovincial terrestrial physical geographical names (ITPGN, including terrain ITPGN and water ITPGN) were extracted. The coefficient of variation and Moran's I were combined together to measure the spatial variation and spatial association of ITPGN. The influencing factors of the distribution of ITPGN and the implications for the regional management were further discussed. The results showed that 11325 ITPGN were extracted, including 7082 terrain ITPGN and 4243 water ITPGN. Hunan Province had the largest number of ITPGN in China, and Shanghai had the smallest number. The spatial variance of the terrain ITPGN was larger than that of the water ITPGN, and the ITPGN showed a significant agglomeration phenomenon in the southern part of China. Further analysis showed that the number of ITPGN was positively related with the relative elevation and the population where the relative elevation was lower than 2000m and the population was less than 50 million. But the number of ITPGN showed a negative relationship with the two factors when their values became larger, indicating a large number of unnamed entities existed in complex terrain areas and a decreasing number of terrestrial physical geographical entities in densely populated area. Based on these analysis, we suggest the government take the ITPGN as management units to realize a balance development between different parts of the entities and strengthen the geographical names census and the nomination of unnamed interprovincial physical geographical entities. This study also demonstrated that the methods of literature survey, coefficient of variation and Moran's I can be combined to enhance the understanding of the spatial pattern of ITPGN.

  13. Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel

    2016-12-19

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  14. Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.

    2016-01-01

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  15. Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

    Science.gov (United States)

    Hoos, Anne B.; McMahon, Gerard

    2009-01-01

    Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States—higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

  16. Progress in spatial analysis methods and applications

    CERN Document Server

    Páez, Antonio; Buliung, Ron N; Dall'erba, Sandy

    2010-01-01

    This book brings together developments in spatial analysis techniques, including spatial statistics, econometrics, and spatial visualization, and applications to fields such as regional studies, transportation and land use, population and health.

  17. Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor

    Directory of Open Access Journals (Sweden)

    Liu Xin

    2015-12-01

    Full Text Available Spatial local outlier factor (SLOF algorithm was adopted in this study for spatial outlier detection because of the limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data obtained in the carbon capture and storage (CCS project, the K-Nearest Neighbour (KNN graph was constructed using the latitude and longitude information of the monitoring points to identify the spatial neighbourhood of the monitoring points. Then SLOF was adopted to calculate the outlier degrees of the monitoring points and the 3σ rule was employed to identify the spatial outlier. Finally, the selection of K value was analysed and the optimal one was selected. The results show that, compared with the static threshold method, the proposed algorithm has a higher detection precision. It can overcome the shortcomings of the static threshold method and improve the accuracy and diversity of local outlier detection, which provides a reliable reference for the safety assessment and warning of CCS monitoring.

  18. Hierarchical modeling and analysis for spatial data

    CERN Document Server

    Banerjee, Sudipto; Gelfand, Alan E

    2003-01-01

    Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and dat

  19. Spatial Analysis in Determining Physical Factors of Pedestrian Space Livability, Case Study: Pedestrian Space on Jalan Kemasan, Yogyakarta

    Science.gov (United States)

    Fauzi, A. F.; Aditianata, A.

    2018-02-01

    The existence of street as a place to perform various human activities becomes an important issue nowadays. In the last few decades, cars and motorcycles dominate streets in various cities in the world. On the other hand, human activity on the street is the determinant of the city livability. Previous research has pointed out that if there is lots of human activity in the street, then the city will be interesting. Otherwise, if the street has no activity, then the city will be boring. Learning from that statement, now various cities in the world are developing the concept of livable streets. Livable streets shown by diversity of human activities conducted in the streets’ pedestrian space. In Yogyakarta, one of the streets shown diversity of human activities is Jalan Kemasan. This study attempts to determine the physical factors of pedestrian space affecting the livability in Jalan Kemasan Yogyakarta through spatial analysis. Spatial analysis was performed by overlay technique between liveable point (activity diversity) distribution map and variable distribution map. Those physical pedestrian space research variable included element of shading, street vendors, building setback, seat location, divider between street and pedestrian way, and mixed use building function. More diverse the activity of one variable, then those variable are more affected then others. Overlay result then strengthened by field observation to qualitatively ensure the deduction. In the end, this research will provide valuable input for street and pedestrian space planning that is comfortable for human activities.

  20. Correlation Factors Describing Primary and Spatial Sensations of Sound Fields

    Science.gov (United States)

    ANDO, Y.

    2002-11-01

    The theory of subjective preference of the sound field in a concert hall is established based on the model of human auditory-brain system. The model consists of the autocorrelation function (ACF) mechanism and the interaural crosscorrelation function (IACF) mechanism for signals arriving at two ear entrances, and the specialization of human cerebral hemispheres. This theory can be developed to describe primary sensations such as pitch or missing fundamental, loudness, timbre and, in addition, duration sensation which is introduced here as a fourth. These four primary sensations may be formulated by the temporal factors extracted from the ACF associated with the left hemisphere and, spatial sensations such as localization in the horizontal plane, apparent source width and subjective diffuseness are described by the spatial factors extracted from the IACF associated with the right hemisphere. Any important subjective responses of sound fields may be described by both temporal and spatial factors.

  1. Spatial Characteristics and Driving Factors of Provincial Wastewater Discharge in China.

    Science.gov (United States)

    Chen, Kunlun; Liu, Xiaoqiong; Ding, Lei; Huang, Gengzhi; Li, Zhigang

    2016-12-09

    Based on the increasing pressure on the water environment, this study aims to clarify the overall status of wastewater discharge in China, including the spatio-temporal distribution characteristics of wastewater discharge and its driving factors, so as to provide reference for developing "emission reduction" strategies in China and discuss regional sustainable development and resources environment policies. We utilized the Exploratory Spatial Data Analysis (ESDA) method to analyze the characteristics of the spatio-temporal distribution of the total wastewater discharge among 31 provinces in China from 2002 to 2013. Then, we discussed about the driving factors, affected the wastewater discharge through the Logarithmic Mean Divisia Index (LMDI) method and classified those driving factors. Results indicate that: (1) the total wastewater discharge steadily increased, based on the social economic development, with an average growth rate of 5.3% per year; the domestic wastewater discharge is the main source of total wastewater discharge, and the amount of domestic wastewater discharge is larger than the industrial wastewater discharge. There are many spatial differences of wastewater discharge among provinces via the ESDA method. For example, provinces with high wastewater discharge are mainly the developed coastal provinces such as Jiangsu Province and Guangdong Province. Provinces and their surrounding areas with low wastewater discharge are mainly the undeveloped ones in Northwest China; (2) The dominant factors affecting wastewater discharge are the economy and technological advance; The secondary one is the efficiency of resource utilization, which brings about the unstable effect; population plays a less important role in wastewater discharge. The dominant driving factors affecting wastewater discharge among 31 provinces are divided into three types, including two-factor dominant type, three-factor leading type and four-factor antagonistic type. In addition, the

  2. Factors Affecting the Clinical Measurement of Visuo-Spatial Neglect

    Directory of Open Access Journals (Sweden)

    L. Pizzamiglio

    1992-01-01

    Full Text Available The present study examined a battery of tests to evaluate unilateral spatial neglect; the tests included different tasks involving several modalities of spatial exploration mapping perceptual, motor, attentional and personal or extrapersonal space dimensions. The subjects, 121 right-brain-damaged patients with unilateral neglect, were studied in seven laboratories in four European countries. Relationships among the various tests were examined by correlations, a cluster analysis and by an analysis of individual cases. Different sensitivity was found among various tests for detecting neglect performances. Both the cluster analysis and the single case analysis clearly showed a segregation between personal and extrapersonal neglect. Analysis of the large cluster, including a variety of tests of extra personal neglect, together with the study of single cases, suggests the possibility of differentiating the various manifestations of spatial neglect which can be interpreted on the basis of the descriptions of other individual cases previously reported in the literature. Finally, the present study indicated the relative stability of neglect following the acute phase and its independence from age.

  3. Spatial distribution of juvenile and adult stages of limnetic Cladocera in relation to selected environmental factors

    Directory of Open Access Journals (Sweden)

    Małgorzata Adamczuk

    2012-01-01

    Full Text Available Environmental factors have a varied impact on the development of juvenile and adult Cladocera, depending on their different physiological conditions and body size. The values of these factors alter spatially and temporarily, thus implying that they play a role in the spatial distribution of the pre-reproductive and potentially reproductive stages of cladocerans. The aim of the study was to determine seasonal and spatial variations in the distribution of juvenile and adult individuals of limnetic Cladocera in relation to selected physicochemical factors (temperature, conductivity, pH, concentration of dissolved oxygen, total organic carbon, total suspended solids and fish predation pressure (measured by Chesson’s coefficient λ in deep Lake Piaseczno (eastern Poland. Adult Cladocera displayed spatial distribution related to fish predation pressure. The species selectively eaten, B. coregoni and D. longispina, and non-selectively eaten, D. cucullata, selected the pelagic zone to exist, whereas those avoided by fish, D. brachyurum and B. longirostris, were evenly distributed in the littoral and pelagic zone. Juvenile cladocerans were strongly impacted by physico-chemical factors. Juvenile Daphnia, Diaphanosoma and B. longirostris showed preferences to biotic zones similar to the adults but differed in their habitat choices. Juvenile and adult stages of B. coregoni differed in their distribution, indicating that adult individuals impacted by high predation pressure alternatively modified their habitat selection. Principal component analysis (PCA ordination showed a seasonal tendency for the spatial segregation of the cladocerans, suggesting that possible competitive interactions between the studied cladocerans may also influence their distribution patterns.

  4. Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh

    Science.gov (United States)

    Tan, Z.; Yang, Q.; Zheng, C.; Zheng, Y.

    2017-12-01

    Elevated concentrations of geogenic arsenic in groundwater have been found in many countries to exceed 10 μg/L, the WHO's guideline value for drinking water. A common yet unexplained characteristic of groundwater arsenic spatial distribution is the extensive variability at various spatial scales. This study investigates factors influencing the spatial variability of groundwater arsenic in Bangladesh to improve the accuracy of models predicting arsenic exceedance rate spatially. A novel boosted regression tree method is used to establish a weak-learning ensemble model, which is compared to a linear model using a conventional stepwise logistic regression method. The boosted regression tree models offer the advantage of parametric interaction when big datasets are analyzed in comparison to the logistic regression. The point data set (n=3,538) of groundwater hydrochemistry with 19 parameters was obtained by the British Geological Survey in 2001. The spatial data sets of geological parameters (n=13) were from the Consortium for Spatial Information, Technical University of Denmark, University of East Anglia and the FAO, while the soil parameters (n=42) were from the Harmonized World Soil Database. The aforementioned parameters were regressed to categorical groundwater arsenic concentrations below or above three thresholds: 5 μg/L, 10 μg/L and 50 μg/L to identify respective controlling factors. Boosted regression tree method outperformed logistic regression methods in all three threshold levels in terms of accuracy, specificity and sensitivity, resulting in an improvement of spatial distribution map of probability of groundwater arsenic exceeding all three thresholds when compared to disjunctive-kriging interpolated spatial arsenic map using the same groundwater arsenic dataset. Boosted regression tree models also show that the most important controlling factors of groundwater arsenic distribution include groundwater iron content and well depth for all three

  5. Spatial and null infinity via advanced and retarded conformal factors

    International Nuclear Information System (INIS)

    Hayward, Sean A.

    2003-01-01

    A new approach to space-time asymptotics is presented, refining Penrose's idea of conformal transformations with infinity represented by the conformal boundary of space-time. It is proposed that the Penrose conformal factor be a product of advanced and retarded conformal factors, which asymptotically relate physical and conformal null coordinates and vanish at future and past null infinity respectively. A refined definition of asymptotic flatness at both spatial and null infinity is given, including that the conformal boundary is locally a light cone, with spatial infinity as the vertex. It is shown how to choose the conformal factors so that this asymptotic light cone is locally a metric light cone. The theory is implemented in the spin-coefficient (or null-tetrad) formalism by a joint transformation of the spin-metric and spin-basis (or metric and tetrad). Asymptotic regularity conditions are proposed, based on the conformal boundary locally being a smoothly embedded metric light cone. These conditions ensure that the Bondi-Sachs energy-flux integrals of ingoing and outgoing gravitational radiation decay at spatial infinity such that the total radiated energy is finite, and that the Bondi-Sachs energy-momentum has a unique limit at spatial infinity, coinciding with the uniquely rendered ADM energy-momentum

  6. Developing Talented Soccer Players: An Analysis of Socio-Spatial Factors as Possible Key Constraints

    Directory of Open Access Journals (Sweden)

    Serra-Olivares Jaime

    2016-12-01

    Full Text Available Most studies on the identification and development of soccer talent have been one-dimensional in nature. Although some multi-dimensional analyses have been conducted, few research studies have assessed in any depth the socio-spatial factors influencing talent development. The aim of this particular study was to analyse variations in the international representation of clubs (n = 821 and countries (n = 59 in the development of players who took part in the 2014 FIFA Soccer World Cup. Clubs and countries were ranked and divided into quartiles according to the number of players developed between the ages of 15 and 21 (clubs and countries that developed players for at least three years between these ages and the number of official league matches played by these players up to the age of 23. Significant variations were observed between clubs in terms of the number of developed players who took part in the World Cup and the number of official league matches played by these players up to the age of 23 (p < .05, and also between countries (p < .05. The findings reveal the need to carry out more in-depth studies into the type of training and competition engaged in by elite players in the period of development between the ages of 15 and 21. It may be the case that these factors are potentially decisive socio-spatial constraints in the development of soccer talent.

  7. Socio-economic factors of bacillary dysentery based on spatial correlation analysis in Guangxi Province, China.

    Directory of Open Access Journals (Sweden)

    Chengjing Nie

    Full Text Available BACKGROUND: In the past decade, bacillary dysentery was still a big public health problem in China, especially in Guangxi Province, where thousands of severe diarrhea cases occur every year. METHODS: Reported bacillary dysentery cases in Guangxi Province were obtained from local Centers for Diseases Prevention and Control. The 14 socio-economic indexes were selected as potential explanatory variables for the study. The spatial correlation analysis was used to explore the associations between the selected factors and bacillary dysentery incidence at county level, which was based on the software of ArcGIS10.2 and GeoDA 0.9.5i. RESULTS: The proportion of primary industry, the proportion of younger than 5-year-old children in total population, the number of hospitals per thousand persons and the rates of bacillary dysentery incidence show statistically significant positive correlation. But the proportion of secondary industry, per capital GDP, per capital government revenue, rural population proportion, popularization rate of tap water in rural area, access rate to the sanitation toilets in rural, number of beds in hospitals per thousand persons, medical and technical personnel per thousand persons and the rate of bacillary dysentery incidence show statistically significant negative correlation. The socio-economic factors can be divided into four aspects, including economic development, health development, medical development and human own condition. The four aspects were not isolated from each other, but interacted with each other.

  8. The AIDS epidemic and economic input impact factors in Chongqing, China, from 2006 to 2012: a spatial-temporal analysis.

    Science.gov (United States)

    Zhang, Yanqi; Xiao, Qin; Zhou, Liang; Ma, Dihui; Liu, Ling; Lu, Rongrong; Yi, Dali; Yi, Dong

    2015-03-27

    To analyse the spatial-temporal clustering of the HIV/AIDS epidemic in Chongqing and to explore its association with the economic indices of AIDS prevention and treatment. Data on the HIV/AIDS epidemic and economic indices of AIDS prevention and treatment were obtained from the annual reports of the Chongqing Municipal Center for Disease Control for 2006-2012. Spatial clustering analysis, temporal-spatial clustering analysis, and spatial regression were used to conduct statistical analysis. The annual average new HIV infection rate, incidence rate for new AIDS cases, and rate of people living with HIV in Chongqing were 5.97, 2.42 and 28.12 per 100,000, respectively, for 2006-2012. The HIV/AIDS epidemic showed a non-random spatial distribution (Moran's I≥0.310; p<0.05). The epidemic hotspots were distributed in the 15 mid-western counties. The most likely clusters were primarily located in the central region and southwest of Chongqing and occurred in 2010-2012. The regression coefficients of the total amount of special funds allocated to AIDS and to the public awareness unit for the numbers of new HIV cases, new AIDS cases, and people living with HIV were 0.775, 0.976 and 0.816, and -0.188, -0.259 and -0.215 (p<0.002), respectively. The Chongqing HIV/AIDS epidemic showed temporal-spatial clustering and was mainly clustered in the mid-western and south-western counties, showing an upward trend over time. The amount of special funds dedicated to AIDS and to the public awareness unit showed positive and negative relationships with HIV/AIDS spatial clustering, respectively. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  9. An analytical evaluation for spatial-dependent intra-pebble Dancoff factor and escape probability

    International Nuclear Information System (INIS)

    Kim, Songhyun; Kim, Hong-Chul; Kim, Jong Kyung; Kim, Soon Young; Noh, Jae Man

    2009-01-01

    The analytical evaluation of spatial-dependent intra-pebble Dancoff factors and their escape probabilities is pursued by the model developed in this study. Intra-pebble Dancoff factors and their escape probabilities are calculated as a function of fuel kernel radius, number of fuel kernels, and fuel region radius. The method in this study can be easily utilized to analyze the tendency of spatial-dependent intra-pebble Dancoff factor and spatial-dependent fuel region escape probability for the various geometries because it is faster than the MCNP method as well as good accuracy. (author)

  10. Spatial Analysis of Regional Factors and Lung Cancer Mortality in China, 1973-2013.

    Science.gov (United States)

    Shen, Xiaoping; Wang, Limin; Zhu, Li

    2017-04-01

    Background: China's lung cancer crude death rate has increased 6.9-fold from 1973 to 2014. During this time, the country experienced extremely rapid economic growth and social change. It is important to understand the effects of risk factors on lung cancer mortality (LCM) for better allocation of limited resources of cancer prevention and control in China. Methods: Using three nationwide mortality surveys from 1973 to 2005, Global Health Data Exchange data in 2013, three nationwide smoking surveys from 1984 to 2013, four population censuses from 1964 to 2000, and other datasets, we have compiled datasets and developed spatial random effect models to assess the association of various area-level-contributing factors on LCM. Spatial scan statistics are used to detect high-risk clusters of LCM. Results: LCM is higher in urban and more industrialized areas (RR = 1.17) compared with those in rural areas. The level of industrial development's effect is higher for men, which accounts for about 70% of all LCM. Smoking is positively associated with regional variation of LCM rates, and the effect is higher for women than for men. Conclusions: The geographic pattern of high LCM in China is different from that of Western countries. LCM is positively associated with higher socioeconomic status, with more urbanized areas at a higher level of industrial development. Impact: There is a need to further explore additional risk in the high-risk clusters. The study is about China, but this situation may happen in other countries experiencing rapid industrialization and other developing countries. Cancer Epidemiol Biomarkers Prev; 26(4); 569-77. ©2017 AACR See all the articles in this CEBP Focus section, "Geospatial Approaches to Cancer Control and Population Sciences." ©2017 American Association for Cancer Research.

  11. Spatial analysis of under-5 mortality and potential risk factors in the Basse Health and Demographic Surveillance System, the Gambia.

    Science.gov (United States)

    Quattrochi, John; Jasseh, Momodou; Mackenzie, Grant; Castro, Marcia C

    2015-07-01

    To describe the spatial pattern in under-5 mortality rates in the Basse Health and Demographic Surveillance System (BHDSS) and to test for associations between under-5 deaths and biodemographic and socio-economic risk factors. Using data on child survival from 2007 to 2011 in the BHDSS, we mapped under-5 mortality by km(2) . We tested for spatial clustering of high or low death rates using Kulldorff's spatial scan statistic. Associations between child death and a variety of biodemographic and socio-economic factors were assessed with Cox proportional hazards models, and deviance residuals from the best-fitting model were tested for spatial clustering. The overall death rate among children under 5 was 0.0195 deaths per child-year. We found two spatial clusters of high death rates and one spatial cluster of low death rates; children in the two high clusters died at a rate of 0.0264 and 0.0292 deaths per child-year, while in the low cluster, the rate was 0.0144 deaths per child-year. We also found that children born to Fula mothers experienced, on average, a higher hazard of death, whereas children born in the households in the upper two quintiles of asset ownership experienced, on average, a lower hazard of death. After accounting for the spatial distribution of biodemographic and socio-economic characteristics, we found no residual spatial pattern in child mortality risk. This study demonstrates that significant inequality in under-5 death rates can occur within a relatively small area (1100 km(2) ). Risks of under-5 mortality were associated with mother's ethnicity and household wealth. If high mortality clusters persist, then equity concerns may require additional public health efforts in those areas. © 2015 John Wiley & Sons Ltd.

  12. Spatial analysis of Schistosomiasis in Hubei Province, China: a GIS-based analysis of Schistosomiasis from 2009 to 2013.

    Directory of Open Access Journals (Sweden)

    Yan-Yan Chen

    Full Text Available Schistosomiasis remains a major public health problem in China. The major endemic areas are located in the lake and marshland regions of southern China, particularly in areas along the middle and low reach of the Yangtze River. Spatial analytical techniques are often used in epidemiology to identify spatial clusters in disease regions. This study assesses the spatial distribution of schistosomiasis and explores high-risk regions in Hubei Province, China to provide guidance on schistosomiasis control in marshland regions.In this study, spatial autocorrelation methodologies, including global Moran's I and local Getis-Ord statistics, were utilized to describe and map spatial clusters and areas where human Schistosoma japonicum infection is prevalent at the county level in Hubei province. In addition, linear logistic regression model was used to determine the characteristics of spatial autocorrelation with time.The infection rates of S. japonicum decreased from 2009 to 2013. The global autocorrelation analysis results on the infection rate of S. japonicum for five years showed statistical significance (Moran's I > 0, P < 0.01, which suggested that spatial clusters were present in the distribution of S. japonicum infection from 2009 to 2013. Local autocorrelation analysis results showed that the number of highly aggregated areas ranged from eight to eleven within the five-year analysis period. The highly aggregated areas were mainly distributed in eight counties.The spatial distribution of human S. japonicum infections did not exhibit a temporal change at the county level in Hubei Province. The risk factors that influence human S. japonicum transmission may not have changed after achieving the national criterion of infection control. The findings indicated that spatial-temporal surveillance of S. japonicum transmission plays a significant role on schistosomiasis control. Timely and integrated prevention should be continued, especially in the Yangtze

  13. The spatial analysis on hemorrhagic fever with renal syndrome in Jiangsu province, China based on geographic information system.

    Directory of Open Access Journals (Sweden)

    Changjun Bao

    Full Text Available Hemorrhagic fever with renal syndrome (HFRS is endemic in mainland China, accounting for 90% of total reported cases worldwide, and Jiangsu is one of the most severely affected provinces. In this study, the authors conducted GIS-based spatial analyses in order to determine the spatial distribution of the HFRS cases, identify key areas and explore risk factors for public health planning and resource allocation.Interpolation maps by inverse distance weighting were produced to detect the spatial distribution of HFRS cases in Jiangsu from 2001 to 2011. Spatio-temporal clustering was applied to identify clusters at the county level. Spatial correlation analysis was conducted to detect influencing factors of HFRS in Jiangsu.HFRS cases in Jiangsu from 2001 to 2011 were mapped and the results suggested that cases in Jiangsu were not distributed randomly. Cases were mainly distributed in northeastern and southwestern Jiangsu, especially in Dafeng and Sihong counties. It was notable that prior to this study, Sihong county had rarely been reported as a high-risk area of HFRS. With the maximum spatial size of 50% of the total population and the maximum temporal size of 50% of the total population, spatio-temporal clustering showed that there was one most likely cluster (LLR = 624.52, P<0.0001, RR = 8.19 and one second-most likely cluster (LLR = 553.97, P<0.0001, RR = 8.25, and both of these clusters appeared from 2001 to 2004. Spatial correlation analysis showed that the incidence of HFRS in Jiangsu was influenced by distances to highways, railways, rivers and lakes.The application of GIS together with spatial interpolation, spatio-temporal clustering and spatial correlation analysis can effectively identify high-risk areas and factors influencing HFRS incidence to lay a foundation for researching its pathogenesis.

  14. Scalable group level probabilistic sparse factor analysis

    DEFF Research Database (Denmark)

    Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard

    2017-01-01

    Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...

  15. Spatial Econometric data analysis: moving beyond traditional models

    NARCIS (Netherlands)

    Florax, R.J.G.M.; Vlist, van der A.J.

    2003-01-01

    This article appraises recent advances in the spatial econometric literature. It serves as the introduction too collection of new papers on spatial econometric data analysis brought together in this special issue, dealing specifically with new extensions to the spatial econometric modeling

  16. The spatial analysis on hemorrhagic fever with renal syndrome in Jiangsu province, China based on geographic information system.

    Science.gov (United States)

    Bao, Changjun; Liu, Wanwan; Zhu, Yefei; Liu, Wendong; Hu, Jianli; Liang, Qi; Cheng, Yuejia; Wu, Ying; Yu, Rongbin; Zhou, Minghao; Shen, Hongbing; Chen, Feng; Tang, Fenyang; Peng, Zhihang

    2014-01-01

    Hemorrhagic fever with renal syndrome (HFRS) is endemic in mainland China, accounting for 90% of total reported cases worldwide, and Jiangsu is one of the most severely affected provinces. In this study, the authors conducted GIS-based spatial analyses in order to determine the spatial distribution of the HFRS cases, identify key areas and explore risk factors for public health planning and resource allocation. Interpolation maps by inverse distance weighting were produced to detect the spatial distribution of HFRS cases in Jiangsu from 2001 to 2011. Spatio-temporal clustering was applied to identify clusters at the county level. Spatial correlation analysis was conducted to detect influencing factors of HFRS in Jiangsu. HFRS cases in Jiangsu from 2001 to 2011 were mapped and the results suggested that cases in Jiangsu were not distributed randomly. Cases were mainly distributed in northeastern and southwestern Jiangsu, especially in Dafeng and Sihong counties. It was notable that prior to this study, Sihong county had rarely been reported as a high-risk area of HFRS. With the maximum spatial size of 50% of the total population and the maximum temporal size of 50% of the total population, spatio-temporal clustering showed that there was one most likely cluster (LLR = 624.52, Phighways, railways, rivers and lakes. The application of GIS together with spatial interpolation, spatio-temporal clustering and spatial correlation analysis can effectively identify high-risk areas and factors influencing HFRS incidence to lay a foundation for researching its pathogenesis.

  17. Where do overweight women in Ghana live? Answers from exploratory spatial data analysis

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    Fidelia A.A. Dake

    2012-03-01

    Full Text Available Contextual influence on health outcomes is increasingly becoming an important area of research. Analytical techniques such as spatial analysis help explain the variations and dynamics in health inequalities across different context and among different population groups. This paper explores spatial clustering in body mass index among Ghanaian women by analysing data from the 2008 Ghana Demographic and Health Survey using exploratory spatial data analysis techniques. Overweight was a more common occurrence in urban areas than in rural areas. Close to a quarter of the clusters in Ghana, mostly those in the southern sector contained women who were overweight. Women who lived in clusters where the women were overweight were more likely to live around other clusters where the women were also overweight. The results suggest that the urban environment could be a potential contributing factor to the high levels of obesity in urban areas of Ghana. There is the need for researchers to include a spatial dimension to obesity research in Ghana paying particular attention the urban environment.

  18. Determinants of the distribution and concentration of biogas production in Germany. A spatial econometric analysis

    International Nuclear Information System (INIS)

    Scholz, Lukas

    2015-01-01

    The biogas production in Germany is characterized by a heterogeneous distribution and the formation of regional centers. In the present study the determinants of the spatial distribution and concentration are analyzed with methods of spatial statistics and spatial econometrics. In addition to the consideration of ''classic'' site factors of agricultural production, the analysis here focuses on the possible relevance of agglomeration effects. The results of the work contribute to a better understanding of the regional distribution and concentration of the biogas production in Germany. [de

  19. Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh.

    Science.gov (United States)

    Bi, Qifang; Azman, Andrew S; Satter, Syed Moinuddin; Khan, Azharul Islam; Ahmed, Dilruba; Riaj, Altaf Ahmed; Gurley, Emily S; Lessler, Justin

    2016-02-01

    Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets) near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs) to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98), type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00), and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00) exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a cholera endemic

  20. Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh.

    Directory of Open Access Journals (Sweden)

    Qifang Bi

    2016-02-01

    Full Text Available Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98, type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00, and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00 exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a

  1. Factors Influencing the Spatial Difference in Household Energy Consumption in China

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    Yongxia Ding

    2016-12-01

    Full Text Available What factors determine the spatial heterogeneity of household energy consumption (HEC in China? Can the impacts of these factors be quantified? What are the trends and characteristics of the spatial differences? To date, these issues are still unclear. Based on the STIRPAT model and panel dataset for 30 provinces in China over the period 1997–2013, this paper investigated influences of the income per capita, urbanization level and annual average temperature on HEC, and revealed the spatial effects of these influencing factors. The results show that the income level is the main influencing factor, followed by the annual average temperature. There exists a diminishing marginal contribution with increasing income. The influence of urbanization level varies according to income level. In addition, from the eastern region to western region of China, variances largely depend upon economic level at the provincial level. From the northern region to southern region, change is mainly caused by temperature. The urbanization level has more significant impact on the structure and efficiency of household energy consumption than on its quantity. These results could provide reference for policy making and energy planning.

  2. TEMPORAL AND SPATIAL ANALYSIS OF EXTREME RAINFALL ON THE SLOPE AREA OF MT. MERAPI

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    Dhian Dharma Prayuda

    2015-02-01

    Full Text Available Rainfall has temporal and spatial characteristics with certain pattern which are affected by topographic variations and climatology of an area. The intensity of extreme rainfall is one of important characteristics related to the trigger factors for debris flow. This research will discuss the result of analysis on short duration rainfall data in the south and west slope of Mt. Merapi. Measured hourly rainfall data in 14 rainfall stations for the last 27 years were used as analysis input. The rainfall intensity-duration-frequency relationship (IDF was derived using empirical formula of Sherman, Kimijima, Haspers, and Mononobe method. The analysis on the characteristics of extreme rainfall intensity was performed by conducting spatial interpolation using Inverse Distance Weighted (IDW method. Result of analysis shows that IDF of rainfall in the research area fits to Sherman’s formula. Besides, the spatial distribution pattern of maximum rainfall intensity was assessed on the basis of area rainfall. Furthermore, the difference on the result of spatial map for one hour extreme rainfall based on isolated event and non-isolated event method can be evaluated. The result of this preliminary research is expected to be inputs in the establishment of debris flow early warning in Mt. Merapi slope area.

  3. Discourse Factors Influencing Spatial Descriptions in English and German

    Science.gov (United States)

    Vorwerg, Constanze; Tenbrink, Thora

    The ways in which objects are referred to by using spatial language depend on many factors, including the spatial configuration and the discourse context. We present the results of a web experiment in which speakers were asked to either describe where a specified item was located in a picture containing several items, or which item was specified. Furthermore, conditions differed as to whether the first six configurations were specifically simple or specifically complex. Results show that speakers' spatial descriptions are more detailed if the question is where rather than which, mirroring the fact that contrasting the target item from the others in which tasks may not always require an equally detailed spatial description as in where tasks. Furthermore, speakers are influenced by the complexity of initial configurations in intricate ways: on the one hand, individual speakers tend to self-align with respect to their earlier linguistic strategies; however, also a contrast effect could be identified with respect to the usage of combined projective terms.

  4. Socio-economic and Climate Factors Associated with Dengue Fever Spatial Heterogeneity: A Worked Example in New Caledonia

    Science.gov (United States)

    Teurlai, Magali; Menkès, Christophe Eugène; Cavarero, Virgil; Degallier, Nicolas; Descloux, Elodie; Grangeon, Jean-Paul; Guillaumot, Laurent; Libourel, Thérèse; Lucio, Paulo Sergio; Mathieu-Daudé, Françoise; Mangeas, Morgan

    2015-01-01

    Background/Objectives Understanding the factors underlying the spatio-temporal distribution of infectious diseases provides useful information regarding their prevention and control. Dengue fever spatio-temporal patterns result from complex interactions between the virus, the host, and the vector. These interactions can be influenced by environmental conditions. Our objectives were to analyse dengue fever spatial distribution over New Caledonia during epidemic years, to identify some of the main underlying factors, and to predict the spatial evolution of dengue fever under changing climatic conditions, at the 2100 horizon. Methods We used principal component analysis and support vector machines to analyse and model the influence of climate and socio-economic variables on the mean spatial distribution of 24,272 dengue cases reported from 1995 to 2012 in thirty-three communes of New Caledonia. We then modelled and estimated the future evolution of dengue incidence rates using a regional downscaling of future climate projections. Results The spatial distribution of dengue fever cases is highly heterogeneous. The variables most associated with this observed heterogeneity are the mean temperature, the mean number of people per premise, and the mean percentage of unemployed people, a variable highly correlated with people's way of life. Rainfall does not seem to play an important role in the spatial distribution of dengue cases during epidemics. By the end of the 21st century, if temperature increases by approximately 3°C, mean incidence rates during epidemics could double. Conclusion In New Caledonia, a subtropical insular environment, both temperature and socio-economic conditions are influencing the spatial spread of dengue fever. Extension of this study to other countries worldwide should improve the knowledge about climate influence on dengue burden and about the complex interplay between different factors. This study presents a methodology that can be used as a

  5. Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

    Science.gov (United States)

    Rasam, A. R. A.; Ghazali, R.; Noor, A. M. M.; Mohd, W. M. N. W.; Hamid, J. R. A.; Bazlan, M. J.; Ahmad, N.

    2014-02-01

    Cholera spatial epidemiology is the study of the spread and control of the disease spatial pattern and epidemics. Previous studies have shown that multi-factorial causation such as human behaviour, ecology and other infectious risk factors influence the disease outbreaks. Thus, understanding spatial pattern and possible interrelationship factors of the outbreaks are crucial to be explored an in-depth study. This study focuses on the integration of geographical information system (GIS) and epidemiological techniques in exploratory analyzing the cholera spatial pattern and distribution in the selected district of Sabah. Spatial Statistic and Pattern tools in ArcGIS and Microsoft Excel software were utilized to map and analyze the reported cholera cases and other data used. Meanwhile, cohort study in epidemiological technique was applied to investigate multiple outcomes of the disease exposure. The general spatial pattern of cholera was highly clustered showed the disease spread easily at a place or person to others especially 1500 meters from the infected person and locations. Although the cholera outbreaks in the districts are not critical, it could be endemic at the crowded areas, unhygienic environment, and close to contaminated water. It was also strongly believed that the coastal water of the study areas has possible relationship with the cholera transmission and phytoplankton bloom since the areas recorded higher cases. GIS demonstrates a vital spatial epidemiological technique in determining the distribution pattern and elucidating the hypotheses generating of the disease. The next research would be applying some advanced geo-analysis methods and other disease risk factors for producing a significant a local scale predictive risk model of the disease in Malaysia.

  6. Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

    International Nuclear Information System (INIS)

    Rasam, A R A; Ghazali, R; Noor, A M M; Mohd, W M N W; Hamid, J R A; Bazlan, M J; Ahmad, N

    2014-01-01

    Cholera spatial epidemiology is the study of the spread and control of the disease spatial pattern and epidemics. Previous studies have shown that multi-factorial causation such as human behaviour, ecology and other infectious risk factors influence the disease outbreaks. Thus, understanding spatial pattern and possible interrelationship factors of the outbreaks are crucial to be explored an in-depth study. This study focuses on the integration of geographical information system (GIS) and epidemiological techniques in exploratory analyzing the cholera spatial pattern and distribution in the selected district of Sabah. Spatial Statistic and Pattern tools in ArcGIS and Microsoft Excel software were utilized to map and analyze the reported cholera cases and other data used. Meanwhile, cohort study in epidemiological technique was applied to investigate multiple outcomes of the disease exposure. The general spatial pattern of cholera was highly clustered showed the disease spread easily at a place or person to others especially 1500 meters from the infected person and locations. Although the cholera outbreaks in the districts are not critical, it could be endemic at the crowded areas, unhygienic environment, and close to contaminated water. It was also strongly believed that the coastal water of the study areas has possible relationship with the cholera transmission and phytoplankton bloom since the areas recorded higher cases. GIS demonstrates a vital spatial epidemiological technique in determining the distribution pattern and elucidating the hypotheses generating of the disease. The next research would be applying some advanced geo-analysis methods and other disease risk factors for producing a significant a local scale predictive risk model of the disease in Malaysia

  7. Influence of Topographic and Hydrographic Factors on the Spatial Distribution of Leptospirosis Disease in São Paulo County, Brazil: An Approach Using Geospatial Techniques and GIS Analysis

    Science.gov (United States)

    Ferreira, M. C.; Ferreira, M. F. M.

    2016-06-01

    Leptospirosis is a zoonosis caused by Leptospira genus bacteria. Rodents, especially Rattus norvegicus, are the most frequent hosts of this microorganism in the cities. The human transmission occurs by contact with urine, blood or tissues of the rodent and contacting water or mud contaminated by rodent urine. Spatial patterns of concentration of leptospirosis are related to the multiple environmental and socioeconomic factors, like housing near flooding areas, domestic garbage disposal sites and high-density of peoples living in slums located near river channels. We used geospatial techniques and geographical information system (GIS) to analysing spatial relationship between the distribution of leptospirosis cases and distance from rivers, river density in the census sector and terrain slope factors, in Sao Paulo County, Brazil. To test this methodology we used a sample of 183 geocoded leptospirosis cases confirmed in 2007, ASTER GDEM2 data, hydrography and census sectors shapefiles. Our results showed that GIS and geospatial analysis techniques improved the mapping of the disease and permitted identify the spatial pattern of association between location of cases and spatial distribution of the environmental variables analyzed. This study showed also that leptospirosis cases might be more related to the census sectors located on higher river density areas and households situated at shorter distances from rivers. In the other hand, it was not possible to assert that slope terrain contributes significantly to the location of leptospirosis cases.

  8. Relative risk of visceral leishmaniasis in Brazil: a spatial analysis in urban area.

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    Valdelaine Etelvina Miranda de Araújo

    2013-11-01

    Full Text Available BACKGROUND: Visceral leishmaniasis (VL is a vector-borne disease whose factors involved in transmission are poorly understood, especially in more urban and densely populated counties. In Brazil, the VL urbanization is a challenge for the control program. The goals were to identify the greater risk areas for human VL and the risk factors involved in transmission. METHODOLOGY: This is an ecological study on the relative risk of human VL. Spatial units of analysis were the coverage areas of the Basic Health Units (146 small-areas of Belo Horizonte, Minas Gerais State, Brazil. Human VL cases, from 2007 to 2009 (n = 412, were obtained in the Brazilian Reportable Disease Information System. Bayesian approach was used to model the relative risk of VL including potential risk factors involved in transmission (canine infection, socioeconomic and environmental features and to identify the small-areas of greater risk to human VL. PRINCIPAL FINDINGS: The relative risk of VL was shown to be correlated with income, education, and the number of infected dogs per inhabitants. The estimates of relative risk of VL were higher than 1.0 in 54% of the areas (79/146. The spatial modeling highlighted 14 areas with the highest relative risk of VL and 12 of them are concentrated in the northern region of the city. CONCLUSIONS: The spatial analysis used in this study is useful for the identification of small-areas according to risk of human VL and presents operational applicability in control and surveillance program in an urban environment with an unequal spatial distribution of the disease. Thus the frequent monitoring of relative risk of human VL in small-areas is important to direct and prioritize the actions of the control program in urban environment, especially in big cities.

  9. Epidemiological features and risk factors associated with the spatial and temporal distribution of human brucellosis in China

    Science.gov (United States)

    2013-01-01

    Background Human brucellosis incidence in China has been increasing dramatically since 1999. However, epidemiological features and potential factors underlying the re-emergence of the disease remain less understood. Methods Data on human and animal brucellosis cases at the county scale were collected for the year 2004 to 2010. Also collected were environmental and socioeconomic variables. Epidemiological features including spatial and temporal patterns of the disease were characterized, and the potential factors related to the spatial heterogeneity and the temporal trend of were analysed using Poisson regression analysis, Granger causality analysis, and autoregressive distributed lag (ADL) models, respectively. Results The epidemic showed a significantly higher spatial correlation with the number of sheep and goats than swine and cattle. The disease was most prevalent in grassland areas with elevation between 800–1,600 meters. The ADL models revealed that local epidemics were correlated with comparatively lower temperatures and less sunshine in winter and spring, with a 1–7 month lag before the epidemic peak in May. Conclusions Our findings indicate that human brucellosis tended to occur most commonly in grasslands at moderate elevation where sheep and goats were the predominant livestock, and in years with cooler winter and spring or less sunshine. PMID:24238301

  10. Spatial analysis of myocardial infarction in Iran: National report from the Iranian myocardial infarction registry

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    Ali Ahmadi

    2015-01-01

    Full Text Available Background: Myocardial infarction (MI is a leading cause of mortality and morbidity in Iran. No spatial analysis of MI has been conducted to date. The present study was conducted to determine the pattern of MI incidence and to identify the associated factors in Iran by province. Materials and Methods: This study has two parts. One part is prospective and hospital-based, and the other part is an ecological study. In this study, the data of 20,750 new MI cases registered in Iranian Myocardial Infarction Registry in 2012 were used. For spatial analysis in global and local, spatial autocorrelation, Moran′s I, Getis-Ord, and logistic regression models were used. Data were analyzed by Stata software and ArcGIS 9.3. Results: Based on autocorrelation coefficient, a specific pattern was observed in the distribution of MI incidence in different provinces (Moran′s I: 0.75, P < 0.001. Spatial pattern of incidence was approximately the same in men and women. MI incidence was clustering in six provinces (North Khorasan, Yazd, Kerman, Semnan, Golestan, and Mazandaran. Out of the associated factors with clustered MI in six provinces, temperature, humidity, hypertension, smoking, and body mass index (BMI could be mentioned. Hypertension, smoking, and BMI contributed to clustering with, respectively, 2.36, 1.31, and 1.31 odds ratio. Conclusion: Addressing the place-based pattern of incidence and clarifying their epidemiologic dimension, including spatial analysis, has not yet been implemented in Iran. Report on MI incidence rate by place and formal borders is useful and is used in the planning and prioritization in different levels of health system.

  11. Analysis Framework of China’s Grain Production System: A Spatial Resilience Perspective

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    Dazhuan Ge

    2017-12-01

    Full Text Available China’s grain production has transformed from absolute shortage to a current structural oversupply. High-intensity production introduced further challenges for the eco-environment, smallholder livelihood, and the man-land interrelationship. Driven by urban-rural transformation, research on food security patterns and grain production has expanded into a new field. To analyze the challenges and required countermeasures for China’s grain production system (GPS, this study constructed a theoretical GPS framework based on space resilience. Firstly, a new GPS concept was proposed and a functional system was established for protecting the regional food security, thus guaranteeing smallholder livelihood, stabilizing urban-rural transformation, and sustaining the eco-environment in terms of economic, social, and ecological attributes of the GPS. Secondly, based on a cross-scale interaction analysis that varied from a smallholder scale to a global scale, the systematic crisis of the GPS was analyzed. Thirdly, a cross-scale analytic framework of the GPS was formed from the perspective of spatial resilience, integrating both inner and external disturbance factors of the GPS. Both spatial heterogeneity and connectivity of internal and external disturbance factors are important contents of system space resilience. Finally, the hierarchy of spatial resilience of GPS became clear. The transformation of labor force and the land use transition form key thresholds of the GPS. In summary: based on protecting the basic functions of the GPS, the cross-scale effect of systematic disturbance factors and relevant countermeasures for spatial resilience are effectively influenced by the coordination of the interests of multiple stakeholders; spatial resilience is an effective analytical tool for GPS regulation, providing a reference for revealing the inherent mechanism and functional evolution of the GPS in the process of urban-rural transformation.

  12. Spatial data analysis and the use of maps in scientific health articles.

    Science.gov (United States)

    Nucci, Luciana Bertoldi; Souccar, Patrick Theodore; Castilho, Silvia Diez

    2016-07-01

    Despite the growing number of studies with a characteristic element of spatial analysis, the application of the techniques is not always clear and its continuity in epidemiological studies requires careful evaluation. To verify the spread and use of those processes in national and international scientific papers. An assessment was made of periodicals according to the impact index. Among 8,281 journals surveyed, four national and four international were selected, of which 1,274 articles were analyzed regarding the presence or absence of spatial analysis techniques. Just over 10% of articles published in 2011 in high impact journals, both national and international, showed some element of geographical location. Although these percentages vary greatly from one journal to another, denoting different publication profiles, we consider this percentage as an indication that location variables have become an important factor in studies of health.

  13. Spatial analysis of the electrical energy demand in Greece

    International Nuclear Information System (INIS)

    Tyralis, Hristos; Mamassis, Nikos; Photis, Yorgos N.

    2017-01-01

    The Electrical Energy Demand (EED) of the agricultural, commercial and industrial sector in Greece, as well as its use for domestic activities, public and municipal authorities and street lighting are analysed spatially using Geographical Information System and spatial statistical methods. The analysis is performed on data which span from 2008 to 2012 and have annual temporal resolution and spatial resolution down to the NUTS (Nomenclature of Territorial Units for Statistics) level 3. The aim is to identify spatial patterns of the EED and its transformations such as the ratios of the EED to socioeconomic variables, i.e. the population, the total area, the population density and the Gross Domestic Product (GDP). Based on the analysis, Greece is divided in five regions, each one with a different development model, i.e. Attica and Thessaloniki which are two heavily populated major poles, Thessaly and Central Greece which form a connected geographical region with important agricultural and industrial sector, the islands and some coastal areas which are characterized by an important commercial sector and the rest Greek areas. The spatial patterns can provide additional information for policy decision about the electrical energy management and better representation of the regional socioeconomic conditions. - Highlights: • We visualize spatially the Electrical Energy Demand (EED) in Greece. • We apply spatial analysis methods to the EED data. • Spatial patterns of the EED are identified. • Greece is classified in five distinct groups, based on the analysis. • The results can be used for optimal planning of the electric system.

  14. Spatial analysis of weed patterns

    NARCIS (Netherlands)

    Heijting, S.

    2007-01-01

    Keywords: Spatial analysis, weed patterns, Mead’s test, space-time correlograms, 2-D correlograms, dispersal, Generalized Linear Models, heterogeneity, soil, Taylor’s power law. Weeds in agriculture occur in patches. This thesis is a contribution to the characterization of this patchiness, to its

  15. Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis

    Science.gov (United States)

    Tang, Zhongqian; Zhang, Hua; Yi, Shanzhen; Xiao, Yangfan

    2018-03-01

    GIS-based multi-criteria decision analysis (MCDA) is increasingly used to support flood risk assessment. However, conventional GIS-MCDA methods fail to adequately represent spatial variability and are accompanied with considerable uncertainty. It is, thus, important to incorporate spatial variability and uncertainty into GIS-based decision analysis procedures. This research develops a spatially explicit, probabilistic GIS-MCDA approach for the delineation of potentially flood susceptible areas. The approach integrates the probabilistic and the local ordered weighted averaging (OWA) methods via Monte Carlo simulation, to take into account the uncertainty related to criteria weights, spatial heterogeneity of preferences and the risk attitude of the analyst. The approach is applied to a pilot study for the Gucheng County, central China, heavily affected by the hazardous 2012 flood. A GIS database of six geomorphological and hydrometeorological factors for the evaluation of susceptibility was created. Moreover, uncertainty and sensitivity analysis were performed to investigate the robustness of the model. The results indicate that the ensemble method improves the robustness of the model outcomes with respect to variation in criteria weights and identifies which criteria weights are most responsible for the variability of model outcomes. Therefore, the proposed approach is an improvement over the conventional deterministic method and can provides a more rational, objective and unbiased tool for flood susceptibility evaluation.

  16. Analyzing Spatial Factors in Crime-inducing Culture in Chaloos

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    Mohammad Hossein Omidi Noghlehbari

    2017-02-01

    Full Text Available Feelings of security in urban areas are one of the qualitative criteria of living space. With the rise in urbanization and increasing abnormal urban behavior, especially offenses, this issue has become of great importance. This study aims to identify and analyze the effects of spatial factors on crime-inducing culture in affluent and non-affluent neighborhoods of Chaloos. The method used in this study is analytical-descriptive and it is practical in terms of objective. In order to study and understand the status of physical-spatial structures of affluent and non-affluent neighborhoods in Chaloos, we used field study method. Data were collected through interview, notes taking and questionnaire. The results indicate that in affluent neighborhoods, all criteria components of spatial differences are above the mean (Mean=3, but in non-affluent neighborhoods, all components are lower than the mean. In addition, there is a significant relationship between criteria components of spatial differences and the formation of crime-inducing culture (except the diagnosis component in the affluent and non-affluent neighborhoods.

  17. Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001–2011

    International Nuclear Information System (INIS)

    Li, Li; Qian, Jun; Ou, Chun-Quan; Zhou, Ying-Xue; Guo, Cui; Guo, Yuming

    2014-01-01

    There is an increasing interest in spatial and temporal variation of air pollution and its association with weather conditions. We presented the spatial and temporal variation of Air Pollution Index (API) and examined the associations between API and meteorological factors during 2001–2011 in Guangzhou, China. A Seasonal-Trend Decomposition Procedure Based on Loess (STL) was used to decompose API. Wavelet analyses were performed to examine the relationships between API and several meteorological factors. Air quality has improved since 2005. APIs were highly correlated among five monitoring stations, and there were substantial temporal variations. Timescale-dependent relationships were found between API and a variety of meteorological factors. Temperature, relative humidity, precipitation and wind speed were negatively correlated with API, while diurnal temperature range and atmospheric pressure were positively correlated with API in the annual cycle. Our findings should be taken into account when determining air quality forecasts and pollution control measures. - Highlights: • Air pollution is still serious in Guangzhou, China. • Air Pollution Index was associated with a variety of meteorological parameters. • The temporal relationships were timescale-dependent. • The findings should be taken into account in air quality forecasts and pollution control. - Spatial and temporal variation of API and its timescale-dependent relationship with meteorological factors in Guangzhou were demonstrated

  18. Field evaluation of spatial repellency of metofluthrin-impregnated latticework plastic strips against Aedes aegypti (L.) and analysis of environmental factors affecting its efficacy in My Tho City, Tien Giang, Vietnam.

    Science.gov (United States)

    Kawada, Hitoshi; Iwasaki, Tomonori; LE Loan, Luu; Tien, Tran Khanh; Mai, Nguyen Thi Nhu; Shono, Yoshinori; Katayama, Yasuyuki; Takagi, Masahiro

    2006-12-01

    Spatial repellency of metofluthrin-impregnated polyethylene latticework plastic strips against Aedes aegypti mosquitoes was evaluated. Analysis of environmental factors affecting the efficacy of these strips, such as room temperature, humidity, and house structure, was performed in a residential area in My Tho City, Tien Giang Province, Vietnam. Treatment with the strips at the rate of 1 strip per 2.6-5.52 m(2) (approximately 600 mg per 2.6-5.52 m(2)) reduced the collection of Ae. aegypti resting inside the houses for at least eight weeks. Multiple regression analysis indicated that both increase in the average room temperature and decrease in the area of openings in the rooms that were treated with the strips positively affected the spatial repellency of metofluthrin.

  19. Human factors issues and approaches in the spatial layout of a space station control room, including the use of virtual reality as a design analysis tool

    Science.gov (United States)

    Hale, Joseph P., II

    1994-01-01

    Human Factors Engineering support was provided for the 30% design review of the late Space Station Freedom Payload Control Area (PCA). The PCA was to be the payload operations control room, analogous to the Spacelab Payload Operations Control Center (POCC). This effort began with a systematic collection and refinement of the relevant requirements driving the spatial layout of the consoles and PCA. This information was used as input for specialized human factors analytical tools and techniques in the design and design analysis activities. Design concepts and configuration options were developed and reviewed using sketches, 2-D Computer-Aided Design (CAD) drawings, and immersive Virtual Reality (VR) mockups.

  20. Crash rates analysis in China using a spatial panel model

    Directory of Open Access Journals (Sweden)

    Wonmongo Lacina Soro

    2017-10-01

    Full Text Available The consideration of spatial externalities in traffic safety analysis is of paramount importance for the success of road safety policies. Yet, the quasi-totality of spatial dependence studies on crash rates is performed within the framework of single-equation spatial cross-sectional studies. The present study extends the spatial cross-sectional scheme to a spatial fixed-effects panel model estimated using the maximum likelihood method. The spatial units are the 31 administrative regions of mainland China over the period 2004–2013. The presence of neighborhood effects is evidenced through the Moran's I statistic. Consistent with previous studies, the analysis reveals that omitting the spatial effects in traffic safety analysis is likely to bias the estimation results. The spatial and error lags are all positive and statistically significant suggesting similarities of crash rates pattern in neighboring regions. Some other explanatory variables, such as freight traffic, the length of paved roads and the populations of age 65 and above are related to higher rates while the opposite trend is observed for the Gross Regional Product, the urban unemployment rate and passenger traffic.

  1. Spatial analysis of NDVI readings with difference sampling density

    Science.gov (United States)

    Advanced remote sensing technologies provide research an innovative way of collecting spatial data for use in precision agriculture. Sensor information and spatial analysis together allow for a complete understanding of the spatial complexity of a field and its crop. The objective of the study was...

  2. Spatial analysis for prevalence of type 2 diabetes mellitus - A state investigation

    Science.gov (United States)

    Zainal, Siti Salsabilah Nabilah; Masnan, Maz Jamilah; Amin, Nor Azrita Mohd; Mohamed, Nordin

    2017-11-01

    Type 2 Diabetes Mellitus (T2DM) is a chronic and non-communicable disease, which is characterized as the cause of premature deaths in the world. Unfortunately, Malaysia is one of the many countries facing this epidemic. Based on the increasing current trend of T2DM patients' cases from the National Diabetes Registry (NDR) Report from 2009 to 2012, there were approximately 2.6 million adults aged 18 years and above living with diabetes disease in Malaysia. Thus, this study aims to (i) perform preliminary spatial analysis for the prevalence of T2DM patients based on some factors, (ii) map the findings of the analyses according to some spatial properties, and (iii) analyze the pattern of diagnosed T2DM patients based on the studied factors. The studied population is one of the highest prevalence states of T2DM in Malaysia. This study is expected to reveal some demographic patterns that probably significant to this alarming epidemic.

  3. Spatial Analysis Methods of Road Traffic Collisions

    DEFF Research Database (Denmark)

    Loo, Becky P. Y.; Anderson, Tessa Kate

    Spatial Analysis Methods of Road Traffic Collisions centers on the geographical nature of road crashes, and uses spatial methods to provide a greater understanding of the patterns and processes that cause them. Written by internationally known experts in the field of transport geography, the book...... outlines the key issues in identifying hazardous road locations (HRLs), considers current approaches used for reducing and preventing road traffic collisions, and outlines a strategy for improved road safety. The book covers spatial accuracy, validation, and other statistical issues, as well as link...

  4. Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

    Science.gov (United States)

    Gaitan, S.; ten Veldhuis, J. A. E.

    2015-06-01

    Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.

  5. Analysis of the spatial-temporal variation characteristics of vegetative drought and its relationship with meteorological factors in China from 1982 to 2010.

    Science.gov (United States)

    Shen, Qiu; Liang, Liang; Luo, Xiang; Li, Yanjun; Zhang, Lianpeng

    2017-08-25

    Drought is a complex natural phenomenon that can cause reduced water supplies and can consequently have substantial effects on agriculture and socioeconomic activities. The objective of this study was to gain a better understanding of the spatial-temporal variation characteristics of vegetative drought and its relationship with meteorological factors in China. The Vegetation Condition Index (VCI) dataset calculated from NOAA/AVHRR images from 1982 to 2010 was used to analyse the spatial-temporal variation characteristics of vegetative drought in China. This study also examined the trends in meteorological factors and their influences on drought using monitoring data collected from 686 national ground meteorological stations. The results showed that the VCI appeared to slowly rise in China from 1982 to 2010. From 1982 to 1999, the VCI rose slowly. Then, around 2000, the VCI exhibited a severe fluctuation before it entered into a relatively stable stage. Drought frequencies in China were higher, showing a spatial distribution feature of "higher in the north and lower in the south". Based on the different levels of drought, the frequencies of mild and moderate drought in four geographical areas were higher, and the frequency of severe drought was higher only in ecologically vulnerable areas, such as the Tarim Basin and the Qaidam Basin. Drought was mainly influenced by meteorological factors, which differed regionally. In the northern region, the main influential factor was sunshine duration, while the other factors showed minimal effects. In the southern region and Tibetan Plateau, the main influential factors were sunshine duration and temperature. In the northwestern region, the main influential factors were wind velocity and station atmospheric pressure.

  6. Socio-Spatial Factors Affecting Household Recycling in Townhouses in Pretoria, South Africa

    Directory of Open Access Journals (Sweden)

    Jacques du Toit

    2017-11-01

    Full Text Available Although social factors affecting recycling have been widely researched, the effect of spatial and physical factors posed by medium-density housing, such as townhouses, is less understood. Using the Theory of Planned Behaviour, the relative effect of three sets of factors on household recycling in townhouses are examined, including ‘attitude’ (about recycling, ‘subjective norm’ (social pressure, and ‘perceived behaviour control’ (ability to recycle. A questionnaire survey of 300 households was conducted in Equestria, an enclosed middle-income residential estate consisting of several townhouse complexes. Confirmatory factor analysis verified the three factor measurement model for recycling participation. Both recyclers and non-recyclers showed positive attitudes toward recycling and felt social pressure to recycle. Non-recyclers, however, felt significantly less able to recycle. Most recyclers as well as non-recyclers indicated that certain proposals for increasing recycling may cause them to recycle more, in particular a system through which the management agency arranges access for a recycling company to collect recyclables from strategically located collection points inside the complex. Urban planning and design recommendations for facilitating recycling in townhouses are discussed.

  7. A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti.

    Science.gov (United States)

    Curtis, Andrew; Blackburn, Jason K; Widmer, Jocelyn M; Morris, J Glenn

    2013-04-15

    Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these "hotspots". Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio

  8. Spatial diffusion of influenza outbreak-related climate factors in Chiang Mai Province, Thailand.

    Science.gov (United States)

    Nakapan, Supachai; Tripathi, Nitin Kumar; Tipdecho, Taravudh; Souris, Marc

    2012-10-24

    Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.

  9. Spatial Diffusion of Influenza Outbreak-Related Climate Factors in Chiang Mai Province, Thailand

    Directory of Open Access Journals (Sweden)

    Marc Souris

    2012-10-01

    Full Text Available Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW interpolation and Geographic Information System (GIS techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.

  10. A Flood Risk Assessment of Quang Nam, Vietnam Using Spatial Multicriteria Decision Analysis

    Directory of Open Access Journals (Sweden)

    Chinh Luu

    2018-04-01

    Full Text Available Vietnam is highly vulnerable to flood and storm impacts. Holistic flood risk assessment maps that adequately consider flood risk factors of hazard, exposure, and vulnerability are not available. These are vital for flood risk preparedness and disaster mitigation measures at the local scale. Unfortunately, there is a lack of knowledge about spatial multicriteria decision analysis and flood risk analysis more broadly in Vietnam. In response to this need, we identify and quantify flood risk components in Quang Nam province through spatial multicriteria decision analysis. The study presents a new approach to local flood risk assessment mapping, which combines historical flood marks with exposure and vulnerability data. The flood risk map output could assist and empower decision-makers in undertaking flood risk management activities in the province. Our study demonstrates a methodology to build flood risk assessment maps using flood mark, exposure and vulnerability data, which could be applied in other provinces in Vietnam.

  11. Research progress and hotspot analysis of spatial interpolation

    Science.gov (United States)

    Jia, Li-juan; Zheng, Xin-qi; Miao, Jin-li

    2018-02-01

    In this paper, the literatures related to spatial interpolation between 1982 and 2017, which are included in the Web of Science core database, are used as data sources, and the visualization analysis is carried out according to the co-country network, co-category network, co-citation network, keywords co-occurrence network. It is found that spatial interpolation has experienced three stages: slow development, steady development and rapid development; The cross effect between 11 clustering groups, the main convergence of spatial interpolation theory research, the practical application and case study of spatial interpolation and research on the accuracy and efficiency of spatial interpolation. Finding the optimal spatial interpolation is the frontier and hot spot of the research. Spatial interpolation research has formed a theoretical basis and research system framework, interdisciplinary strong, is widely used in various fields.

  12. Asymptotic analysis of spatial discretizations in implicit Monte Carlo

    International Nuclear Information System (INIS)

    Densmore, Jeffery D.

    2009-01-01

    We perform an asymptotic analysis of spatial discretizations in Implicit Monte Carlo (IMC). We consider two asymptotic scalings: one that represents a time step that resolves the mean-free time, and one that corresponds to a fixed, optically large time step. We show that only the latter scaling results in a valid spatial discretization of the proper diffusion equation, and thus we conclude that IMC only yields accurate solutions when using optically large spatial cells if time steps are also optically large. We demonstrate the validity of our analysis with a set of numerical examples.

  13. Supplementary Material for: Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.

    2016-01-01

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  14. Repeated measures from FIA data facilitates analysis across spatial scales of tree growth responses to nitrogen deposition from individual trees to whole ecoregions

    Science.gov (United States)

    Charles H. (Hobie) Perry; Kevin J. Horn; R. Quinn Thomas; Linda H. Pardo; Erica A.H. Smithwick; Doug Baldwin; Gregory B. Lawrence; Scott W. Bailey; Sabine Braun; Christopher M. Clark; Mark Fenn; Annika Nordin; Jennifer N. Phelan; Paul G. Schaberg; Sam St. Clair; Richard Warby; Shaun Watmough; Steven S. Perakis

    2015-01-01

    The abundance of temporally and spatially consistent Forest Inventory and Analysis data facilitates hierarchical/multilevel analysis to investigate factors affecting tree growth, scaling from plot-level to continental scales. Herein we use FIA tree and soil inventories in conjunction with various spatial climate and soils data to estimate species-specific responses of...

  15. Identification of the key ecological factors influencing vegetation degradation in semi-arid agro-pastoral ecotone considering spatial scales

    Science.gov (United States)

    Peng, Yu; Wang, Qinghui; Fan, Min

    2017-11-01

    When assessing re-vegetation project performance and optimizing land management, identification of the key ecological factors inducing vegetation degradation has crucial implications. Rainfall, temperature, elevation, slope, aspect, land use type, and human disturbance are ecological factors affecting the status of vegetation index. However, at different spatial scales, the key factors may vary. Using Helin County, Inner-Mongolia, China as the study site and combining remote sensing image interpretation, field surveying, and mathematical methods, this study assesses key ecological factors affecting vegetation degradation under different spatial scales in a semi-arid agro-pastoral ecotone. It indicates that the key factors are different at various spatial scales. Elevation, rainfall, and temperature are identified as crucial for all spatial extents. Elevation, rainfall and human disturbance are key factors for small-scale quadrats of 300 m × 300 m and 600 m × 600 m, temperature and land use type are key factors for a medium-scale quadrat of 1 km × 1 km, and rainfall, temperature, and land use are key factors for large-scale quadrats of 2 km × 2 km and 5 km × 5 km. For this region, human disturbance is not the key factor for vegetation degradation across spatial scales. It is necessary to consider spatial scale for the identification of key factors determining vegetation characteristics. The eco-restoration programs at various spatial scales should identify key influencing factors according their scales so as to take effective measurements. The new understanding obtained in this study may help to explore the forces which driving vegetation degradation in the degraded regions in the world.

  16. A Spatial and Temporal analysis of Labour Market Characteristics

    Directory of Open Access Journals (Sweden)

    Pośpiech Ewa

    2016-12-01

    Full Text Available The use of spatial methods is becoming increasingly common in social and economic research as it emphasizes the relevance of spatiality to the understanding of socio-economic facts. Once embraced, the spatial factor can substantially help explain variations in the properties being examined, thus improving the quality of their description and supporting the development of econometric models. This paper explores some of the characteristics of Poland’s job market, making an inquiry into their spatial dependencies. The study looks at the country’s labour market from a local perspective, examining its properties for spatial autocorrelation (both global and local. Linear econometric models are subsequently built for such variables as the number of persons in employment, the number of women and men in employment. The models are further investigated to assess the applicability of spatial modelling in their development.

  17. UTOOLS: microcomputer software for spatial analysis and landscape visualization.

    Science.gov (United States)

    Alan A. Ager; Robert J. McGaughey

    1997-01-01

    UTOOLS is a collection of programs designed to integrate various spatial data in a way that allows versatile spatial analysis and visualization. The programs were designed for watershed-scale assessments in which a wide array of resource data must be integrated, analyzed, and interpreted. UTOOLS software combines raster, attribute, and vector data into "spatial...

  18. Problems in implementation of the spatial plan of the Republic of Srpska until 2015: Quantitative analysis

    Directory of Open Access Journals (Sweden)

    Bijelić Branislav

    2017-01-01

    Full Text Available The implementation of spatial plans in the Republic of Srpska is certainly the weakest phase of the process of spatial planning in this entity. It is particularly evident in the case of the Spatial Plan of the Republic of Srpska until 2015 which is the highest strategic spatial planning document in the Republic of Srpska. More precisely, the implementation of spatial plans has been defined as the carrying out of spatial planning documents, i.e. planning propositions as defined in the spatial plans. For the purpose of this paper, a quantitative analysis of the implementation of the planning propositions envisioned by this document has been carried out. The difference between what was planned and what was implemented at the end of the planning period (ex-post evaluation of planning decisions is presented in this paper. The weighting factor is defined for each thematic field and planning proposition, where the main criterion for determining the weighting factor is the share of the planning proposition and thematic field in the estimated total costs of the plan (financial criterion. The paper has also tackled the issue of the implementation of the Spatial Plan of Bosnia and Herzegovina for the period 1981 - 2000, as well as of the Spatial Plan of the Republic of Srpska 1996 - 2001 - Phased Plan for the period 1996 - 2001, as the previous strategic spatial planning documents of the highest rank covering the area of the Republic of Srpska. The research results have proven primary hypothesis of the paper that the level of the implementation of Spatial Plan of the Republic of Srpska until 2015 is less than 10%.

  19. A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels

    OpenAIRE

    Gonroos, M; Jacobsen, D; Hamada, N; Gothe, E; Enclada, A; Dudgeon, D; Dangles, O; Cottenie, K; Callisto, M; Brand, C; Bonada, N; Angeler, DG; Al-Shami, SA; Altermatt, F; Bini, LM

    2015-01-01

    The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second,...

  20. Geospatial analysis platform: Supporting strategic spatial analysis and planning

    CSIR Research Space (South Africa)

    Naude, A

    2008-11-01

    Full Text Available Whilst there have been rapid advances in satellite imagery and related fine resolution mapping and web-based interfaces (e.g. Google Earth), the development of capabilities for strategic spatial analysis and planning support has lagged behind...

  1. Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

    Directory of Open Access Journals (Sweden)

    S. Gaitan

    2015-06-01

    Full Text Available Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.

  2. Spatial analysis of falls in an urban community of Hong Kong

    Directory of Open Access Journals (Sweden)

    Wong Wing C

    2009-03-01

    Full Text Available Abstract Background Falls are an issue of great public health concern. This study focuses on outdoor falls within an urban community in Hong Kong. Urban environmental hazards are often place-specific and dependent upon the built features, landscape characteristics, and habitual activities. Therefore, falls must be examined with respect to local situations. Results This paper uses spatial analysis methods to map fall occurrences and examine possible environmental attributes of falls in an urban community of Hong Kong. The Nearest neighbour hierarchical (Nnh and Standard Deviational Ellipse (SDE techniques can offer additional insights about the circumstances and environmental factors that contribute to falls. The results affirm the multi-factorial nature of falls at specific locations and for selected groups of the population. Conclusion The techniques to detect hot spots of falls yield meaningful results that enable the identification of high risk locations. The combined use of descriptive and spatial analyses can be beneficial to policy makers because different preventive measures can be devised based on the types of environmental risk factors identified. The analyses are also important preludes to establishing research hypotheses for more focused studies.

  3. Spatial analysis of falls in an urban community of Hong Kong

    Science.gov (United States)

    Lai, Poh C; Low, Chien T; Wong, Martin; Wong, Wing C; Chan, Ming H

    2009-01-01

    Background Falls are an issue of great public health concern. This study focuses on outdoor falls within an urban community in Hong Kong. Urban environmental hazards are often place-specific and dependent upon the built features, landscape characteristics, and habitual activities. Therefore, falls must be examined with respect to local situations. Results This paper uses spatial analysis methods to map fall occurrences and examine possible environmental attributes of falls in an urban community of Hong Kong. The Nearest neighbour hierarchical (Nnh) and Standard Deviational Ellipse (SDE) techniques can offer additional insights about the circumstances and environmental factors that contribute to falls. The results affirm the multi-factorial nature of falls at specific locations and for selected groups of the population. Conclusion The techniques to detect hot spots of falls yield meaningful results that enable the identification of high risk locations. The combined use of descriptive and spatial analyses can be beneficial to policy makers because different preventive measures can be devised based on the types of environmental risk factors identified. The analyses are also important preludes to establishing research hypotheses for more focused studies. PMID:19291326

  4. The spatial-temporal evolution of aerosol optical depth and the analysis of influence factors in Bohai Rim

    International Nuclear Information System (INIS)

    Hou, Chunliang; Jiang, Hong; Wang, Xiaoyan; Pei, Huan

    2014-01-01

    Aerosol Optical Depth (AOD) is an important parameter of aerosol optical properties and it is an important physical parameter quantity to understanding the atmospheric environment. Bohai Rim is one of the three major urban agglomeration regions with rapidly developing economy in China. The study of AOD over this region is important to understand the environment and climate in Bohai Rim. Firstly, aerosol product data from 2000 to 2010, published by NASA, were used to analyze the temporal-spatial evolution of AOD in Bohai Rim with precision evaluation. The results showed that the spatial distribution of AOD had an obvious regional characteristic. The spatial distribution characterized that a much high value existed at urban areas and plain areas. On the contrary, the low value data existed in some mountainous regions which had higher percentages of forest coverage. The AOD values fluctuated somewhat each year in the region, from the minimum annual mean in 2003 to the maximum in 2009. Generally, the highest AOD value was in summer, followed by spring, autumn and winter. In terms of monthly variation, the value of AOD reached its peak in June and the lowest value was in December. This study analyzed the relation between AOD and some influence factors such as land use types, elevation, and distribution of urban agglomeration and so on. These results provide an important basic dataset for climate and environmental research

  5. Rings and sector : intrasite spatial analysis of stone age sites

    NARCIS (Netherlands)

    Stapert, Durk

    1992-01-01

    This thesis deals with intrasite spatial analysis: the analysis of spatial patterns on site level. My main concern has been to develop a simple method for analysing Stone Age sites of a special type: those characterised by the presence of a hearth closely associated in space with an artefact

  6. Stereological analysis of spatial structures

    DEFF Research Database (Denmark)

    Hansen, Linda Vadgård

    The thesis deals with stereological analysis of spatial structures. One area of focus has been to improve the precision of well-known stereological estimators by including information that is available via automatic image analysis. Furthermore, the thesis presents a stochastic model for star......-shaped three-dimensional objects using the radial function. It appears that the model is highly fleksiblel in the sense that it can be used to describe an object with arbitrary irregular surface. Results on the distribution of well-known local stereological volume estimators are provided....

  7. Spatial reliability analysis of a wind turbine blade cross section subjected to multi-axial extreme loading

    DEFF Research Database (Denmark)

    Dimitrov, Nikolay Krasimirov; Bitsche, Robert; Blasques, José Pedro Albergaria Amaral

    2017-01-01

    This paper presents a methodology for structural reliability analysis of wind turbine blades. The study introduces several novel elements by taking into account loading direction using a multiaxial probabilistic load model, considering random material strength, spatial correlation between material...... properties, progressive material failure, and system reliability effects. An example analysis of reliability against material failure is demonstrated for a blade cross section. Based on the study we discuss the implications of using a system reliability approach, the effect of spatial correlation length......, type of material degradation algorithm, and reliability methods on the system failure probability, as well as the main factors that have an influence on the reliability. (C) 2017 Elsevier Ltd. All rights reserved....

  8. Executive Order 12898 and Social, Economic, and Sociopolitical Factors Influencing Toxic Release Inventory Facility Location in EPA Region 6: A Multi-Scale Spatial Assessment of Environmental Justice

    Science.gov (United States)

    Moore, Andrea Lisa

    2013-01-01

    Toxic Release Inventory facilities are among the many environmental hazards shown to create environmental inequities in the United States. This project examined four factors associated with Toxic Release Inventory, specifically, manufacturing facility location at multiple spatial scales using spatial analysis techniques (i.e., O-ring statistic and…

  9. Estimation of spatial distribution of recharge factors at Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    Stothoff, S.A.; Bagtzoglou, A.C.; Castellaw, H.

    1995-01-01

    Determining compliance with the performance objectives for both the repository system and the Geologic Setting (GS) requires prediction of groundwater flow. Since infiltration is the primary source of water in the subsurface, the amounts and locations of infiltration are controlling factors in the movement of groundwater throughout the GS. In fractured, unsaturated rock, such as that found at the Yucca Mountain (YM) site, occurrence of infrequent, high-intensity rainfall events will modify, perhaps drastically, the subsurface flow regime from the one predicted by assuming that all rainfall events have averaged intensities. Indeed, the DOW has concluded that the spatial and temporal distribution of infiltration may be the most important factors influencing groundwater flow path development. Deep percolation fluxes are affected by processes active in the near-surface zone, including evaporation, transpiration, liquid water flow, and vapor flow. Each of these processes is governed by several factors. For example, precipitation has been found to vary substantially over the YM region, both spatially and temporally, and winter storms are, in general, more uniform and of longer duration than summer storms (Hevesi et al., 1992a,b; 1994). These observations indicate that, especially for summer storms, a spatially uniform precipitation pattern is clearly not applicable, even at the subregional scale. Similarly, evaporation from the ground surface is affected by air temperature, atmospheric vapor pressure, wind speed profile, incident solar radiation, surface soil and rock texture, plant activity, surficial temperature, and surficial moisture content. Many of these factors, such as surficial slope and orientation (related to incident solar radiation), surficial composition, and plant distributions, are or can be mapped at the YM site

  10. Spatial smoothing coherence factor for ultrasound computed tomography

    Science.gov (United States)

    Lou, Cuijuan; Xu, Mengling; Ding, Mingyue; Yuchi, Ming

    2016-04-01

    In recent years, many research studies have been carried out on ultrasound computed tomography (USCT) for its application prospect in early diagnosis of breast cancer. This paper applies four kinds of coherence-factor-like beamforming methods to improve the image quality of synthetic aperture focusing method for USCT, including the coherence-factor (CF), the phase coherence factor (PCF), the sign coherence factor (SCF) and the spatial smoothing coherence factor (SSCF) (proposed in our previous work). The performance of these methods was tested with simulated raw data which were generated by the ultrasound simulation software PZFlex 2014. The simulated phantom was set to be water of 4cm diameter with three nylon objects of different diameters inside. The ring-type transducer had 72 elements with a center frequency of 1MHz. The results show that all the methods can reveal the biggest nylon circle with the radius of 2.5mm. SSCF gets the highest SNR among the proposed methods and provides a more homogenous background. None of these methods can reveal the two smaller nylon circles with the radius of 0.75mm and 0.25mm. This may be due to the small number of elements.

  11. Research on the spatial analysis method of seismic hazard for island

    International Nuclear Information System (INIS)

    Jia, Jing; Jiang, Jitong; Zheng, Qiuhong; Gao, Huiying

    2017-01-01

    Seismic hazard analysis(SHA) is a key component of earthquake disaster prevention field for island engineering, whose result could provide parameters for seismic design microscopically and also is the requisite work for the island conservation planning’s earthquake and comprehensive disaster prevention planning macroscopically, in the exploitation and construction process of both inhabited and uninhabited islands. The existing seismic hazard analysis methods are compared in their application, and their application and limitation for island is analysed. Then a specialized spatial analysis method of seismic hazard for island (SAMSHI) is given to support the further related work of earthquake disaster prevention planning, based on spatial analysis tools in GIS and fuzzy comprehensive evaluation model. The basic spatial database of SAMSHI includes faults data, historical earthquake record data, geological data and Bouguer gravity anomalies data, which are the data sources for the 11 indices of the fuzzy comprehensive evaluation model, and these indices are calculated by the spatial analysis model constructed in ArcGIS’s Model Builder platform. (paper)

  12. Research on the spatial analysis method of seismic hazard for island

    Science.gov (United States)

    Jia, Jing; Jiang, Jitong; Zheng, Qiuhong; Gao, Huiying

    2017-05-01

    Seismic hazard analysis(SHA) is a key component of earthquake disaster prevention field for island engineering, whose result could provide parameters for seismic design microscopically and also is the requisite work for the island conservation planning’s earthquake and comprehensive disaster prevention planning macroscopically, in the exploitation and construction process of both inhabited and uninhabited islands. The existing seismic hazard analysis methods are compared in their application, and their application and limitation for island is analysed. Then a specialized spatial analysis method of seismic hazard for island (SAMSHI) is given to support the further related work of earthquake disaster prevention planning, based on spatial analysis tools in GIS and fuzzy comprehensive evaluation model. The basic spatial database of SAMSHI includes faults data, historical earthquake record data, geological data and Bouguer gravity anomalies data, which are the data sources for the 11 indices of the fuzzy comprehensive evaluation model, and these indices are calculated by the spatial analysis model constructed in ArcGIS’s Model Builder platform.

  13. LABOUR INPUT IN POLISH AGRICULTURE AGAINST SIZE OF AGRICULTURAL HOLDINGS – SPATIAL ANALYSIS*

    Directory of Open Access Journals (Sweden)

    Łukasz Wiśniewski

    2016-09-01

    Full Text Available The aim of this paper is to present the analysis of spatial diversifi cation in the labour input in agriculture on the basis of these selected indicators: annual work unit (AWU per 100 ha agricultural acreage; number of people per AWU; and comparison of labour input to EU-15 standards (LI-EU. Agricultural holdings have been categorised by size into three groups: 0–10 ha (small, 10–20 ha (average, and over 20 ha (large. Labour resources were subject to a comparative study of the density rates of AWUs per 100 ha agricultural acreage in Poland and the EU (EU-15. The analysis covered all of Poland with consideration of the administrative division into sixteen voivodeships (tabular analysis and 314 poviats (townships and country districts together, including regional offi ces of the Agency for Restructuring and Modernisation of Agriculture (ARiMR; cartographic analysis. The results of the Agricultural Census of 2010 were used in the study. It demonstrated a signifi cant spatial diversifi cation in labour input in agriculture in general and in each holding size group. Predominantly, the diff erences are related to historical and political factors. The analysis corroborated agrarian overpopulation in south-east Poland.

  14. Spatially explicit spectral analysis of point clouds and geospatial data

    Science.gov (United States)

    Buscombe, Daniel D.

    2015-01-01

    The increasing use of spatially explicit analyses of high-resolution spatially distributed data (imagery and point clouds) for the purposes of characterising spatial heterogeneity in geophysical phenomena necessitates the development of custom analytical and computational tools. In recent years, such analyses have become the basis of, for example, automated texture characterisation and segmentation, roughness and grain size calculation, and feature detection and classification, from a variety of data types. In this work, much use has been made of statistical descriptors of localised spatial variations in amplitude variance (roughness), however the horizontal scale (wavelength) and spacing of roughness elements is rarely considered. This is despite the fact that the ratio of characteristic vertical to horizontal scales is not constant and can yield important information about physical scaling relationships. Spectral analysis is a hitherto under-utilised but powerful means to acquire statistical information about relevant amplitude and wavelength scales, simultaneously and with computational efficiency. Further, quantifying spatially distributed data in the frequency domain lends itself to the development of stochastic models for probing the underlying mechanisms which govern the spatial distribution of geological and geophysical phenomena. The software packagePySESA (Python program for Spatially Explicit Spectral Analysis) has been developed for generic analyses of spatially distributed data in both the spatial and frequency domains. Developed predominantly in Python, it accesses libraries written in Cython and C++ for efficiency. It is open source and modular, therefore readily incorporated into, and combined with, other data analysis tools and frameworks with particular utility for supporting research in the fields of geomorphology, geophysics, hydrography, photogrammetry and remote sensing. The analytical and computational structure of the toolbox is

  15. Spatially explicit spectral analysis of point clouds and geospatial data

    Science.gov (United States)

    Buscombe, Daniel

    2016-01-01

    The increasing use of spatially explicit analyses of high-resolution spatially distributed data (imagery and point clouds) for the purposes of characterising spatial heterogeneity in geophysical phenomena necessitates the development of custom analytical and computational tools. In recent years, such analyses have become the basis of, for example, automated texture characterisation and segmentation, roughness and grain size calculation, and feature detection and classification, from a variety of data types. In this work, much use has been made of statistical descriptors of localised spatial variations in amplitude variance (roughness), however the horizontal scale (wavelength) and spacing of roughness elements is rarely considered. This is despite the fact that the ratio of characteristic vertical to horizontal scales is not constant and can yield important information about physical scaling relationships. Spectral analysis is a hitherto under-utilised but powerful means to acquire statistical information about relevant amplitude and wavelength scales, simultaneously and with computational efficiency. Further, quantifying spatially distributed data in the frequency domain lends itself to the development of stochastic models for probing the underlying mechanisms which govern the spatial distribution of geological and geophysical phenomena. The software package PySESA (Python program for Spatially Explicit Spectral Analysis) has been developed for generic analyses of spatially distributed data in both the spatial and frequency domains. Developed predominantly in Python, it accesses libraries written in Cython and C++ for efficiency. It is open source and modular, therefore readily incorporated into, and combined with, other data analysis tools and frameworks with particular utility for supporting research in the fields of geomorphology, geophysics, hydrography, photogrammetry and remote sensing. The analytical and computational structure of the toolbox is described

  16. Application of Fourier analysis to multispectral/spatial recognition

    Science.gov (United States)

    Hornung, R. J.; Smith, J. A.

    1973-01-01

    One approach for investigating spectral response from materials is to consider spatial features of the response. This might be accomplished by considering the Fourier spectrum of the spatial response. The Fourier Transform may be used in a one-dimensional to multidimensional analysis of more than one channel of data. The two-dimensional transform represents the Fraunhofer diffraction pattern of the image in optics and has certain invariant features. Physically the diffraction pattern contains spatial features which are possibly unique to a given configuration or classification type. Different sampling strategies may be used to either enhance geometrical differences or extract additional features.

  17. Spatial analysis of the etiology of amyotrophic lateral sclerosis among 1991 Gulf War veterans.

    Science.gov (United States)

    Miranda, Marie Lynn; Alicia Overstreet Galeano, M; Tassone, Eric; Allen, Kelli D; Horner, Ronnie D

    2008-11-01

    Veterans of the 1991 Gulf War have an increased risk of amyotrophic lateral sclerosis (ALS), but the etiology is unknown. This study sought to identify geographic areas with elevated risk for the later development of ALS among military personnel who served in the first Gulf War. A unified geographic information system (GIS) was constructed to allow analysis of secondary data on troop movements in the 1991 Gulf War theatre in the Persian Gulf region including Iraq, northern Saudi Arabia, and Kuwait. We fit Bayesian Poisson regression models to adjust for potential risk factors, including one relatively discrete environmental exposure, and to identify areas associated with elevated risk of ALS. We found that service in particular locations of the Gulf was associated with an elevated risk for later developing ALS, both before and after adjustment for branch of service and potential of exposure to chemical warfare agents in and around Khamisiyah, Iraq. Specific geographic locations of troop units within the 1991 Gulf War theatre are associated with an increased risk for the subsequent development of ALS among members of those units. The identified spatial locations represent the logical starting points in the search for potential etiologic factors of ALS among Gulf War veterans. Of note, for locations where the relative odds of subsequently developing ALS are among the highest, specific risk factors, whether environmental or occupationally related, have not been identified. The results of spatial models can be used to subsequently look for risk factors that follow the spatial pattern of elevated risk.

  18. Spatial modelling of malaria risk factors in Ruhuha sector in the east ...

    African Journals Online (AJOL)

    Spatial clusters of malaria occurrence were subsequently determined using Getis and Ord spatial statistics. This cluster analysis showed that malaria distribution is characterized by zones with high malaria risk, so called hot spots, zones with moderate malaria risk known as not significant spots and zones of low malaria risk ...

  19. Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search.

    Science.gov (United States)

    Mena, Carlos; Sepúlveda, Cesar; Fuentes, Eduardo; Ormazábal, Yony; Palomo, Iván

    2018-05-07

    Cardiovascular diseases (CVDs) are the primary cause of death and disability in de world, and the detection of populations at risk as well as localization of vulnerable areas is essential for adequate epidemiological management. Techniques developed for spatial analysis, among them geographical information systems and spatial statistics, such as cluster detection and spatial correlation, are useful for the study of the distribution of the CVDs. These techniques, enabling recognition of events at different geographical levels of study (e.g., rural, deprived neighbourhoods, etc.), make it possible to relate CVDs to factors present in the immediate environment. The systemic literature presented here shows that this group of diseases is clustered with regard to incidence, mortality and hospitalization as well as obesity, smoking, increased glycated haemoglobin levels, hypertension physical activity and age. In addition, acquired variables such as income, residency (rural or urban) and education, contribute to CVD clustering. Both local cluster detection and spatial regression techniques give statistical weight to the findings providing valuable information that can influence response mechanisms in the health services by indicating locations in need of intervention and assignment of available resources.

  20. European regional efficiency and geographical externalities: a spatial nonparametric frontier analysis

    Science.gov (United States)

    Ramajo, Julián; Cordero, José Manuel; Márquez, Miguel Ángel

    2017-10-01

    This paper analyses region-level technical efficiency in nine European countries over the 1995-2007 period. We propose the application of a nonparametric conditional frontier approach to account for the presence of heterogeneous conditions in the form of geographical externalities. Such environmental factors are beyond the control of regional authorities, but may affect the production function. Therefore, they need to be considered in the frontier estimation. Specifically, a spatial autoregressive term is included as an external conditioning factor in a robust order- m model. Thus we can test the hypothesis of non-separability (the external factor impacts both the input-output space and the distribution of efficiencies), demonstrating the existence of significant global interregional spillovers into the production process. Our findings show that geographical externalities affect both the frontier level and the probability of being more or less efficient. Specifically, the results support the fact that the spatial lag variable has an inverted U-shaped non-linear impact on the performance of regions. This finding can be interpreted as a differential effect of interregional spillovers depending on the size of the neighboring economies: positive externalities for small values, possibly related to agglomeration economies, and negative externalities for high values, indicating the possibility of production congestion. Additionally, evidence of the existence of a strong geographic pattern of European regional efficiency is reported and the levels of technical efficiency are acknowledged to have converged during the period under analysis.

  1. Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

    Science.gov (United States)

    Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D; Nichols, Thomas E

    2018-03-01

    Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. © 2017, The International Biometric Society.

  2. Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

    Science.gov (United States)

    Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.

    2017-01-01

    Summary Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. PMID:28498564

  3. Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis

    Directory of Open Access Journals (Sweden)

    Hone-Jay Chu

    2009-08-01

    Full Text Available The objectives of the study are to integrate the conditional Latin Hypercube Sampling (cLHS, sequential Gaussian simulation (SGS and spatial analysis in remotely sensed images, to monitor the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial heterogeneity and variability. The multiple NDVI images demonstrate that spatial patterns of disturbed landscapes were successfully delineated by spatial analysis such as variogram, Moran’I and landscape metrics in the study area. The hybrid method delineates the spatial patterns and spatial variability of landscapes caused by these large disturbances. The cLHS approach is applied to select samples from Normalized Difference Vegetation Index (NDVI images from SPOT HRV images in the Chenyulan watershed of Taiwan, and then SGS with sufficient samples is used to generate maps of NDVI images. In final, the NDVI simulated maps are verified using indexes such as the correlation coefficient and mean absolute error (MAE. Therefore, the statistics and spatial structures of multiple NDVI images present a very robust behavior, which advocates the use of the index for the quantification of the landscape spatial patterns and land cover change. In addition, the results transferred by Open Geospatial techniques can be accessed from web-based and end-user applications of the watershed management.

  4. Spatial Data Envelopment Analysis Method for the Evaluation of Regional Infrastructure Disparities

    Directory of Open Access Journals (Sweden)

    Birutė Galinienė

    2012-12-01

    Full Text Available Purpose—to achieve a more detailed assessment of regional differences, exploring regional infrastructure and human capital usage efficiency and to display analysis capabilities of spatial data efficient frontier method.Design/methodology/approach—the data envelopment analysis (DEA is applied to find the efficient frontier, which extends the application of production function of the regions. This method of mathematical programming optimization allows assessing the effectiveness of the regional spatial aspects presented. In recent studies this method is applied for evaluating the European Union regional policy issues.Findings—the application of DEA reveals its feasibility for regional input and output studies to evaluate more detailed and more reasonable fund allocation between Lithuanian regions. This analysis shows that in the comparatively efficient Lithuanian regions, such as Vilnius and Klaipėda, “the bottleneck” of usage of transport infrastructure and regional specific human capital is reached. It is stated that decision-making units could enhance region attractiveness for private investors by improving indirect factors in these regions. For practical significance of the study the results are compared with German regional analysis, conducted by Schaffer and other researchers (2011.Practical implications—the practical value of this work is based on giving more accurate planning tools for fund allocation decisions in Lithuanian regions while planning infrastructure and human capital development. The regional indicators were analyzed for 2010.Research type—case study.

  5. Sex differences in visual-spatial working memory: A meta-analysis.

    Science.gov (United States)

    Voyer, Daniel; Voyer, Susan D; Saint-Aubin, Jean

    2017-04-01

    Visual-spatial working memory measures are widely used in clinical and experimental settings. Furthermore, it has been argued that the male advantage in spatial abilities can be explained by a sex difference in visual-spatial working memory. Therefore, sex differences in visual-spatial working memory have important implication for research, theory, and practice, but they have yet to be quantified. The present meta-analysis quantified the magnitude of sex differences in visual-spatial working memory and examined variables that might moderate them. The analysis used a set of 180 effect sizes from healthy males and females drawn from 98 samples ranging in mean age from 3 to 86 years. Multilevel meta-analysis was used on the overall data set to account for non-independent effect sizes. The data also were analyzed in separate task subgroups by means of multilevel and mixed-effects models. Results showed a small but significant male advantage (mean d = 0.155, 95 % confidence interval = 0.087-0.223). All the tasks produced a male advantage, except for memory for location, where a female advantage emerged. Age of the participants was a significant moderator, indicating that sex differences in visual-spatial working memory appeared first in the 13-17 years age group. Removing memory for location tasks from the sample affected the pattern of significant moderators. The present results indicate a male advantage in visual-spatial working memory, although age and specific task modulate the magnitude and direction of the effects. Implications for clinical applications, cognitive model building, and experimental research are discussed.

  6. Unsupervised seismic facies analysis with spatial constraints using regularized fuzzy c-means

    Science.gov (United States)

    Song, Chengyun; Liu, Zhining; Cai, Hanpeng; Wang, Yaojun; Li, Xingming; Hu, Guangmin

    2017-12-01

    Seismic facies analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic facies analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear facies boundaries and channel morphology, which reveals that the method is an effective seismic facies analysis tool.

  7. Multi-spatial analysis of aeolian dune-field patterns

    Science.gov (United States)

    Ewing, Ryan C.; McDonald, George D.; Hayes, Alex G.

    2015-07-01

    Aeolian dune-fields are composed of different spatial scales of bedform patterns that respond to changes in environmental boundary conditions over a wide range of time scales. This study examines how variations in spatial scales of dune and ripple patterns found within dune fields are used in environmental reconstructions on Earth, Mars and Titan. Within a single bedform type, different spatial scales of bedforms emerge as a pattern evolves from an initial state into a well-organized pattern, such as with the transition from protodunes to dunes. Additionally, different types of bedforms, such as ripples, coarse-grained ripples and dunes, coexist at different spatial scales within a dune-field. Analysis of dune-field patterns at the intersection of different scales and types of bedforms at different stages of development provides a more comprehensive record of sediment supply and wind regime than analysis of a single scale and type of bedform. Interpretations of environmental conditions from any scale of bedform, however, are limited to environmental signals associated with the response time of that bedform. Large-scale dune-field patterns integrate signals over long-term climate cycles and reveal little about short-term variations in wind or sediment supply. Wind ripples respond instantly to changing conditions, but reveal little about longer-term variations in wind or sediment supply. Recognizing the response time scales across different spatial scales of bedforms maximizes environmental interpretations from dune-field patterns.

  8. Spatial environmental risk factors for pedestrian injury collisions in Ciudad Juárez, Mexico (2008-2009): implications for urban planning.

    Science.gov (United States)

    Fuentes, Cesar Mario; Hernandez, Vladimir

    2013-01-01

    The aim of this study is to examine the spatial distribution of pedestrian injury collisions and analyse the environmental (social and physical) risk factors in Ciudad Juarez, Mexico. More specifically, this study investigates the influence of land use, density, traffic and socio-economic characteristics. This cross sectional study is based on pedestrian injury collision data that were collected by the Municipal Transit Police during 2008-2009. This research presents an analysis of vehicle-pedestrian collisions and their spatial risk determinants using mixed methods that included (1) spatial/geographical information systems (GIS) analysis of pedestrian collision data and (2) ordinary least squares (OLS) regression analysis to explain the density of pedestrian collisions data. In our model, we found a higher probability for pedestrian collisions in census tracts with population and employment density, large concentration of commercial/retail land uses and older people (65 and more). Interventions to alleviate this situation including transportation planning such as decentralisation of municipal transport system, investment in road infrastructure - density of traffic lights, pedestrian crossing, road design, improves lane demarcation. Besides, land use planning interventions should be implemented in commercial/retail areas, in particular separating pedestrian and vehicular spaces.

  9. Determining the spatial variability of wetland soil bulk density, organic matter, and the conversion factor between organic matter and organic carbon across coastal Louisiana, U.S.A.

    Science.gov (United States)

    Wang, Hongqing; Piazza, Sarai C.; Sharp, Leigh A.; Stagg, Camille L.; Couvillion, Brady R.; Steyer, Gregory D.; McGinnis, Thomas E.

    2016-01-01

    Soil bulk density (BD), soil organic matter (SOM) content, and a conversion factor between SOM and soil organic carbon (SOC) are often used in estimating SOC sequestration and storage. Spatial variability in BD, SOM, and the SOM–SOC conversion factor affects the ability to accurately estimate SOC sequestration, storage, and the benefits (e.g., land building area and vertical accretion) associated with wetland restoration efforts, such as marsh creation and sediment diversions. There are, however, only a few studies that have examined large-scale spatial variability in BD, SOM, and SOM–SOC conversion factors in coastal wetlands. In this study, soil cores, distributed across the entire coastal Louisiana (approximately 14,667 km2) were used to examine the regional-scale spatial variability in BD, SOM, and the SOM–SOC conversion factor. Soil cores for BD and SOM analyses were collected during 2006–09 from 331 spatially well-distributed sites in the Coastwide Reference Monitoring System network. Soil cores for the SOM–SOC conversion factor analysis were collected from 15 sites across coastal Louisiana during 2006–07. Results of a split-plot analysis of variance with incomplete block design indicated that BD and SOM varied significantly at a landscape level, defined by both hydrologic basins and vegetation types. Vertically, BD and SOM varied significantly among different vegetation types. The SOM–SOC conversion factor also varied significantly at the landscape level. This study provides critical information for the assessment of the role of coastal wetlands in large regional carbon budgets and the estimation of carbon credits from coastal restoration.

  10. Global sensitivity analysis for models with spatially dependent outputs

    International Nuclear Information System (INIS)

    Iooss, B.; Marrel, A.; Jullien, M.; Laurent, B.

    2011-01-01

    The global sensitivity analysis of a complex numerical model often calls for the estimation of variance-based importance measures, named Sobol' indices. Meta-model-based techniques have been developed in order to replace the CPU time-expensive computer code with an inexpensive mathematical function, which predicts the computer code output. The common meta-model-based sensitivity analysis methods are well suited for computer codes with scalar outputs. However, in the environmental domain, as in many areas of application, the numerical model outputs are often spatial maps, which may also vary with time. In this paper, we introduce an innovative method to obtain a spatial map of Sobol' indices with a minimal number of numerical model computations. It is based upon the functional decomposition of the spatial output onto a wavelet basis and the meta-modeling of the wavelet coefficients by the Gaussian process. An analytical example is presented to clarify the various steps of our methodology. This technique is then applied to a real hydrogeological case: for each model input variable, a spatial map of Sobol' indices is thus obtained. (authors)

  11. The contribution of spatial analysis to understanding HIV/TB mortality in children: a structural equation modelling approach

    Directory of Open Access Journals (Sweden)

    Eustasius Musenge

    2013-01-01

    Full Text Available Background: South Africa accounts for more than a sixth of the global population of people infected with HIV and TB, ranking her highest in HIV/TB co-infection worldwide. Remote areas often bear the greatest burden of morbidity and mortality, yet there are spatial differences within rural settings. Objectives: The primary aim was to investigate HIV/TB mortality determinants and their spatial distribution in the rural Agincourt sub-district for children aged 1–5 years in 2004. Our secondary aim was to model how the associated factors were interrelated as either underlying or proximate factors of child mortality using pathway analysis based on a Mosley-Chen conceptual framework. Methods: We conducted a secondary data analysis based on cross-sectional data collected in 2004 from the Agincourt sub-district in rural northeast South Africa. Child HIV/TB death was the outcome measure derived from physician assessed verbal autopsy. Modelling used multiple logit regression models with and without spatial household random effects. Structural equation models were used in modelling the complex relationships between multiple exposures and the outcome (child HIV/TB mortality as relayed on a conceptual framework. Results: Fifty-four of 6,692 children aged 1–5 years died of HIV/TB, from a total of 5,084 households. Maternal death had the greatest effect on child HIV/TB mortality (adjusted odds ratio=4.00; 95% confidence interval=1.01–15.80. A protective effect was found in households with better socio-economic status and when the child was older. Spatial models disclosed that the areas which experienced the greatest child HIV/TB mortality were those without any health facility. Conclusion: Low socio-economic status and maternal deaths impacted indirectly and directly on child mortality, respectively. These factors are major concerns locally and should be used in formulating interventions to reduce child mortality. Spatial prediction maps can guide policy

  12. Spatial pattern analysis and demography of two tropical trees in the Brazilian Caatinga

    Directory of Open Access Journals (Sweden)

    Maira Fontes Manzan

    2014-12-01

    Full Text Available Aspidosperma pyrifolium (Apocynaceae and Caesalpinia pyramidalis (Fabaceae share the same habitat in the Brazilian Caatinga domain. In this paper, we investigate the intra and inter-species interactions between these two plants using spatial pattern analysis among cohorts. The results showed that the adult trees of each species present higher densities at distances shorter than 9 m to 12 m. However, due to seed dispersal via autochory, we expected a more aggregate density for C. pyramidalis than A. pyrifolium as the later disperses seeds through anemochory. Difference in spatial aggregation among cohorts was not observed and therefore the results contradict the expectations of the Janzen-Connell hypothesis. It is likely that this is associated with anthropogenic factors in the past such as fire, animal husbandry and logging. Using a bivariate analysis of the neighborhood density, we also confirmed the significant coexistence between the two species. This coexistence could be explained by the process of positive interspecific interactions, such as facilitation, which is common in semi-arid regions under stressful conditions.

  13. 3D spatially-adaptive canonical correlation analysis: Local and global methods.

    Science.gov (United States)

    Yang, Zhengshi; Zhuang, Xiaowei; Sreenivasan, Karthik; Mishra, Virendra; Curran, Tim; Byrd, Richard; Nandy, Rajesh; Cordes, Dietmar

    2018-04-01

    Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Spatial Assessment of Road Traffic Injuries in the Greater Toronto Area (GTA: Spatial Analysis Framework

    Directory of Open Access Journals (Sweden)

    Sina Tehranchi

    2017-03-01

    Full Text Available This research presents a Geographic Information Systems (GIS and spatial analysis approach based on the global spatial autocorrelation of road traffic injuries for identifying spatial patterns. A locational spatial autocorrelation was also used for identifying traffic injury at spatial level. Data for this research study were acquired from Canadian Institute for Health Information (CIHI based on 2004 and 2011. Moran’s I statistics were used to examine spatial patterns of road traffic injuries in the Greater Toronto Area (GTA. An assessment of Getis-Ord Gi* statistic was followed as to identify hot spots and cold spots within the study area. The results revealed that Peel and Durham have the highest collision rate for other motor vehicle with motor vehicle. Geographic weighted regression (GWR technique was conducted to test the relationships between the dependent variable, number of road traffic injury incidents and independent variables such as number of seniors, low education, unemployed, vulnerable groups, people smoking and drinking, urban density and average median income. The result of this model suggested that number of seniors and low education have a very strong correlation with the number of road traffic injury incidents.

  15. Spatial compression algorithm for the analysis of very large multivariate images

    Science.gov (United States)

    Keenan, Michael R [Albuquerque, NM

    2008-07-15

    A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

  16. Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model

    Directory of Open Access Journals (Sweden)

    Chengcheng Xu

    2017-08-01

    Full Text Available Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.

  17. Identifying Flood-Related Infectious Diseases in Anhui Province, China: A Spatial and Temporal Analysis

    Science.gov (United States)

    Gao, Lu; Zhang, Ying; Ding, Guoyong; Liu, Qiyong; Jiang, Baofa

    2016-01-01

    The aim of this study was to explore infectious diseases related to the 2007 Huai River flood in Anhui Province, China. The study was based on the notified incidences of infectious diseases between June 29 and July 25 from 2004 to 2011. Daily incidences of notified diseases in 2007 were compared with the corresponding daily incidences during the same period in the other years (from 2004 to 2011, except 2007) by Poisson regression analysis. Spatial autocorrelation analysis was used to test the distribution pattern of the diseases. Spatial regression models were then performed to examine the association between the incidence of each disease and flood, considering lag effects and other confounders. After controlling the other meteorological and socioeconomic factors, malaria (odds ratio [OR] = 3.67, 95% confidence interval [CI] = 1.77–7.61), diarrhea (OR = 2.16, 95% CI = 1.24–3.78), and hepatitis A virus (HAV) infection (OR = 6.11, 95% CI = 1.04–35.84) were significantly related to the 2007 Huai River flood both from the spatial and temporal analyses. Special attention should be given to develop public health preparation and interventions with a focus on malaria, diarrhea, and HAV infection, in the study region. PMID:26903612

  18. Components of Spatial Thinking: Evidence from a Spatial Thinking Ability Test

    Science.gov (United States)

    Lee, Jongwon; Bednarz, Robert

    2012-01-01

    This article introduces the development and validation of the spatial thinking ability test (STAT). The STAT consists of sixteen multiple-choice questions of eight types. The STAT was validated by administering it to a sample of 532 junior high, high school, and university students. Factor analysis using principal components extraction was applied…

  19. Exploration of Urban Spatial Planning Evaluation Based on Humanland Harmony

    Science.gov (United States)

    Hu, X. S.; Ma, Q. R.; Liang, W. Q.; Wang, C. X.; Xiong, X. Q.; Han, X. H.

    2017-09-01

    This study puts forward a new concept, "population urbanization level forecast - driving factor analysis - urban spatial planning analysis" for achieving efficient and intensive development of urbanization considering human-land harmony. We analyzed big data for national economic and social development, studied the development trends of population urbanization and its influencing factors using the grey system model in Chengmai county of Hainan province, China. In turn, we calculated the population of Chengmai coming years based on the forecasting urbanization rate and the corresponding amount of urban construction land, and evaluated the urban spatial planning with GIS spatial analysis method in the study area. The result shows that the proposed concept is feasible for evaluation of urban spatial planning, and is meaningful for guiding the rational distribution of urban space, controlling the scale of development, improving the quality of urbanization and thus promoting highly-efficient and intensive use of limited land resource.

  20. Landslide susceptibility assessment using Spatial Analysis and GIS modeling in Cluj-Napoca Metropolitan Area, Romania

    Directory of Open Access Journals (Sweden)

    Bogdan Eugen Dolean

    2017-06-01

    Full Text Available In Romania, landslides together with the multitude geomorphological processes linked to them are some of the most common hazards which manifested in vulnerable areas with important human activities can induce many negative effects. From this perspective, identifying the areas affected by landslides, based on GIS spatial analysis models and statistical methods, is a subject frequently discussed in the national and international literature. This research was focused on the methods and practices of GIS spatial analysis, with a target of creating a complex model and a viable methodology of assessment the probability of occurrence of landslides, applicable within any territory. The study was based on the identification and analysis in a bivariate systemic manner of the numerous factors involved in the production of landslides, such as topography, morphology, hydrography, geological, lithology, weather, land use. The area in which the analysis has been conducted, The Metropolitan Area of Cluj-Napoca, was chosen due to the exacerbated urbanization of the recent years, coupled with a massive increase in the number of inhabitants, thus being a space of socioeconomic importance and a real challenge regarding spatial planning. Applying the model in this area has generated relatively good results, with a power of predictability of over 80%, measured in landslides sample areas used for the validation of the results, fact which attest the viability of the model and the fact that the model can be used in different areas with related morphometric and environmental characteristics.

  1. Interactions between the spatial and temporal stimulus factors that influence multisensory integration in human performance.

    Science.gov (United States)

    Stevenson, Ryan A; Fister, Juliane Krueger; Barnett, Zachary P; Nidiffer, Aaron R; Wallace, Mark T

    2012-05-01

    In natural environments, human sensory systems work in a coordinated and integrated manner to perceive and respond to external events. Previous research has shown that the spatial and temporal relationships of sensory signals are paramount in determining how information is integrated across sensory modalities, but in ecologically plausible settings, these factors are not independent. In the current study, we provide a novel exploration of the impact on behavioral performance for systematic manipulations of the spatial location and temporal synchrony of a visual-auditory stimulus pair. Simple auditory and visual stimuli were presented across a range of spatial locations and stimulus onset asynchronies (SOAs), and participants performed both a spatial localization and simultaneity judgment task. Response times in localizing paired visual-auditory stimuli were slower in the periphery and at larger SOAs, but most importantly, an interaction was found between the two factors, in which the effect of SOA was greater in peripheral as opposed to central locations. Simultaneity judgments also revealed a novel interaction between space and time: individuals were more likely to judge stimuli as synchronous when occurring in the periphery at large SOAs. The results of this study provide novel insights into (a) how the speed of spatial localization of an audiovisual stimulus is affected by location and temporal coincidence and the interaction between these two factors and (b) how the location of a multisensory stimulus impacts judgments concerning the temporal relationship of the paired stimuli. These findings provide strong evidence for a complex interdependency between spatial location and temporal structure in determining the ultimate behavioral and perceptual outcome associated with a paired multisensory (i.e., visual-auditory) stimulus.

  2. Spatially explicit multi-criteria decision analysis for managing vector-borne diseases

    Science.gov (United States)

    2011-01-01

    The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA) is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector-borne disease management in particular

  3. Spatially explicit multi-criteria decision analysis for managing vector-borne diseases

    Directory of Open Access Journals (Sweden)

    Hongoh Valerie

    2011-12-01

    Full Text Available Abstract The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector

  4. Advanced spatial metrics analysis in cellular automata land use and cover change modeling

    International Nuclear Information System (INIS)

    Zamyatin, Alexander; Cabral, Pedro

    2011-01-01

    This paper proposes an approach for a more effective definition of cellular automata transition rules for landscape change modeling using an advanced spatial metrics analysis. This approach considers a four-stage methodology based on: (i) the search for the appropriate spatial metrics with minimal correlations; (ii) the selection of the appropriate neighborhood size; (iii) the selection of the appropriate technique for spatial metrics application; and (iv) the analysis of the contribution level of each spatial metric for joint use. The case study uses an initial set of 7 spatial metrics of which 4 are selected for modeling. Results show a better model performance when compared to modeling without any spatial metrics or with the initial set of 7 metrics.

  5. Analysis of spatial distribution of land cover maps accuracy

    Science.gov (United States)

    Khatami, R.; Mountrakis, G.; Stehman, S. V.

    2017-12-01

    Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain

  6. Spatial and Angular Moment Analysis of Continuous and Discretized Transport Problems

    International Nuclear Information System (INIS)

    Brantley, Patrick S.; Larsen, Edward W.

    2000-01-01

    A new theoretical tool for analyzing continuous and discretized transport equations is presented. This technique is based on a spatial and angular moment analysis of the analytic transport equation, which yields exact expressions for the 'center of mass' and 'squared radius of gyration' of the particle distribution. Essentially the same moment analysis is applied to discretized particle transport problems to determine numerical expressions for the center of mass and squared radius of gyration. Because this technique makes no assumption about the optical thickness of the spatial cells or about the amount of absorption in the system, it is applicable to problems that cannot be analyzed by a truncation analysis or an asymptotic diffusion limit analysis. The spatial differencing schemes examined (weighted- diamond, lumped linear discontinuous, and multiple balance) yield a numerically consistent expression for computing the squared radius of gyration plus an error term that depends on the mesh spacing, quadrature constants, and material properties of the system. The numerical results presented suggest that the relative accuracy of spatial differencing schemes for different types of problems can be assessed by comparing the magnitudes of these error terms

  7. Considering the spatial-scale factor when modelling sustainable land management.

    Science.gov (United States)

    Bouma, Johan

    2015-04-01

    Considering the spatial-scale factor when modelling sustainable land management. J.Bouma Em.prof. soil science, Wageningen University, Netherlands. Modelling soil-plant processes is a necessity when exploring future effects of climate change and innovative soil management on agricultural productivity. Soil data are needed to run models and traditional soil maps and the associated databases (based on various soil Taxonomies ), have widely been applied to provide such data obtained at "representative" points in the field. Pedotransferfunctions (PTF)are used to feed simulation models, statistically relating soil survey data ( obtained at a given point in the landscape) to physical parameters for simulation, thus providing a link with soil functionality. Soil science has a basic problem: their object of study is invisible. Only point data are obtained by augering or in pits. Only occasionally roadcuts provide a better view. Extrapolating point to area data is essential for all applications and presents a basic problem for soil science, because mapping units on soil maps, named for a given soil type,may also contain other soil types and quantitative information about the composition of soil map units is usually not available. For detailed work at farm level ( 1:5000-1:10000), an alternative procedure is proposed. Based on a geostatistical analysis, onsite soil observations are made in a grid pattern with spacings based on a geostatistical analysis. Multi-year simulations are made for each point of the functional properties that are relevant for the case being studied, such as the moisture supply capacity, nitrate leaching etc. under standardized boundary conditions to allow comparisons. Functional spatial units are derived next by aggregating functional point data. These units, which have successfully functioned as the basis for precision agriculture, do not necessarily correspond with Taxonomic units but when they do the Taxonomic names should be noted . At lower

  8. Risk factors for spatial memory impairment in patients with temporal lobe epilepsy.

    Science.gov (United States)

    Amlerova, Jana; Laczo, Jan; Vlcek, Kamil; Javurkova, Alena; Andel, Ross; Marusic, Petr

    2013-01-01

    At present, the risk factors for world-centered (allocentric) navigation impairment in patients with temporal lobe epilepsy (TLE) are not known. There is some evidence on the importance of the right hippocampus but other clinical features have not been investigated yet. In this study, we used an experimental human equivalent to the Morris water maze to examine spatial navigation performance in patients with drug-refractory unilateral TLE. We included 47 left-hemisphere speech dominant patients (25 right sided; 22 left sided). The aim of our study was to identify clinical and demographic characteristics of TLE patients who performed poorly in allocentric spatial memory tests. Our results demonstrate that poor spatial navigation is significantly associated with younger age at epilepsy onset, longer disease duration, and lower intelligence level. Allocentric navigation in TLE patients was impaired irrespective of epilepsy lateralization. Good and poor navigators did not differ in their age, gender, or preoperative/postoperative status. This study provides evidence on risk factors that increase the likelihood of allocentric navigation impairment in TLE patients. The results indicate that not only temporal lobe dysfunction itself but also low general cognitive abilities may contribute to the navigation impairment. Copyright © 2012 Elsevier Inc. All rights reserved.

  9. Procedural Factors That Affect Psychophysical Measures of Spatial Selectivity in Cochlear Implant Users

    Directory of Open Access Journals (Sweden)

    Stefano Cosentino

    2015-09-01

    Full Text Available Behavioral measures of spatial selectivity in cochlear implants are important both for guiding the programing of individual users’ implants and for the evaluation of different stimulation methods. However, the methods used are subject to a number of confounding factors that can contaminate estimates of spatial selectivity. These factors include off-site listening, charge interactions between masker and probe pulses in interleaved masking paradigms, and confusion effects in forward masking. We review the effects of these confounds and discuss methods for minimizing them. We describe one such method in which the level of a 125-pps masker is adjusted so as to mask a 125-pps probe, and where the masker and probe pulses are temporally interleaved. Five experiments describe the method and evaluate the potential roles of the different potential confounding factors. No evidence was obtained for off-site listening of the type observed in acoustic hearing. The choice of the masking paradigm was shown to alter the measured spatial selectivity. For short gaps between masker and probe pulses, both facilitation and refractory mechanisms had an effect on masking; this finding should inform the choice of stimulation rate in interleaved masking experiments. No evidence for confusion effects in forward masking was revealed. It is concluded that the proposed method avoids many potential confounds but that the choice of method should depend on the research question under investigation.

  10. Application of computer intensive data analysis methods to the analysis of digital images and spatial data

    DEFF Research Database (Denmark)

    Windfeld, Kristian

    1992-01-01

    Computer-intensive methods for data analysis in a traditional setting has developed rapidly in the last decade. The application of and adaption of some of these methods to the analysis of multivariate digital images and spatial data are explored, evaluated and compared to well established classical...... into the projection pursuit is presented. Examples from remote sensing are given. The ACE algorithm for computing non-linear transformations for maximizing correlation is extended and applied to obtain a non-linear transformation that maximizes autocorrelation or 'signal' in a multivariate image....... This is a generalization of the minimum /maximum autocorrelation factors (MAF's) which is a linear method. The non-linear method is compared to the linear method when analyzing a multivariate TM image from Greenland. The ACE method is shown to give a more detailed decomposition of the image than the MAF-transformation...

  11. Spatial recurrence analysis: A sensitive and fast detection tool in digital mammography

    International Nuclear Information System (INIS)

    Prado, T. L.; Galuzio, P. P.; Lopes, S. R.; Viana, R. L.

    2014-01-01

    Efficient diagnostics of breast cancer requires fast digital mammographic image processing. Many breast lesions, both benign and malignant, are barely visible to the untrained eye and requires accurate and reliable methods of image processing. We propose a new method of digital mammographic image analysis that meets both needs. It uses the concept of spatial recurrence as the basis of a spatial recurrence quantification analysis, which is the spatial extension of the well-known time recurrence analysis. The recurrence-based quantifiers are able to evidence breast lesions in a way as good as the best standard image processing methods available, but with a better control over the spurious fragments in the image

  12. Towards factor analysis exploration applied to positron emission tomography functional imaging for breast cancer characterization

    International Nuclear Information System (INIS)

    Rekik, W.; Ketata, I.; Sellami, L.; Ben slima, M.; Ben Hamida, A.; Chtourou, K.; Ruan, S.

    2011-01-01

    This paper aims to explore the factor analysis when applied to a dynamic sequence of medical images obtained using nuclear imaging modality, Positron Emission Tomography (PET). This latter modality allows obtaining information on physiological phenomena, through the examination of radiotracer evolution during time. Factor analysis of dynamic medical images sequence (FADMIS) estimates the underlying fundamental spatial distributions by factor images and the associated so-called fundamental functions (describing the signal variations) by factors. This method is based on an orthogonal analysis followed by an oblique analysis. The results of the FADMIS are physiological curves showing the evolution during time of radiotracer within homogeneous tissues distributions. This functional analysis of dynamic nuclear medical images is considered to be very efficient for cancer diagnostics. In fact, it could be applied for cancer characterization, vascularization as well as possible evaluation of response to therapy.

  13. Geographical Inequalities and Social and Environmental Risk Factors for Under-Five Mortality in Ghana in 2000 and 2010: Bayesian Spatial Analysis of Census Data.

    Science.gov (United States)

    Arku, Raphael E; Bennett, James E; Castro, Marcia C; Agyeman-Duah, Kofi; Mintah, Samilia E; Ware, James H; Nyarko, Philomena; Spengler, John D; Agyei-Mensah, Samuel; Ezzati, Majid

    2016-06-01

    Under-five mortality is declining in Ghana and many other countries. Very few studies have measured under-five mortality-and its social and environmental risk factors-at fine spatial resolutions, which is relevant for policy purposes. Our aim was to estimate under-five mortality and its social and environmental risk factors at the district level in Ghana. We used 10% random samples of Ghana's 2000 and 2010 National Population and Housing Censuses. We applied indirect demographic methods and a Bayesian spatial model to the information on total number of children ever born and children surviving to estimate under-five mortality (probability of dying by 5 y of age, 5q0) for each of Ghana's 110 districts. We also used the census data to estimate the distributions of households or persons in each district in terms of fuel used for cooking, sanitation facility, drinking water source, and parental education. Median district 5q0 declined from 99 deaths per 1,000 live births in 2000 to 70 in 2010. The decline ranged from 40% in southern districts, where it had been lower in 2000, exacerbating existing inequalities. Primary education increased in men and women, and more households had access to improved water and sanitation and cleaner cooking fuels. Higher use of liquefied petroleum gas for cooking was associated with lower 5q0 in multivariate analysis. Under-five mortality has declined in all of Ghana's districts, but the cross-district inequality in mortality has increased. There is a need for additional data, including on healthcare, and additional environmental and socioeconomic measurements, to understand the reasons for the variations in mortality levels and trends.

  14. Geographical Inequalities and Social and Environmental Risk Factors for Under-Five Mortality in Ghana in 2000 and 2010: Bayesian Spatial Analysis of Census Data.

    Directory of Open Access Journals (Sweden)

    Raphael E Arku

    2016-06-01

    Full Text Available Under-five mortality is declining in Ghana and many other countries. Very few studies have measured under-five mortality-and its social and environmental risk factors-at fine spatial resolutions, which is relevant for policy purposes. Our aim was to estimate under-five mortality and its social and environmental risk factors at the district level in Ghana.We used 10% random samples of Ghana's 2000 and 2010 National Population and Housing Censuses. We applied indirect demographic methods and a Bayesian spatial model to the information on total number of children ever born and children surviving to estimate under-five mortality (probability of dying by 5 y of age, 5q0 for each of Ghana's 110 districts. We also used the census data to estimate the distributions of households or persons in each district in terms of fuel used for cooking, sanitation facility, drinking water source, and parental education. Median district 5q0 declined from 99 deaths per 1,000 live births in 2000 to 70 in 2010. The decline ranged from 40% in southern districts, where it had been lower in 2000, exacerbating existing inequalities. Primary education increased in men and women, and more households had access to improved water and sanitation and cleaner cooking fuels. Higher use of liquefied petroleum gas for cooking was associated with lower 5q0 in multivariate analysis.Under-five mortality has declined in all of Ghana's districts, but the cross-district inequality in mortality has increased. There is a need for additional data, including on healthcare, and additional environmental and socioeconomic measurements, to understand the reasons for the variations in mortality levels and trends.

  15. Time dependent analysis of Xenon spatial oscillations in small power reactors

    International Nuclear Information System (INIS)

    Decco, Claudia Cristina Ghirardello

    1997-01-01

    This work presents time dependent analysis of xenon spatial oscillations studying the influence of the power density distribution, type of reactivity perturbation, power level and core size, using the one-dimensional and three-dimensional analysis with the MID2 and citation codes, respectively. It is concluded that small pressurized water reactors with height smaller than 1.5 m are stable and do not have xenon spatial oscillations. (author)

  16. Research on the Spatial Differentiation and Driving Factors of Tourism Enterprises’ Efficiency: Chinese Scenic Spots, Travel Agencies, and Hotels

    Directory of Open Access Journals (Sweden)

    Bing Xia

    2018-03-01

    Full Text Available Tourism is an important sustainable industry in the economy that optimizes the industrial structure. Thus, as a core part of this market, tourism enterprises perform a key role in the effective operation of this industry. This paper applies data envelopment analysis (DEA and Malmquist index (MI models to calculate the efficiency of Chinese tourism enterprises between 2005 and 2014. Results showed that: (1 The efficiency and the total factor productivity change index (TFPC of tourism enterprises remained low, and both have decreased. (2 The efficiency of regional tourism enterprises across China cloud be characterized as high in the east region, low in the central region, and high in both northeast and western regions. (3 The efficiency levels of the cities of Beijing and Shanghai were ahead of the country over the period of this study, while Chongqing, Tibet, Qinghai, and Ningxia all possess a number of obvious advantages in the western region. (4 Centers of overall tourism enterprise efficiency mainly moved in a southeast-to-northwest direction over the period of this research. (5 The spatial autocorrelation of tourism enterprise efficiencies is also assessed in this study, and the results show that the comprehensive efficiency (CE of tourism enterprises in southeastern coastal regions of China tended to a certain spatial agglomeration effect, while the correlation between the central region and northern China was not significant. (6 The Geodetector model is applied to analyze the key factors driving the spatial differentiation of tourism enterprise efficiencies, and the results show that the degree of opening to the outside world, potential human capital, and traffic conditions were the most important factors driving spatial differentiation in the efficiency of tourism enterprises.

  17. Meta-analysis of field scale spatial variability of grassland soil CO2 efflux: Interaction of biotic and abiotic drivers

    Czech Academy of Sciences Publication Activity Database

    Fóti, S.; Balogh, J.; Herbst, M.; Papp, M.; Koncz, P.; Bartha, S.; Zimmermann, Z.; Komoly, C.; Szabó, G.; Margóczi, G.; Acosta, Manuel; Nagy, Z.

    2016-01-01

    Roč. 143, aug (2016), s. 78-89 ISSN 0341-8162 R&D Projects: GA MŠk(CZ) LO1415 Institutional support: RVO:67179843 Keywords : Cross-variogram * Principal component analysis * Soil CO2 efflux * Spatial pattern * Variogram Subject RIV: EH - Ecology, Behaviour Impact factor: 3.191, year: 2016

  18. Spectro-spatial analysis of wave packet propagation in nonlinear acoustic metamaterials

    Science.gov (United States)

    Zhou, W. J.; Li, X. P.; Wang, Y. S.; Chen, W. Q.; Huang, G. L.

    2018-01-01

    The objective of this work is to analyze wave packet propagation in weakly nonlinear acoustic metamaterials and reveal the interior nonlinear wave mechanism through spectro-spatial analysis. The spectro-spatial analysis is based on full-scale transient analysis of the finite system, by which dispersion curves are generated from the transmitted waves and also verified by the perturbation method (the L-P method). We found that the spectro-spatial analysis can provide detailed information about the solitary wave in short-wavelength region which cannot be captured by the L-P method. It is also found that the optical wave modes in the nonlinear metamaterial are sensitive to the parameters of the nonlinear constitutive relation. Specifically, a significant frequency shift phenomenon is found in the middle-wavelength region of the optical wave branch, which makes this frequency region behave like a band gap for transient waves. This special frequency shift is then used to design a direction-biased waveguide device, and its efficiency is shown by numerical simulations.

  19. Spatial distribution of, and risk factors for, Opisthorchis viverrini infection in southern Lao PDR.

    Directory of Open Access Journals (Sweden)

    Armelle Forrer

    Full Text Available BACKGROUND: Opisthorchis viverrini is a food-borne trematode species that might give rise to biliary diseases and the fatal cholangiocarcinoma. In Lao PDR, an estimated 2.5 million individuals are infected with O. viverrini, but epidemiological studies are scarce and the spatial distribution of infection remains to be determined. Our aim was to map the distribution of O. viverrini in southern Lao PDR, identify underlying risk factors, and predict the prevalence of O. viverrini at non-surveyed locations. METHODOLOGY: A cross-sectional parasitological and questionnaire survey was carried out in 51 villages in Champasack province in the first half of 2007. Data on demography, socioeconomic status, water supply, sanitation, and behavior were combined with remotely sensed environmental data and fed into a geographical information system. Bayesian geostatistical models were employed to identify risk factors and to investigate the spatial pattern of O. viverrini infection. Bayesian kriging was utilized to predict infection risk at non-surveyed locations. PRINCIPAL FINDINGS: The prevalence of O. viverrini among 3,371 study participants with complete data records was 61.1%. Geostatistical models identified age, Lao Loum ethnic group, educational attainment, occupation (i.e., rice farmer, fisherman, and animal breeder, and unsafe drinking water source as risk factors for infection. History of praziquantel treatment, access to sanitation, and distance to freshwater bodies were found to be protective factors. Spatial patterns of O. viverrini were mainly governed by environmental factors with predictive modeling identifying two different risk profiles: low risk of O. viverrini in the mountains and high risk in the Mekong corridor. CONCLUSIONS/SIGNIFICANCE: We present the first risk map of O. viverrini infection in Champasack province, which is important for spatial targeting of control efforts. Infection with O. viverrini appears to be strongly associated

  20. Assessment of tuberculosis spatial hotspot areas in Antananarivo, Madagascar, by combining spatial analysis and genotyping.

    Science.gov (United States)

    Ratovonirina, Noël Harijaona; Rakotosamimanana, Niaina; Razafimahatratra, Solohery Lalaina; Raherison, Mamy Serge; Refrégier, Guislaine; Sola, Christophe; Rakotomanana, Fanjasoa; Rasolofo Razanamparany, Voahangy

    2017-08-14

    Tuberculosis (TB) remains a public health problem in Madagascar. A crucial element of TB control is the development of an easy and rapid method for the orientation of TB control strategies in the country. Our main objective was to develop a TB spatial hotspot identification method by combining spatial analysis and TB genotyping method in Antananarivo. Sputa of new pulmonary TB cases from 20 TB diagnosis and treatment centers (DTCs) in Antananarivo were collected from August 2013 to May 2014 for culture. Mycobacterium tuberculosis complex (MTBC) clinical isolates were typed by spoligotyping on a Luminex® 200 platform. All TB patients were respectively localized according to their neighborhood residence and the spatial distribution of all pulmonary TB patients and patients with genotypic clustered isolates were scanned respectively by the Kulldorff spatial scanning method for identification of significant spatial clustering. Areas exhibiting spatial clustering of patients with genotypic clustered isolates were considered as hotspot TB areas for transmission. Overall, 467 new cases were included in the study, and 394 spoligotypes were obtained (84.4%). New TB cases were distributed in 133 of the 192 Fokontany (administrative neighborhoods) of Antananarivo (1 to 15 clinical patients per Fokontany) and patients with genotypic clustered isolates were distributed in 127 of the 192 Fokontany (1 to 13 per Fokontany). A single spatial focal point of epidemics was detected when ignoring genotypic data (p = 0.039). One Fokontany of this focal point and three additional ones were detected to be spatially clustered when taking genotypes into account (p Madagascar and will allow better TB control strategies by public health authorities.

  1. Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis.

    Science.gov (United States)

    Westerholt, Rene; Steiger, Enrico; Resch, Bernd; Zipf, Alexander

    2016-01-01

    Twitter and related social media feeds have become valuable data sources to many fields of research. Numerous researchers have thereby used social media posts for spatial analysis, since many of them contain explicit geographic locations. However, despite its widespread use within applied research, a thorough understanding of the underlying spatial characteristics of these data is still lacking. In this paper, we investigate how topological outliers influence the outcomes of spatial analyses of social media data. These outliers appear when different users contribute heterogeneous information about different phenomena simultaneously from similar locations. As a consequence, various messages representing different spatial phenomena are captured closely to each other, and are at risk to be falsely related in a spatial analysis. Our results reveal indications for corresponding spurious effects when analyzing Twitter data. Further, we show how the outliers distort the range of outcomes of spatial analysis methods. This has significant influence on the power of spatial inferential techniques, and, more generally, on the validity and interpretability of spatial analysis results. We further investigate how the issues caused by topological outliers are composed in detail. We unveil that multiple disturbing effects are acting simultaneously and that these are related to the geographic scales of the involved overlapping patterns. Our results show that at some scale configurations, the disturbances added through overlap are more severe than at others. Further, their behavior turns into a volatile and almost chaotic fluctuation when the scales of the involved patterns become too different. Overall, our results highlight the critical importance of thoroughly considering the specific characteristics of social media data when analyzing them spatially.

  2. SPATIAL ANALYSIS AND DECISION ASSISTANCE (SADA) TRAINING COURSE

    Science.gov (United States)

    Spatial Analysis and Decision Assistance (SADA) is a Windows freeware program that incorporates tools from environmental assessment into an effective problem-solving environment. SADA was developed by the Institute for Environmental Modeling at the University of Tennessee and inc...

  3. Fast Transient And Spatially Non-Homogenous Accident Analysis Of Two-Dimensional Cylindrical Nuclear Reactor

    International Nuclear Information System (INIS)

    Yulianti, Yanti; Su'ud, Zaki; Waris, Abdul; Khotimah, S. N.; Shafii, M. Ali

    2010-01-01

    The research about fast transient and spatially non-homogenous nuclear reactor accident analysis of two-dimensional nuclear reactor has been done. This research is about prediction of reactor behavior is during accident. In the present study, space-time diffusion equation is solved by using direct methods which consider spatial factor in detail during nuclear reactor accident simulation. Set of equations that obtained from full implicit finite-difference discretization method is solved by using iterative methods ADI (Alternating Direct Implicit). The indication of accident is decreasing macroscopic absorption cross-section that results large external reactivity. The power reactor has a peak value before reactor has new balance condition. Changing of temperature reactor produce a negative Doppler feedback reactivity. The reactivity will reduce excess positive reactivity. Temperature reactor during accident is still in below fuel melting point which is in secure condition.

  4. Spatial data analysis for exploration of regional scale geothermal resources

    Science.gov (United States)

    Moghaddam, Majid Kiavarz; Noorollahi, Younes; Samadzadegan, Farhad; Sharifi, Mohammad Ali; Itoi, Ryuichi

    2013-10-01

    Defining a comprehensive conceptual model of the resources sought is one of the most important steps in geothermal potential mapping. In this study, Fry analysis as a spatial distribution method and 5% well existence, distance distribution, weights of evidence (WofE), and evidential belief function (EBFs) methods as spatial association methods were applied comparatively to known geothermal occurrences, and to publicly-available regional-scale geoscience data in Akita and Iwate provinces within the Tohoku volcanic arc, in northern Japan. Fry analysis and rose diagrams revealed similar directional patterns of geothermal wells and volcanoes, NNW-, NNE-, NE-trending faults, hotsprings and fumaroles. Among the spatial association methods, WofE defined a conceptual model correspondent with the real world situations, approved with the aid of expert opinion. The results of the spatial association analyses quantitatively indicated that the known geothermal occurrences are strongly spatially-associated with geological features such as volcanoes, craters, NNW-, NNE-, NE-direction faults and geochemical features such as hotsprings, hydrothermal alteration zones and fumaroles. Geophysical data contains temperature gradients over 100 °C/km and heat flow over 100 mW/m2. In general, geochemical and geophysical data were better evidence layers than geological data for exploring geothermal resources. The spatial analyses of the case study area suggested that quantitative knowledge from hydrothermal geothermal resources was significantly useful for further exploration and for geothermal potential mapping in the case study region. The results can also be extended to the regions with nearly similar characteristics.

  5. Exploring the Spatial-Seasonal Dynamics of Water Quality, Submerged Aquatic Plants and Their Influencing Factors in Different Areas of a Lake

    Directory of Open Access Journals (Sweden)

    Kun Li

    2017-09-01

    Full Text Available The degradation of water quality in lakes and its negative effects on freshwater ecosystems have become a serious problem worldwide. Exploring the dynamics in the associated factors is essential for water pollution management and control. GIS interpolation, principal component analysis (PCA and multivariate statistical techniques were used to identify the main pollution sources in different areas of Honghu Lake. The results indicate that the spatial distribution of the concentrations of total nitrogen (TN, total phosphate (TP, ammonia nitrogen (NH4+–N, and permanganate index (CODMn have similar characteristics and that their values gradually increased from south to north during the three seasons in Honghu Lake. The major influencing factors of water quality varied across the different areas and seasons. The relatively high concentrations of TN and TP, which might limit the growth of submerged aquatic plants, were mainly caused by anthropogenic factors. Our work suggests that spatial analyses combined with PCA are useful for investigating the factors that influence water quality and submerged aquatic plant biomass in different areas of a lake. These findings provide sound information for the future water quality management of the lake or even the entire lake basin.

  6. Spatial correlation length of normalized cone data in sand

    DEFF Research Database (Denmark)

    Firouzianbandpey, Sarah; Griffiths, D. V.; Ibsen, Lars Bo

    2014-01-01

    The main topic of this study is to assess the anisotropic spatial correlation lengths of a sand layer deposit based on cone penetration testing with pore pressure measurement (CPTu) data. Spatial correlation length can be an important factor in reliability analysis of geotechnical systems, yet it...

  7. Statistical analysis of long term spatial and temporal trends of ...

    Indian Academy of Sciences (India)

    Statistical analysis of long term spatial and temporal trends of temperature ... CGCM3; HadCM3; modified Mann–Kendall test; statistical analysis; Sutlej basin. ... Water Resources Systems Division, National Institute of Hydrology, Roorkee 247 ...

  8. Image Chunking: Defining Spatial Building Blocks for Scene Analysis.

    Science.gov (United States)

    1987-04-01

    mumgs0.USmusa 7.AUWOJO 4. CIUTAC Rm6ANT Wuugme*j James V/. Mlahoney DACA? 6-85-C-00 10 NOQ 1 4-85-K-O 124 Artificial Inteligence Laboratory US USS 545...0197 672 IMAGE CHUWING: DEINING SPATIAL UILDING PLOCKS FOR 142 SCENE ANRLYSIS(U) MASSACHUSETTS INST OF TECH CAIIAIDGE ARTIFICIAL INTELLIGENCE LAO J...Technical Report 980 F-Image Chunking: Defining Spatial Building Blocks for Scene DTm -Analysis S ELECTED James V. Mahoney’ MIT Artificial Intelligence

  9. Spatial analysis methods and land-use planning models for rural areas

    Directory of Open Access Journals (Sweden)

    Patrizia Tassinari

    2009-10-01

    Full Text Available The work presents a brief report of the main results of a study carried out by the Spatial Engineering Division of the Department of Agricultural Economics and Engineering of the University of Bologna, within a broader PRIN 2005 research project concerning landscape and economic analysis, planning and programming. In particular, the study focuses on the design of spatial analysis methods aimed at building knowledge frameworks of the various natural and anthropic resources of rural areas. The goal is to increase the level of spatial and information detail of common databases, thus allowing higher accuracy and effectiveness of the analyses needed to achieve the goals of new generation spatial and agriculture planning. Specific in-depth analyses allowed to define techniques useful in order to reduce the increase in survey costs. Moreover, the work reports the main results regarding a multicriteria model for the analysis of the countryside defined by the research. Such model is aimed to assess the various agricultural, environmental and landscape features, vocations, expressions and attitudes, and support the definition and implementation of specific and targeted planning and programming policies.

  10. Rock avalanche occurrence in the San Juan province (Argentina): an analysis of their spatial distribution and main forcing factors

    Science.gov (United States)

    Penna, Ivanna; Tonini, Marj; Vega Orozco, Carmen D.; Longchamp, Céline; Derron, Marc-Henri; Jaboyedoff, Michel

    2013-04-01

    Rock avalanches are frequent phenomena in the Argentinean Andes and a particular high concentration of these events is observed in the Northwest (~25°S) and in the Central Andes from 30°S until the transition with the Patagonian Andes (~38°S). Tectonic deformation and seismicity are generally proposed as main driving factors, with weather and lithologic conditions playing a subordinate role. From 28°S to 33°S, the subhorizontal subduction of the Nazca plate drives higher shortening rates than in the surrounding areas, and an intense seismicity. Main morphotectonic units in this regions are the Cordillera and Precordillera, separated by the Barreal-Calingasta depression. In the southern central part of the flat subduction area (30°30'°-32°30'S), it is observed high valley incision and maximum local relief of 2900 m, while in the Precordillera main fluvial courses developed in the inter-thrust valleys, where local relief is up to 2400 m. In both mountain ranges, we recognized 34 rock avalanches deposits with volumes up to 0.3 km3. There is no apparent lithologic control on detachments, which involved sedimentary, volcanic and granite rocks, even though ~20% of them were favored by layering orientation. However, about 50% of the inventoried rock avalanches with the greatest volumes, developed along tectonic structures or less than 1 km far from them. The main objective of the present study is to explore the spatial distribution of rock avalanche deposits, and compare it with the instrumental seismicity and landscape conditions by means of statistical tools (e.g. exploratory data analyses, Ripley's K-function). Those analyses allow to highlight the spatial correlation between the geological events. Moreover, to visually display the detected cluster spatial patterns we elaborated kernel density maps. Our findings revealed that most of the rock avalanches show a high spatial aggregation mainly between 31°20'S-31°50'S. Main concentration of bedrock landslides

  11. A digital elevation analysis: Spatially distributed flow apportioning algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sang-Hyun; Kim, Kyung-Hyun [Pusan National University, Pusan(Korea); Jung, Sun-Hee [Korea Environment Institute, (Korea)

    2001-06-30

    A flow determination algorithm is proposed for the distributed hydrologic model. The advantages of a single flow direction scheme and multiple flow direction schemes are selectively considered to address the drawbacks of existing algorithms. A spatially varied flow apportioning factor is introduced in order to accommodate the accumulated area from upslope cells. The channel initiation threshold area(CIT) concept is expanded and integrated into the spatially distributed flow apportioning algorithm in order to delineate a realistic channel network. An application of a field example suggests that the linearly distributed flow apportioning scheme provides some advantages over existing approaches, such as the relaxation of over-dissipation problems near channel cells, the connectivity feature of river cells, the continuity of saturated areas and the negligence of the optimization of few parameters in existing algorithms. The effects of grid sizes are explored spatially as well as statistically. (author). 28 refs., 7 figs.

  12. Modeling Spatial Patterns of Soil Respiration in Maize Fields from Vegetation and Soil Property Factors with the Use of Remote Sensing and Geographical Information System

    Science.gov (United States)

    Huang, Ni; Wang, Li; Guo, Yiqiang; Hao, Pengyu; Niu, Zheng

    2014-01-01

    To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China. PMID:25157827

  13. Applying spatial regression to evaluate risk factors for microbiological contamination of urban groundwater sources in Juba, South Sudan

    Science.gov (United States)

    Engström, Emma; Mörtberg, Ulla; Karlström, Anders; Mangold, Mikael

    2017-06-01

    This study developed methodology for statistically assessing groundwater contamination mechanisms. It focused on microbial water pollution in low-income regions. Risk factors for faecal contamination of groundwater-fed drinking-water sources were evaluated in a case study in Juba, South Sudan. The study was based on counts of thermotolerant coliforms in water samples from 129 sources, collected by the humanitarian aid organisation Médecins Sans Frontières in 2010. The factors included hydrogeological settings, land use and socio-economic characteristics. The results showed that the residuals of a conventional probit regression model had a significant positive spatial autocorrelation (Moran's I = 3.05, I-stat = 9.28); therefore, a spatial model was developed that had better goodness-of-fit to the observations. The most significant factor in this model ( p-value 0.005) was the distance from a water source to the nearest Tukul area, an area with informal settlements that lack sanitation services. It is thus recommended that future remediation and monitoring efforts in the city be concentrated in such low-income regions. The spatial model differed from the conventional approach: in contrast with the latter case, lowland topography was not significant at the 5% level, as the p-value was 0.074 in the spatial model and 0.040 in the traditional model. This study showed that statistical risk-factor assessments of groundwater contamination need to consider spatial interactions when the water sources are located close to each other. Future studies might further investigate the cut-off distance that reflects spatial autocorrelation. Particularly, these results advise research on urban groundwater quality.

  14. [Spatial pattern of forest biomass and its influencing factors in the Great Xing'an Mountains, Heilongjiang Province, China].

    Science.gov (United States)

    Wang, Xiao-Li; Chang, Yu; Chen, Hong-Wei; Hu, Yuan-Man; Jiao, Lin-Lin; Feng, Yu-Ting; Wu, Wen; Wu, Hai-Feng

    2014-04-01

    Based on field inventory data and vegetation index EVI (enhanced vegetation index), the spatial pattern of the forest biomass in the Great Xing'an Mountains, Heilongjiang Province was quantitatively analyzed. Using the spatial analysis and statistics tools in ArcGIS software, the impacts of climatic zone, elevation, slope, aspect and vegetation type on the spatial pattern of forest biomass were explored. The results showed that the forest biomass in the Great Xing'an Mountains was 350 Tg and spatially aggregated with great increasing potentials. Forest biomass density in the cold temperate humid zone (64.02 t x hm(-2)) was higher than that in the temperate humid zone (60.26 t x hm(-2)). The biomass density of each vegetation type was in the order of mixed coniferous forest (65.13 t x hm(-2)) > spruce-fir forest (63.92 t x hm(-2)) > Pinus pumila-Larix gmelinii forest (63.79 t x hm(-2)) > Pinus sylvestris var. mongolica forest (61.97 t x hm(-2)) > Larix gmelinii forest (61.40 t x hm(-2)) > deciduous broadleaf forest (58.96 t x hm(-2)). With the increasing elevation and slope, the forest biomass density first decreased and then increased. The forest biomass density in the shady slopes was greater than that in the sunny slopes. The spatial pattern of forest biomass in the Great Xing' an Mountains exhibited a heterogeneous pattern due to the variation of climatic zone, vegetation type and topographical factor. This spatial heterogeneity needs to be accounted when evaluating forest biomass at regional scales.

  15. Capacity analysis of spectrum sharing spatial multiplexing MIMO systems

    KAUST Repository

    Yang, Liang

    2014-12-01

    This paper considers a spectrum sharing (SS) multiple-input multiple-output (MIMO) system operating in a Rayleigh fading environment. First the capacity of a single-user SS spatial multiplexing system is investigated in two scenarios that assume different receivers. To explicitly show the capacity scaling law of SS MIMO systems, some approximate capacity expressions for the two scenarios are derived. Next, we extend our analysis to a multiple user system with zero-forcing receivers (ZF) under spatially-independent scheduling and analyze the sum-rate. Furthermore, we provide an asymptotic sum-rate analysis to investigate the effects of different parameters on the multiuser diversity gain. Our results show that the secondary system with a smaller number of transmit antennas Nt and a larger number of receive antennas Nr can achieve higher capacity at lower interference temperature Q, but at high Q the capacity follows the scaling law of the conventional MIMO systems. However, for a ZF SS spatial multiplexing system, the secondary system with small Nt and large Nr can achieve the highest capacity throughout the entire region of Q. For a ZF SS spatial multiplexing system with scheduling, the asymptotic sum-rate scales like Ntlog2(Q(KNtNp-1)/Nt), where Np denotes the number of antennas of the primary receiver and K represents the number of secondary transmitters.

  16. A GIS-based disaggregate spatial watershed analysis using RADAR data

    International Nuclear Information System (INIS)

    Al-Hamdan, M.

    2002-01-01

    Hydrology is the study of water in all its forms, origins, and destinations on the earth.This paper develops a novel modeling technique using a geographic information system (GIS) to facilitate watershed hydrological routing using RADAR data. The RADAR rainfall data, segmented to 4 km by 4 km blocks, divides the watershed into several sub basins which are modeled independently. A case study for the GIS-based disaggregate spatial watershed analysis using RADAR data is provided for South Fork Cowikee Creek near Batesville, Alabama. All the data necessary to complete the analysis is maintained in the ArcView GIS software. This paper concludes that the GIS-Based disaggregate spatial watershed analysis using RADAR data is a viable method to calculate hydrological routing for large watersheds. (author)

  17. Foundations of factor analysis

    CERN Document Server

    Mulaik, Stanley A

    2009-01-01

    Introduction Factor Analysis and Structural Theories Brief History of Factor Analysis as a Linear Model Example of Factor AnalysisMathematical Foundations for Factor Analysis Introduction Scalar AlgebraVectorsMatrix AlgebraDeterminants Treatment of Variables as Vectors Maxima and Minima of FunctionsComposite Variables and Linear Transformations Introduction Composite Variables Unweighted Composite VariablesDifferentially Weighted Composites Matrix EquationsMulti

  18. Nationwide analysis on the impact of socioeconomic land use factors and incidence of urothelial carcinoma.

    Science.gov (United States)

    Brandt, Maximilian P; Gust, Kilian M; Mani, Jens; Vallo, Stefan; Höfner, Thomas; Borgmann, Hendrik; Tsaur, Igor; Thomas, Christian; Haferkamp, Axel; Herrmann, Eva; Bartsch, Georg

    2018-02-01

    Incidence rates for urothelial carcinoma (UC) have been reported to differ between countries within the European Union (EU). Besides occupational exposure to chemicals, other substances such as tobacco and nitrite in groundwater have been identified as risk factors for UC. We investigated if regional differences in UC incidence rates are associated with agricultural, industrial and residential land use. Newly diagnosed cases of UC between 2003 and 2010 were included. Information within 364 administrative districts of Germany from 2004 for land use factors were obtained and calculated as a proportion of the total area of the respective administrative district and as a smoothed proportion. Furthermore, information on smoking habits was included in our analysis. Kulldorff spatial clustering was used to detect different clusters. A negative binomial model was used to test the spatial association between UC incidence as a ratio of observed versus expected incidence rates, land use and smoking habits. We identified 437,847,834 person years with 171,086 cases of UC. Cluster analysis revealed areas with higher incidence of UC than others (p=0.0002). Multivariate analysis including significant pairwise interactions showed that the environmental factors were independently associated with UC (psocioeconomic factors based on agricultural, industrial and residential land use may be associated with UC incidence rates. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Spatially variable stage-driven groundwater-surface water interaction inferred from time-frequency analysis of distributed temperature sensing data

    Science.gov (United States)

    Mwakanyamale, Kisa; Slater, Lee; Day-Lewis, Frederick D.; Elwaseif, Mehrez; Johnson, Carole D.

    2012-01-01

    Characterization of groundwater-surface water exchange is essential for improving understanding of contaminant transport between aquifers and rivers. Fiber-optic distributed temperature sensing (FODTS) provides rich spatiotemporal datasets for quantitative and qualitative analysis of groundwater-surface water exchange. We demonstrate how time-frequency analysis of FODTS and synchronous river stage time series from the Columbia River adjacent to the Hanford 300-Area, Richland, Washington, provides spatial information on the strength of stage-driven exchange of uranium contaminated groundwater in response to subsurface heterogeneity. Although used in previous studies, the stage-temperature correlation coefficient proved an unreliable indicator of the stage-driven forcing on groundwater discharge in the presence of other factors influencing river water temperature. In contrast, S-transform analysis of the stage and FODTS data definitively identifies the spatial distribution of discharge zones and provided information on the dominant forcing periods (≥2 d) of the complex dam operations driving stage fluctuations and hence groundwater-surface water exchange at the 300-Area.

  20. Analysis Of Influence Of Spatial Planning On Performance Of Regional Development At Waropen District. Papua Indonesia

    Directory of Open Access Journals (Sweden)

    Suwandi

    2015-08-01

    Full Text Available The various problems in regional spatial planning in Waropen District Papua shows that the Spatial Planning RTRW of Waropen District Papua drafted in 2010 has not had a positive contribution to the settlement of spatial planning problems. This is most likely caused by the inconsistency in the spatial planning. This study tried to observe the consistency of spatial planning as well as its relation to the regional development performance. The method used to observe the consistency of the preparation of guided Spatial Planning RTRW is the analysis of comparative table followed by analysis of verbal logic. In order to determine if the preparation of Spatial Planning RTRW has already paid attention on the synergy with the surrounding regions Inter-Regional Context a map overlay was conducted followed by analysis of verbal logic. To determine the performance of the regional development a Principal Components Analysis PCA was done. The analysis results showed that inconsistencies in the spatial planning had caused a variety of problems that resulted in decreased performance of the regional development. The main problems that should receive more attention are infrastructure development growth economic growth transportation aspect and new properties.

  1. Experimental study on the crack detection with optimized spatial wavelet analysis and windowing

    Science.gov (United States)

    Ghanbari Mardasi, Amir; Wu, Nan; Wu, Christine

    2018-05-01

    In this paper, a high sensitive crack detection is experimentally realized and presented on a beam under certain deflection by optimizing spatial wavelet analysis. Due to the crack existence in the beam structure, a perturbation/slop singularity is induced in the deflection profile. Spatial wavelet transformation works as a magnifier to amplify the small perturbation signal at the crack location to detect and localize the damage. The profile of a deflected aluminum cantilever beam is obtained for both intact and cracked beams by a high resolution laser profile sensor. Gabor wavelet transformation is applied on the subtraction of intact and cracked data sets. To improve detection sensitivity, scale factor in spatial wavelet transformation and the transformation repeat times are optimized. Furthermore, to detect the possible crack close to the measurement boundaries, wavelet transformation edge effect, which induces large values of wavelet coefficient around the measurement boundaries, is efficiently reduced by introducing different windowing functions. The result shows that a small crack with depth of less than 10% of the beam height can be localized with a clear perturbation. Moreover, the perturbation caused by a crack at 0.85 mm away from one end of the measurement range, which is covered by wavelet transform edge effect, emerges by applying proper window functions.

  2. Spatial data fusion and analysis for soil characterization: a case study in a coastal basin of south-western Sicily (southern Italy

    Directory of Open Access Journals (Sweden)

    Donato Sollitto

    2012-03-01

    Full Text Available Salinization is one of the most serious problems confronting sustainable agriculture in semi-arid and arid regions. Accurate mapping of soil salinization and the associated risk represent a fundamental step in planning agricultural and remediation activities. Geostatistical analysis is very useful for soil quality assessment because it makes it possible to determine the spatial relationships between selected variables and to produce synthetic maps of spatial variation. The main objective of this paper was to map the soil salinization risk in the Delia-Nivolelli alluvial basin (south-western Sicily, southern Italy, using multivariate geostatistical techniques and a set of topographical, physical and soil hydraulic properties. Elevation data were collected from existing topographic maps and analysed preliminarily to improve the estimate precision of sparsely sampled primary variables. For interpolation multi-collocated cokriging was applied to the dataset, including textural and hydraulic properties and electrical conductivity measurements carried out on 128 collected soil samples, using elevation data as auxiliary variable. Spatial dependence among elevation and physical soil properties was explored with factorial kriging analysis (FKA that could isolate and display the sources of variation acting at different spatial scales. FKA isolated significant regionalised factors which give a concise description of the complex soil physical variability at the different selected spatial scales. These factors mapped, allowed the delineation of zones at different salinisation risk to be managed separately to control and prevent salinization risk. The proposed methodology could be a valid support for land use and soil remediation planning at regional scale.

  3. Spatial Analysis of Accident Spots Using Weighted Severity Index ...

    African Journals Online (AJOL)

    ADOWIE PERE

    Spatial Analysis of Accident Spots Using Weighted Severity Index (WSI) and ... pedestrians avoiding the use of pedestrian bridges/aid even when they are available. ..... not minding an unforeseen obstruction, miscalculations and wrong break.

  4. A book review of Spatial data analysis in ecology and agriculture using R

    Science.gov (United States)

    Spatial Data Analysis in Ecology and Agriculture Using R is a valuable resource to assist agricultural and ecological researchers with spatial data analyses using the R statistical software(www.r-project.org). Special emphasis is on spatial data sets; how-ever, the text also provides ample guidance ...

  5. Spatial-temporal data model and fractal analysis of transportation network in GIS environment

    Science.gov (United States)

    Feng, Yongjiu; Tong, Xiaohua; Li, Yangdong

    2008-10-01

    How to organize transportation data characterized by multi-time, multi-scale, multi-resolution and multi-source is one of the fundamental problems of GIS-T development. A spatial-temporal data model for GIS-T is proposed based on Spatial-temporal- Object Model. Transportation network data is systemically managed using dynamic segmentation technologies. And then a spatial-temporal database is built to integrally store geographical data of multi-time for transportation. Based on the spatial-temporal database, functions of spatial analysis of GIS-T are substantively extended. Fractal module is developed to improve the analyzing in intensity, density, structure and connectivity of transportation network based on the validation and evaluation of topologic relation. Integrated fractal with GIS-T strengthens the functions of spatial analysis and enriches the approaches of data mining and knowledge discovery of transportation network. Finally, the feasibility of the model and methods are tested thorough Guangdong Geographical Information Platform for Highway Project.

  6. N2O emission hotspots at different spatial scales and governing factors for small scale hotspots

    International Nuclear Information System (INIS)

    Heuvel, R.N. van den; Hefting, M.M.; Tan, N.C.G.; Jetten, M.S.M.; Verhoeven, J.T.A.

    2009-01-01

    Chronically nitrate-loaded riparian buffer zones show high N 2 O emissions. Often, a large part of the N 2 O is emitted from small surface areas, resulting in high spatial variability in these buffer zones. These small surface areas with high N 2 O emissions (hotspots) need to be investigated to generate knowledge on the factors governing N 2 O emissions. In this study the N 2 O emission variability was investigated at different spatial scales. Therefore N 2 O emissions from three 32 m 2 grids were determined in summer and winter. Spatial variation and total emission were determined on three different scales (0.3 m 2 , 0.018 m 2 and 0.0013 m 2 ) at plots with different levels of N 2 O emissions. Spatial variation was high at all scales determined and highest at the smallest scale. To test possible factors inducing small scale hotspots, soil samples were collected for slurry incubation to determine responses to increased electron donor/acceptor availability. Acetate addition did increase N 2 O production, but nitrate addition failed to increase total denitrification or net N 2 O production. N 2 O production was similar in all soil slurries, independent of their origin from high or low emission soils, indicating that environmental conditions (including physical factors like gas diffusion) rather than microbial community composition governed N 2 O emission rates

  7. Comparative analysis of time efficiency and spatial resolution between different EIT reconstruction algorithms

    International Nuclear Information System (INIS)

    Kacarska, Marija; Loskovska, Suzana

    2002-01-01

    In this paper comparative analysis between different EIT algorithms is presented. Analysis is made for spatial and temporal resolution of obtained images by several different algorithms. Discussions consider spatial resolution dependent on data acquisition method, too. Obtained results show that conventional applied-current EIT is more powerful compared to induced-current EIT. (Author)

  8. Functional resilience of microbial ecosystems in soil: How important is a spatial analysis?

    Science.gov (United States)

    König, Sara; Banitz, Thomas; Centler, Florian; Frank, Karin; Thullner, Martin

    2015-04-01

    Microbial life in soil is exposed to fluctuating environmental conditions influencing the performance of microbially mediated ecosystem services such as biodegradation of contaminants. However, as this environment is typically very heterogeneous, spatial aspects can be expected to play a major role for the ability to recover from a stress event. To determine key processes for functional resilience, simple scenarios with varying stress intensities were simulated within a microbial simulation model and the biodegradation rate in the recovery phase monitored. Parameters including microbial growth and dispersal rates were varied over a typical range to consider microorganisms with varying properties. Besides an aggregated temporal monitoring, the explicit observation of the spatio-temporal dynamics proved essential to understand the recovery process. For a mechanistic understanding of the model system, scenarios were also simulated with selected processes being switched-off. Results of the mechanistic and the spatial view show that the key factors for functional recovery with respect to biodegradation after a simple stress event depend on the location of the observed habitats. The limiting factors near unstressed areas are spatial processes - the mobility of the bacteria as well as substrate diffusion - the longer the distance to the unstressed region the more important becomes the process growth. Furthermore, recovery depends on the stress intensity - after a low stress event the spatial configuration has no influence on the key factors for functional resilience. To confirm these results, we repeated the stress scenarios but this time including an additional dispersal network representing a fungal network in soil. The system benefits from an increased spatial performance due to the higher mobility of the degrading microorganisms. However, this effect appears only in scenarios where the spatial distribution of the stressed area plays a role. With these simulations we

  9. Environmental and spatial controls of palm (Arecaceae) species richness across the Americas

    DEFF Research Database (Denmark)

    Bjorholm, Stine; Svenning, Jens-Christian; Skov, Flemming

    2005-01-01

    Our analysis suggests that in the Americas, palm species richness at spatial scales from 1° to 10° is most strongly controlled by water availability, although unknown broad-scale factors, perhaps soil, historical processes or geometric constraints, are also important.......Our analysis suggests that in the Americas, palm species richness at spatial scales from 1° to 10° is most strongly controlled by water availability, although unknown broad-scale factors, perhaps soil, historical processes or geometric constraints, are also important....

  10. Deterministic factor analysis: methods of integro-differentiation of non-integral order

    Directory of Open Access Journals (Sweden)

    Valentina V. Tarasova

    2016-12-01

    Full Text Available Objective to summarize the methods of deterministic factor economic analysis namely the differential calculus and the integral method. nbsp Methods mathematical methods for integrodifferentiation of nonintegral order the theory of derivatives and integrals of fractional nonintegral order. Results the basic concepts are formulated and the new methods are developed that take into account the memory and nonlocality effects in the quantitative description of the influence of individual factors on the change in the effective economic indicator. Two methods are proposed for integrodifferentiation of nonintegral order for the deterministic factor analysis of economic processes with memory and nonlocality. It is shown that the method of integrodifferentiation of nonintegral order can give more accurate results compared with standard methods method of differentiation using the first order derivatives and the integral method using the integration of the first order for a wide class of functions describing effective economic indicators. Scientific novelty the new methods of deterministic factor analysis are proposed the method of differential calculus of nonintegral order and the integral method of nonintegral order. Practical significance the basic concepts and formulas of the article can be used in scientific and analytical activity for factor analysis of economic processes. The proposed method for integrodifferentiation of nonintegral order extends the capabilities of the determined factorial economic analysis. The new quantitative method of deterministic factor analysis may become the beginning of quantitative studies of economic agents behavior with memory hereditarity and spatial nonlocality. The proposed methods of deterministic factor analysis can be used in the study of economic processes which follow the exponential law in which the indicators endogenous variables are power functions of the factors exogenous variables including the processes

  11. Quantitative analysis of spatial variability of geotechnical parameters

    Science.gov (United States)

    Fang, Xing

    2018-04-01

    Geotechnical parameters are the basic parameters of geotechnical engineering design, while the geotechnical parameters have strong regional characteristics. At the same time, the spatial variability of geotechnical parameters has been recognized. It is gradually introduced into the reliability analysis of geotechnical engineering. Based on the statistical theory of geostatistical spatial information, the spatial variability of geotechnical parameters is quantitatively analyzed. At the same time, the evaluation of geotechnical parameters and the correlation coefficient between geotechnical parameters are calculated. A residential district of Tianjin Survey Institute was selected as the research object. There are 68 boreholes in this area and 9 layers of mechanical stratification. The parameters are water content, natural gravity, void ratio, liquid limit, plasticity index, liquidity index, compressibility coefficient, compressive modulus, internal friction angle, cohesion and SP index. According to the principle of statistical correlation, the correlation coefficient of geotechnical parameters is calculated. According to the correlation coefficient, the law of geotechnical parameters is obtained.

  12. Backyard housing in Gauteng: An analysis of spatial dynamics

    African Journals Online (AJOL)

    Backyard housing in Gauteng: An analysis of spatial dynamics. Yasmin Shapurjee ... Drawing on quantitative geo-demographic data from GeoTerraImage (GTI). (2010), Knowledge .... a fundamental role in absorbing demand for low-income ...

  13. Sea level rise and the geoid: factor analysis approach

    Directory of Open Access Journals (Sweden)

    Alexey Sadovski

    2013-08-01

    Full Text Available Sea levels are rising around the world, and this is a particular concern along most of the coasts of the United States. A 1989 EPA report shows that sea levels rose 5-6 inches more than the global average along the Mid-Atlantic and Gulf Coasts in the last century. The main reason for this is coastal land subsidence. This sea level rise is considered more as relative sea level rise than global sea level rise. Thus, instead of studying sea level rise globally, this paper describes a statistical approach by using factor analysis of regional sea level rates of change. Unlike physical models and semi-empirical models that attempt to approach how much and how fast sea levels are changing, this methodology allows for a discussion of the factor(s that statistically affects sea level rates of change, and seeks patterns to explain spatial correlations.

  14. Spatial synchrony in cisco recruitment

    Science.gov (United States)

    Myers, Jared T.; Yule, Daniel L.; Jones, Michael L.; Ahrenstorff, Tyler D.; Hrabik, Thomas R.; Claramunt, Randall M.; Ebener, Mark P.; Berglund, Eric K.

    2015-01-01

    We examined the spatial scale of recruitment variability for disparate cisco (Coregonus artedi) populations in the Great Lakes (n = 8) and Minnesota inland lakes (n = 4). We found that the scale of synchrony was approximately 400 km when all available data were utilized; much greater than the 50-km scale suggested for freshwater fish populations in an earlier global analysis. The presence of recruitment synchrony between Great Lakes and inland lake cisco populations supports the hypothesis that synchronicity is driven by climate and not dispersal. We also found synchrony in larval densities among three Lake Superior populations separated by 25–275 km, which further supports the hypothesis that broad-scale climatic factors are the cause of spatial synchrony. Among several candidate climate variables measured during the period of larval cisco emergence, maximum wind speeds exhibited the most similar spatial scale of synchrony to that observed for cisco. Other factors, such as average water temperatures, exhibited synchrony on broader spatial scales, which suggests they could also be contributing to recruitment synchrony. Our results provide evidence that abiotic factors can induce synchronous patterns of recruitment for populations of cisco inhabiting waters across a broad geographic range, and show that broad-scale synchrony of recruitment can occur in freshwater fish populations as well as those from marine systems.

  15. Fine scale spatial variability of microbial pesticide degradation in soil: scales, controlling factors, and implications

    Directory of Open Access Journals (Sweden)

    Arnaud eDechesne

    2014-12-01

    Full Text Available Pesticide biodegradation is a soil microbial function of critical importance for modern agriculture and its environmental impact. While it was once assumed that this activity was homogeneously distributed at the field scale, mounting evidence indicates that this is rarely the case. Here, we critically examine the literature on spatial variability of pesticide biodegradation in agricultural soil. We discuss the motivations, methods, and main findings of the primary literature. We found significant diversity in the approaches used to describe and quantify spatial heterogeneity, which complicates inter-studies comparisons. However, it is clear that the presence and activity of pesticide degraders is often highly spatially variable with coefficients of variation often exceeding 50% and frequently displays nonrandom spatial patterns. A few controlling factors have tentatively been identified across pesticide classes: they include some soil characteristics (pH and some agricultural management practices (pesticide application, tillage, while other potential controlling factors have more conflicting effects depending on the site or the pesticide. Evidence demonstrating the importance of spatial heterogeneity on the fate of pesticides in soil has been difficult to obtain but modelling and experimental systems that do not include soil’s full complexity reveal that this heterogeneity must be considered to improve prediction of pesticide biodegradation rates or of leaching risks. Overall, studying the spatial heterogeneity of pesticide biodegradation is a relatively new field at the interface of agronomy, microbial ecology, and geosciences and a wealth of novel data is being collected from these different disciplinary perspectives. We make suggestions on possible avenues to take full advantage of these investigations for a better understanding and prediction of the fate of pesticides in soil.

  16. Analysis of Regional Unemployment in Russia and Germany: Spatial-Econometric Approach

    Directory of Open Access Journals (Sweden)

    Elena Vyacheslavovna Semerikova

    2015-06-01

    Full Text Available The study was supported by the Government of the Russian Federation, grant No.11.G34.31.0059. This paper analyzes the regional unemployment in Russia and Germany in 2005-2010 and addresses issues of choosing the right specification of spatial-econometric models. The analysis based on data of 75 Russian and 370 German regions showed that for Germany the choice of the spatial weighting matrix has a more significant influence on the parameter estimates than for Russia. Presumably this is due to stronger linkages between regional labor markets in Germany compared to Russia. The authors also proposed an algorithm for choosing between spatial matrices and demonstrated the application of this algorithm on simulated Russian data. The authors found that 1 the deviation of the results from the true ones increases when the spatial dependence between regions is higher and 2 the matrix of inverse distances is more preferable than the boundary one for the analysis of regional unemployment in Russia (because of the lower value of the mean squared error. The authors are also planning to apply the proposed algorithm for simulated data of Germany. These results allow accounting the spatial dependence more correctly when modeling regional unemployment which is very important for making proper regional policy

  17. Extreme Precipitation Estimation with Typhoon Morakot Using Frequency and Spatial Analysis

    Directory of Open Access Journals (Sweden)

    Hone-Jay Chu

    2011-01-01

    Full Text Available Typhoon Morakot lashed Taiwan and produced copious amounts of precipitation in 2009. From the point view of hydrological statistics, the impact of the precipitation from typhoon Morakot using a frequency analysis can be analyzed and discussed. The frequency curve, which was fitted mathematically to historical observed data, can be used to estimate the probability of exceedance for runoff events of a certain magnitude. The study integrates frequency analysis and spatial analysis to assess the effect of Typhoon Morakot event on rainfall frequency in the Gaoping River basin of southern Taiwan. First, extreme rainfall data are collected at sixteen stations for durations of 1, 3, 6, 12, and 24 hours and then an appropriate probability distribution was selected to analyze the impact of the extreme hydrological event. Spatial rainfall patterns for a return period of 200-yr with 24-hr duration with and without Typhoon Morakot are estimated. Results show that the rainfall amount is significantly different with long duration with and without the event for frequency analysis. Furthermore, spatial analysis shows that extreme rainfall for a return period of 200-yr is highly dependent on topography and is smaller in the southwest than that in the east. The results not only demonstrate the distinct effect of Typhoon Morakot on frequency analysis, but also could provide reference in future planning of hydrological engineering.

  18. Remote sensing and spatial analysis based study for detecting deforestation and the associated drivers

    Science.gov (United States)

    El-Abbas, Mustafa M.; Csaplovics, Elmar; Deafalla, Taisser H.

    2013-10-01

    Nowadays, remote-sensing technologies are becoming increasingly interlinked to the issue of deforestation. They offer a systematized and objective strategy to document, understand and simulate the deforestation process and its associated causes. In this context, the main goal of this study, conducted in the Blue Nile region of Sudan, in which most of the natural habitats were dramatically destroyed, was to develop spatial methodologies to assess the deforestation dynamics and its associated factors. To achieve that, optical multispectral satellite scenes (i.e., ASTER and LANDSAT) integrated with field survey in addition to multiple data sources were used for the analyses. Spatiotemporal Object Based Image Analysis (STOBIA) was applied to assess the change dynamics within the period of study. Broadly, the above mentioned analyses include; Object Based (OB) classifications, post-classification change detection, data fusion, information extraction and spatial analysis. Hierarchical multi-scale segmentation thresholds were applied and each class was delimited with semantic meanings by a set of rules associated with membership functions. Consequently, the fused multi-temporal data were introduced to create detailed objects of change classes from the input LU/LC classes. The dynamic changes were quantified and spatially located as well as the spatial and contextual relations from adjacent areas were analyzed. The main finding of the present study is that, the forest areas were drastically decreased, while the agrarian structure in conversion of forest into agricultural fields and grassland was the main force of deforestation. In contrast, the capability of the area to recover was clearly observed. The study concludes with a brief assessment of an 'oriented' framework, focused on the alarming areas where serious dynamics are located and where urgent plans and interventions are most critical, guided with potential solutions based on the identified driving forces.

  19. Seasonal and Spatial Variability of Anthropogenic and Natural Factors Influencing Groundwater Quality Based on Source Apportionment

    Directory of Open Access Journals (Sweden)

    Xueru Guo

    2018-02-01

    Full Text Available Globally, groundwater resources are being deteriorated by rapid social development. Thus, there is an urgent need to assess the combined impacts of natural and enhanced anthropogenic sources on groundwater chemistry. The aim of this study was to identify seasonal characteristics and spatial variations in anthropogenic and natural effects, to improve the understanding of major hydrogeochemical processes based on source apportionment. 34 groundwater points located in a riverside groundwater resource area in northeast China were sampled during the wet and dry seasons in 2015. Using principal component analysis and factor analysis, 4 principal components (PCs were extracted from 16 groundwater parameters. Three of the PCs were water-rock interaction (PC1, geogenic Fe and Mn (PC2, and agricultural pollution (PC3. A remarkable difference (PC4 was organic pollution originating from negative anthropogenic effects during the wet season, and geogenic F enrichment during the dry season. Groundwater exploitation resulted in dramatic depression cone with higher hydraulic gradient around the water source area. It not only intensified dissolution of calcite, dolomite, gypsum, Fe, Mn and fluorine minerals, but also induced more surface water recharge for the water source area. The spatial distribution of the PCs also suggested the center of the study area was extremely vulnerable to contamination by Fe, Mn, COD, and F−.

  20. SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis.

    Science.gov (United States)

    Shi, Yuhu; Zeng, Weiming; Wang, Nizhuan

    2017-09-01

    With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages. In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively. The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains. These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. [A spatially explicit analysis of traffic accidents involving pedestrians and cyclists in Berlin].

    Science.gov (United States)

    Lakes, Tobia

    2017-12-01

    In many German cities and counties, sustainable mobility concepts that strengthen pedestrian and cyclist traffic are promoted. From the perspectives of urban development, traffic planning and public healthcare, a spatially differentiated analysis of traffic accident data is decisive. 1) The identification of spatial and temporal patterns of the distribution of accidents involving cyclists and pedestrians, 2) the identification of hotspots and exploration of possible underlying causes and 3) the critical discussion of benefits and challenges of the results and the derivation of conclusions. Spatio-temporal distributions of data from accident statistics in Berlin involving pedestrians and cyclists from 2011 to 2015 were analysed with geographic information systems (GIS). While the total number of accidents remains relatively stable for pedestrian and cyclist accidents, the spatial distribution analysis shows, however, that there are significant spatial clusters (hotspots) of traffic accidents with a strong concentration in the inner city area. In a critical discussion, the benefits of geographic concepts are identified, such as spatially explicit health data (in this case traffic accident data), the importance of the integration of other data sources for the evaluation of the health impact of areas (traffic accident statistics of the police), and the possibilities and limitations of spatial-temporal data analysis (spatial point-density analyses) for the derivation of decision-supported recommendations and for the evaluation of policy measures of health prevention and of health-relevant urban development.

  2. Geo-Nested Analysis: Mixed-Methods Research with Spatially Dependent Data

    NARCIS (Netherlands)

    Harbers, I.; Ingram, M.C.

    Mixed-methods designs, especially those where cases selected for small-N analysis (SNA) are nested within a large-N analysis (LNA), have become increasingly popular. Yet, since the LNA in this approach assumes that units are independently distributed, such designs are unable to account for spatial

  3. Spatial data analysis and integration for regional-scale geothermal potential mapping, West Java, Indonesia

    Energy Technology Data Exchange (ETDEWEB)

    Carranza, Emmanuel John M.; Barritt, Sally D. [Department of Earth Systems Analysis, International Institute for Geo-information Science and Earth Observation (ITC), Enschede (Netherlands); Wibowo, Hendro; Sumintadireja, Prihadi [Laboratory of Volcanology and Geothermal, Geology Department, Institute of Technology Bandung (ITB), Bandung (Indonesia)

    2008-06-15

    Conceptual modeling and predictive mapping of potential for geothermal resources at the regional-scale in West Java are supported by analysis of the spatial distribution of geothermal prospects and thermal springs, and their spatial associations with geologic features derived from publicly available regional-scale spatial data sets. Fry analysis shows that geothermal occurrences have regional-scale spatial distributions that are related to Quaternary volcanic centers and shallow earthquake epicenters. Spatial frequency distribution analysis shows that geothermal occurrences have strong positive spatial associations with Quaternary volcanic centers, Quaternary volcanic rocks, quasi-gravity lows, and NE-, NNW-, WNW-trending faults. These geological features, with their strong positive spatial associations with geothermal occurrences, constitute spatial recognition criteria of regional-scale geothermal potential in a study area. Application of data-driven evidential belief functions in GIS-based predictive mapping of regional-scale geothermal potential resulted in delineation of high potential zones occupying 25% of West Java, which is a substantial reduction of the search area for further exploration of geothermal resources. The predicted high potential zones delineate about 53-58% of the training geothermal areas and 94% of the validated geothermal occurrences. The results of this study demonstrate the value of regional-scale geothermal potential mapping in: (a) data-poor situations, such as West Java, and (b) regions with geotectonic environments similar to the study area. (author)

  4. [Multiple time scales analysis of spatial differentiation characteristics of non-point source nitrogen loss within watershed].

    Science.gov (United States)

    Liu, Mei-bing; Chen, Xing-wei; Chen, Ying

    2015-07-01

    Identification of the critical source areas of non-point source pollution is an important means to control the non-point source pollution within the watershed. In order to further reveal the impact of multiple time scales on the spatial differentiation characteristics of non-point source nitrogen loss, a SWAT model of Shanmei Reservoir watershed was developed. Based on the simulation of total nitrogen (TN) loss intensity of all 38 subbasins, spatial distribution characteristics of nitrogen loss and critical source areas were analyzed at three time scales of yearly average, monthly average and rainstorms flood process, respectively. Furthermore, multiple linear correlation analysis was conducted to analyze the contribution of natural environment and anthropogenic disturbance on nitrogen loss. The results showed that there were significant spatial differences of TN loss in Shanmei Reservoir watershed at different time scales, and the spatial differentiation degree of nitrogen loss was in the order of monthly average > yearly average > rainstorms flood process. TN loss load mainly came from upland Taoxi subbasin, which was identified as the critical source area. At different time scales, land use types (such as farmland and forest) were always the dominant factor affecting the spatial distribution of nitrogen loss, while the effect of precipitation and runoff on the nitrogen loss was only taken in no fertilization month and several processes of storm flood at no fertilization date. This was mainly due to the significant spatial variation of land use and fertilization, as well as the low spatial variability of precipitation and runoff.

  5. Non-standard spatial statistics and spatial econometrics

    CERN Document Server

    Griffith, Daniel A

    2011-01-01

    Spatial statistics and spatial econometrics are recent sprouts of the tree "spatial analysis with measurement". Still, several general themes have emerged. Exploring selected fields of possible interest is tantalizing, and this is what the authors aim here.

  6. Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2002-07

    Science.gov (United States)

    Pearson, D.K.; Gary, R.H.; Wilson, Z.D.

    2007-01-01

    Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is particularly useful when analyzing a wide variety of spatial data such as with remote sensing and spatial analysis. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This document presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup from 2002 through 2007.

  7. Spatial analysis and characteristics of pig farming in Thailand.

    Science.gov (United States)

    Thanapongtharm, Weerapong; Linard, Catherine; Chinson, Pornpiroon; Kasemsuwan, Suwicha; Visser, Marjolein; Gaughan, Andrea E; Epprech, Michael; Robinson, Timothy P; Gilbert, Marius

    2016-10-06

    In Thailand, pig production intensified significantly during the last decade, with many economic, epidemiological and environmental implications. Strategies toward more sustainable future developments are currently investigated, and these could be informed by a detailed assessment of the main trends in the pig sector, and on how different production systems are geographically distributed. This study had two main objectives. First, we aimed to describe the main trends and geographic patterns of pig production systems in Thailand in terms of pig type (native, breeding, and fattening pigs), farm scales (smallholder and large-scale farming systems) and type of farming systems (farrow-to-finish, nursery, and finishing systems) based on a very detailed 2010 census. Second, we aimed to study the statistical spatial association between these different types of pig farming distribution and a set of spatial variables describing access to feed and markets. Over the last decades, pig population gradually increased, with a continuously increasing number of pigs per holder, suggesting a continuing intensification of the sector. The different pig-production systems showed very contrasted geographical distributions. The spatial distribution of large-scale pig farms corresponds with that of commercial pig breeds, and spatial analysis conducted using Random Forest distribution models indicated that these were concentrated in lowland urban or peri-urban areas, close to means of transportation, facilitating supply to major markets such as provincial capitals and the Bangkok Metropolitan region. Conversely the smallholders were distributed throughout the country, with higher densities located in highland, remote, and rural areas, where they supply local rural markets. A limitation of the study was that pig farming systems were defined from the number of animals per farm, resulting in their possible misclassification, but this should have a limited impact on the main patterns revealed

  8. Temporal and spatial distribution characteristics and influencing factors of air quality index in Xuchang

    Science.gov (United States)

    Wang, Zhenghua; Tian, Zhihui

    2018-01-01

    In recent years, the problem of air pollution becomes more and more serious. Based on the geographic and seasonal climatic characteristics of Xuchang City, this paper studies the temporal and spatial distribution characteristics of air quality index. The results show that: from the time point of view, air quality index shows seasonal difference. Air quality index is highest in winter and is lowest in summer. From the space point of view, there are differences between the north and the south to a certain extent. Changge City, Yuzhou city and central Xuchang county is higher than the southeast of Xiangcheng county and Yanling county. The spatial and temporal variation characteristics of air quality index in Xuchang are influenced by natural factors and human activities, and the economic development and population are the important factors affecting the urban air quality.

  9. Spatially Resolved Analysis of Bragg Selectivity

    Directory of Open Access Journals (Sweden)

    Tina Sabel

    2015-11-01

    Full Text Available This paper targets an inherent control of optical shrinkage in photosensitive polymers, contributing by means of spatially resolved analysis of volume holographic phase gratings. Point by point scanning of the local material response to the Gaussian intensity distribution of the recording beams is accomplished. Derived information on the local grating period and grating slant is evaluated by mapping of optical shrinkage in the lateral plane as well as through the depth of the layer. The influence of recording intensity, exposure duration and the material viscosity on the Bragg selectivity is investigated.

  10. High resolution or optimum resolution? Spatial analysis of the Federmesser site at Andernach, Germany

    NARCIS (Netherlands)

    Stapert, D; Street, M

    1997-01-01

    This paper discusses spatial analysis at site level. It is suggested that spatial analysis has to proceed in several levels, from global to more detailed questions, and that optimum resolution should be established when applying any quantitative methods in this field. As an example, the ring and

  11. Spatial Analysis of International Tourist Movement to Langkawi for 2010 and 2011

    Directory of Open Access Journals (Sweden)

    Basiron Nur Fatma Zuhra

    2014-01-01

    Full Text Available Tourism is a temporary movement of people to a destination of their choice intended for leisure or recreation. The resulting of this movement can be divided into interdestination dan intradestinastion. This study is about interdestination movement which is characterised by movement from tourist-generating regions to one or more destinations. This research aims to analyse the movement of international tourist who come to Langkawi in 2010 and 2011. The application geographic information system (GIS is aids in visualizing spatial data through mapping. Regression analysis using SPSS was also used to examine the relationship between the distance and the amount of flow that exists. Map of the distribution and flow of tourist was produced to facilitate the analysis. The results of the analyses show that the tourist movement is driven by certain factors such as climatic, economic, distance and etc. Regression analysis showed the nexus between the distance and the amount of the tourist flow was not strong. Map produced can be used by LADA specifically for planning marketing strategies and etc.

  12. Comparative analysis of elements and models of implementation in local-level spatial plans in Serbia

    Directory of Open Access Journals (Sweden)

    Stefanović Nebojša

    2017-01-01

    Full Text Available Implementation of local-level spatial plans is of paramount importance to the development of the local community. This paper aims to demonstrate the importance of and offer further directions for research into the implementation of spatial plans by presenting the results of a study on models of implementation. The paper describes the basic theoretical postulates of a model for implementing spatial plans. A comparative analysis of the application of elements and models of implementation of plans in practice was conducted based on the spatial plans for the local municipalities of Arilje, Lazarevac and Sremska Mitrovica. The analysis includes four models of implementation: the strategy and policy of spatial development; spatial protection; the implementation of planning solutions of a technical nature; and the implementation of rules of use, arrangement and construction of spaces. The main results of the analysis are presented and used to give recommendations for improving the elements and models of implementation. Final deliberations show that models of implementation are generally used in practice and combined in spatial plans. Based on the analysis of how models of implementation are applied in practice, a general conclusion concerning the complex character of the local level of planning is presented and elaborated. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. TR 36035: Spatial, Environmental, Energy and Social Aspects of Developing Settlements and Climate Change - Mutual Impacts and Grant no. III 47014: The Role and Implementation of the National Spatial Plan and Regional Development Documents in Renewal of Strategic Research, Thinking and Governance in Serbia

  13. Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system.

    Directory of Open Access Journals (Sweden)

    Ni Huang

    Full Text Available To examine the method for estimating the spatial patterns of soil respiration (Rs in agricultural ecosystems using remote sensing and geographical information system (GIS, Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI, canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m(-2 s(-1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.

  14. Risk factors for spatial memory impairment in patients with temporal lobe epilepsy

    Czech Academy of Sciences Publication Activity Database

    Amlerová, J.; Laczó, J.; Vlček, Kamil; Javůrková, A.; Andel, R.; Marusič, P.

    2013-01-01

    Roč. 26, č. 1 (2013), s. 57-60 ISSN 1525-5050 R&D Projects: GA ČR(CZ) GA309/09/1053 Grant - others:GA MŠk(CZ) ED1.100/02/0123 Institutional research plan: CEZ:AV0Z50110509 Institutional support: RVO:67985823 Keywords : spatial navigation * temporal lobe epilepsy * Morris water maze Subject RIV: FH - Neurology Impact factor: 2.061, year: 2013

  15. Temporal-spatial variations and driving factors analysis of coastal reclamation in China

    Science.gov (United States)

    Meng, Weiqing; Hu, Beibei; He, Mengxuan; Liu, Baiqiao; Mo, Xunqiang; Li, Hongyuan; Wang, Zhongliang; Zhang, Yu

    2017-05-01

    Coastal reclamation is the gain of land from the sea or coastal wetlands for agricultural purposes, industrial use or port expansions. Large-scale coastal land reclamation can have adverse effects on the coastal environment, including loss of marine habitats and deterioration of coastal water quality. In recent decades, coastal land reclamation has occurred extensively to meet the increasing needs of rapid economic development and urbanization in China. The overall objective of this study is to understand the coastal reclamation status of China from 1979 to 2014 and analyzed its driving factors for mitigating negative ecological effects. The data of coastal reclamation were done with the ERDAS Imagine V9.2 platform and ArcGIS software based on remote images including Landsat, SPOT, ZY-2 and ZY-3. Potential driving factors for sea reclamation were selected based on statistics bulletins and the knowledge of experts in coastal management. In order to understand the relationships among possible impact factors and coastal reclamation, the Partial Least-Squares Regression models was constructed. The analysis results indicated that the total area of reclamation was 11162.89 km2 based on remote sensing images between 1979 and 2014. Shandong Province is the largest reclamation area, reaching 2736.54 km2, and the reclamation is mainly concentrated in Zhejiang, Jiangsu and Liaoning, where the reclamation areas were all more than 1000 km2. According to the remote sensing images, there are three coastal reclamation hotspot regions including Bohai bay (in which is located Liaoning, Tianjin and Hebei), Jiangsu province coastal area and Hangzhou bay (in Zhejiang province). A large scale land reclamation plan of more than 5880 km2 has been made by local government and 2469 km2 has approved by the State Council. From the analyzed results, there is a significant collinearity between these indicators, and no significant correlation between the area of reclamation and selected

  16. Spatial correction factors for YALINA Booster facility loaded with medium and low enriched fuels

    International Nuclear Information System (INIS)

    Talamo, A.; Gohar, Y.; Bournos, V.; Fokov, Y.; Kiyavitskaya, H.; Routkovskaya, C.

    2012-01-01

    The Bell and Glasstone spatial correction factor is used in analyses of subcritical assemblies to correct the experimental reactivity as function of the detector position. Besides the detector position, several other parameters affect the correction factor: the energy weighting function of the detector, the detector size, the energy-angle distribution of source neutrons, and the reactivity of the subcritical assembly. This work focuses on the dependency of the correction factor on the detector material and it investigates the YALINA Booster subcritical assembly loaded with medium (36%) and low (10%) enriched fuels. (authors)

  17. Spatial correction factors for YALINA Booster facility loaded with medium and low enriched fuels

    Energy Technology Data Exchange (ETDEWEB)

    Talamo, A.; Gohar, Y. [Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL 60439 (United States); Bournos, V.; Fokov, Y.; Kiyavitskaya, H.; Routkovskaya, C. [Joint Inst. for Power and Nuclear Research-Sosny, 99 Academician A.K.Krasin Str, Minsk 220109 (Belarus)

    2012-07-01

    The Bell and Glasstone spatial correction factor is used in analyses of subcritical assemblies to correct the experimental reactivity as function of the detector position. Besides the detector position, several other parameters affect the correction factor: the energy weighting function of the detector, the detector size, the energy-angle distribution of source neutrons, and the reactivity of the subcritical assembly. This work focuses on the dependency of the correction factor on the detector material and it investigates the YALINA Booster subcritical assembly loaded with medium (36%) and low (10%) enriched fuels. (authors)

  18. Visuo-Spatial Performance in Autism: A Meta-Analysis

    Science.gov (United States)

    Muth, Anne; Hönekopp, Johannes; Falter, Christine M.

    2014-01-01

    Visuo-spatial skills are believed to be enhanced in autism spectrum disorders (ASDs). This meta-analysis tests the current state of evidence for Figure Disembedding, Block Design, Mental Rotation and Navon tasks in ASD and neurotypicals. Block Design (d = 0.32) and Figure Disembedding (d = 0.26) showed superior performance for ASD with large…

  19. Development of spatial data guidelines and standards: spatial data set documentation to support hydrologic analysis in the U.S. Geological Survey

    Science.gov (United States)

    Fulton, James L.

    1992-01-01

    Spatial data analysis has become an integral component in many surface and sub-surface hydrologic investigations within the U.S. Geological Survey (USGS). Currently, one of the largest costs in applying spatial data analysis is the cost of developing the needed spatial data. Therefore, guidelines and standards are required for the development of spatial data in order to allow for data sharing and reuse; this eliminates costly redevelopment. In order to attain this goal, the USGS is expanding efforts to identify guidelines and standards for the development of spatial data for hydrologic analysis. Because of the variety of project and database needs, the USGS has concentrated on developing standards for documenting spatial sets to aid in the assessment of data set quality and compatibility of different data sets. An interim data set documentation standard (1990) has been developed that provides a mechanism for associating a wide variety of information with a data set, including data about source material, data automation and editing procedures used, projection parameters, data statistics, descriptions of features and feature attributes, information on organizational contacts lists of operations performed on the data, and free-form comments and notes about the data, made at various times in the evolution of the data set. The interim data set documentation standard has been automated using a commercial geographic information system (GIS) and data set documentation software developed by the USGS. Where possible, USGS developed software is used to enter data into the data set documentation file automatically. The GIS software closely associates a data set with its data set documentation file; the documentation file is retained with the data set whenever it is modified, copied, or transferred to another computer system. The Water Resources Division of the USGS is continuing to develop spatial data and data processing standards, with emphasis on standards needed to support

  20. Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis

    Directory of Open Access Journals (Sweden)

    F. Tsai

    2010-10-01

    Full Text Available On 8 August 2009, the extreme rainfall of Typhoon Morakot triggered enormous landslides in mountainous regions of southern Taiwan, causing catastrophic infrastructure and property damages and human casualties. A comprehensive evaluation of the landslides is essential for the post-disaster reconstruction and should be helpful for future hazard mitigation. This paper presents a systematic approach to utilize multi-temporal satellite images and other geo-spatial data for the post-disaster assessment of landslides on a regional scale. Rigorous orthorectification and radiometric correction procedures were applied to the satellite images. Landslides were identified with NDVI filtering, change detection analysis and interactive post-analysis editing to produce an accurate landslide map. Spatial analysis was performed to obtain statistical characteristics of the identified landslides and their relationship with topographical factors. A total of 9333 landslides (22 590 ha was detected from change detection analysis of satellite images. Most of the detected landslides are smaller than 10 ha. Less than 5% of them are larger than 10 ha but together they constitute more than 45% of the total landslide area. Spatial analysis of the detected landslides indicates that most of them have average elevations between 500 m to 2000 m and with average slope gradients between 20° and 40°. In addition, a particularly devastating landslide whose debris flow destroyed a riverside village was examined in depth for detailed investigation. The volume of this slide is estimated to be more than 2.6 million m3 with an average depth of 40 m.

  1. Elevation and cholera: an epidemiological spatial analysis of the cholera epidemic in Harare, Zimbabwe, 2008-2009

    Directory of Open Access Journals (Sweden)

    Luque Fernandez Miguel A

    2012-06-01

    Full Text Available Abstract Background In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009. Methods We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighboring suburbs. Results This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30% lower with a rate ratio of 0.70 (95% confidence interval=0.66-0.76. Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20% and 40%. Conclusion This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive

  2. HIV/AIDS in Oyo State, Nigeria: Analysis of Spatial Pattern of ...

    African Journals Online (AJOL)

    DrNneka

    recognized in 1981 in New York, a number of factors, habits and practices have .... poses as a stimulus which catalyzes the manifestation of the symptoms of. AIDS as ... the study on the geographical or spatial distribution of HIV/AIDS cases in.

  3. Spatial and temporal analysis of mass movement using dendrochronology

    NARCIS (Netherlands)

    Braam, R.R.; Weiss, E.E.J.; Burrough, P.A.

    1987-01-01

    Tree growth and inclination on sloping land is affected by mass movement. Suitable analysis of tree growth and tree form can therefore provide considerable information on mass movement activity. This paper reports a new, automated method for studying the temporal and spatial aspects of mass

  4. Analysis of Spatial Concepts, Spatial Skills and Spatial Representations in New York State Regents Earth Science Examinations

    Science.gov (United States)

    Kastens, Kim A.; Pistolesi, Linda; Passow, Michael J.

    2014-01-01

    Research has shown that spatial thinking is important in science in general, and in Earth Science in particular, and that performance on spatially demanding tasks can be fostered through instruction. Because spatial thinking is rarely taught explicitly in the U.S. education system, improving spatial thinking may be "low-hanging fruit" as…

  5. A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

    Directory of Open Access Journals (Sweden)

    Michael F. Goodchild

    2013-05-01

    Full Text Available Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1 widespread sharing and seamless integration of distributed geospatial data; (2 an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3 the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4 capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations.

  6. A Spatial Analysis of Tourism Activity in Romania

    Directory of Open Access Journals (Sweden)

    Daniela Luminita Constantin

    2018-02-01

    Full Text Available Location is a key concept in tourism sector analysis, given the dependence of this activity on the natural, built, cultural and social characteristics of a certain territory. As a result, the tourist zoning is an important instrument for delimiting tourist areas in accordance with multiple criteria, so as to lay the foundations for finding the most suitable solutions of turning to good account the resources in this field. The modern approaches proposed in this paper use a series of analytical tools that combine GIS and spatial agglomeration analysis based techniques. They can be also employed in order to examine and explain the differences between tourist zones (and sub-zones in terms of economic and social results and thus to suggest realistic ways to improve the efficiency and effectiveness of tourist activities in various geographical areas. In the described context this paper proposes an interdisciplinary perspective (spatial statistics and Geographical Information Systems for analysing the tourism activity in Romania, mainly aiming to identify the agglomerations of companies acting in this industry and assess their performance and contribution to the economic development of the corresponding regions. It also intends to contribute to a better understanding of the way in which tourism related business activities develop, in order to enhance appropriate support networks. Territorial and spatial statistics, as well as GIS based analyses are applied, using data about all companies acting in tourism industry in Romania provided by the National Authority for Tourism as well as data from the Environmental Systems Research Institute (ESRI.

  7. Spatially resolved δ13C analysis using laser ablation isotope ratio mass spectrometry

    Science.gov (United States)

    Moran, J.; Riha, K. M.; Nims, M. K.; Linley, T. J.; Hess, N. J.; Nico, P. S.

    2014-12-01

    Inherent geochemical, organic matter, and microbial heterogeneity over small spatial scales can complicate studies of carbon dynamics through soils. Stable isotope analysis has a strong history of helping track substrate turnover, delineate rhizosphere activity zones, and identifying transitions in vegetation cover, but most traditional isotope approaches are limited in spatial resolution by a combination of physical separation techniques (manual dissection) and IRMS instrument sensitivity. We coupled laser ablation sampling with isotope measurement via IRMS to enable spatially resolved analysis over solid surfaces. Once a targeted sample region is ablated the resulting particulates are entrained in a helium carrier gas and passed through a combustion reactor where carbon is converted to CO2. Cyrotrapping of the resulting CO2 enables a reduction in carrier gas flow which improves overall measurement sensitivity versus traditional, high flow sample introduction. Currently we are performing sample analysis at 50 μm resolution, require 65 ng C per analysis, and achieve measurement precision consistent with other continuous flow techniques. We will discuss applications of the laser ablation IRMS (LA-IRMS) system to microbial communities and fish ecology studies to demonstrate the merits of this technique and how similar analytical approaches can be transitioned to soil systems. Preliminary efforts at analyzing soil samples will be used to highlight strengths and limitations of the LA-IRMS approach, paying particular attention to sample preparation requirements, spatial resolution, sample analysis time, and the types of questions most conducive to analysis via LA-IRMS.

  8. Topographic factor analysis: a Bayesian model for inferring brain networks from neural data.

    Directory of Open Access Journals (Sweden)

    Jeremy R Manning

    Full Text Available The neural patterns recorded during a neuroscientific experiment reflect complex interactions between many brain regions, each comprising millions of neurons. However, the measurements themselves are typically abstracted from that underlying structure. For example, functional magnetic resonance imaging (fMRI datasets comprise a time series of three-dimensional images, where each voxel in an image (roughly reflects the activity of the brain structure(s-located at the corresponding point in space-at the time the image was collected. FMRI data often exhibit strong spatial correlations, whereby nearby voxels behave similarly over time as the underlying brain structure modulates its activity. Here we develop topographic factor analysis (TFA, a technique that exploits spatial correlations in fMRI data to recover the underlying structure that the images reflect. Specifically, TFA casts each brain image as a weighted sum of spatial functions. The parameters of those spatial functions, which may be learned by applying TFA to an fMRI dataset, reveal the locations and sizes of the brain structures activated while the data were collected, as well as the interactions between those structures.

  9. Geo-Spatial Multi-criteria Analysis for Wave Energy System Deployment

    Energy Technology Data Exchange (ETDEWEB)

    Nobre, Ana; Pacheco, Miguel (Instituto Hidrografico, Rua das Trinas, 49, Lisboa (PT)); Jorge, Raquel Lopes, M. F. P.; Gato, L. M. C. (IDMEC, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, Lisboa (PT))

    2007-07-01

    The growing requirements for renewable energy production lead to the development of a new series of systems, including wave energy conversion systems. Due to their sensitivity and the impact of the aggressive marine environment, the selection of the most adequate location for these systems is a major and very important task. Several factors, such as technological limitations, environmental conditions, administrative and logistic conditions, have to be taken into account in order to support the decision for best location. This paper describes a geo-spatial multi-criteria analysis methodology, based on geographic information systems technology, for selection of the best location to deploy a wave energy farm. This methodology is not conversion system dependent and therefore can be easily customized for different systems and conditions. Selection factors can include, for example, ocean depth, bottom type, underwater cables, marine protected areas, ports location, shoreline, power grid location, military exercise areas, climatology of wave significant height, period and direction. A case study demonstrating this methodology is presented, for an area offshore the Portuguese southwest coast. The system output allows a clear identification of the best spots for a wave energy farm. It is not just a simple Boolean result showing valid and invalid locations, but a layer with a graded suitability for farm deployment.

  10. Anthropogenic factors and the risk of highly pathogenic avian influenza H5N1: prospects from a spatial-based model.

    Science.gov (United States)

    Paul, Mathilde; Tavornpanich, Saraya; Abrial, David; Gasqui, Patrick; Charras-Garrido, Myriam; Thanapongtharm, Weerapong; Xiao, Xiangming; Gilbert, Marius; Roger, Francois; Ducrot, Christian

    2010-01-01

    Beginning in 2003, highly pathogenic avian influenza (HPAI) H5N1 virus spread across Southeast Asia, causing unprecedented epidemics. Thailand was massively infected in 2004 and 2005 and continues today to experience sporadic outbreaks. While research findings suggest that the spread of HPAI H5N1 is influenced primarily by trade patterns, identifying the anthropogenic risk factors involved remains a challenge. In this study, we investigated which anthropogenic factors played a role in the risk of HPAI in Thailand using outbreak data from the "second wave" of the epidemic (3 July 2004 to 5 May 2005) in the country. We first performed a spatial analysis of the relative risk of HPAI H5N1 at the subdistrict level based on a hierarchical Bayesian model. We observed a strong spatial heterogeneity of the relative risk. We then tested a set of potential risk factors in a multivariable linear model. The results confirmed the role of free-grazing ducks and rice-cropping intensity but showed a weak association with fighting cock density. The results also revealed a set of anthropogenic factors significantly linked with the risk of HPAI. High risk was associated strongly with densely populated areas, short distances to a highway junction, and short distances to large cities. These findings highlight a new explanatory pattern for the risk of HPAI and indicate that, in addition to agro-environmental factors, anthropogenic factors play an important role in the spread of H5N1. To limit the spread of future outbreaks, efforts to control the movement of poultry products must be sustained. INRA, EDP Sciences, 2010.

  11. Positive Analysis of Invasive Species Control as a Dynamic Spatial Process

    OpenAIRE

    Buyuktahtakin, Esra; Feng, Zhuo; Olsson, Aaryn; Frisvold, George B.; Szidarovszky, Ferenc

    2010-01-01

    This paper models control of invasive buffelgrass (Pennisetum ciliare), a fire-prone African bunchgrass spreading rapidly across the southern Arizona desert as a spatial dynamic process. Buffelgrass spreads over a gridded landscape. Weed carrying capacity, treatment costs, and damages vary over grid cells. Damage from buffelgrass depends on its spatial distribution in relation to valued resources. We conduct positive analysis of recommended heuristic strategies for buffelgrass control, evalua...

  12. Spatial Variation of Magnitude Scaling Factors During the 2010 Darfield and 2011 Christchurch, New Zealand, Earthquakes

    OpenAIRE

    Carter, William Lake

    2016-01-01

    Magnitude Scaling Factors (MSF) account for the durational effects of strong ground shaking on the inducement of liquefaction within the simplified liquefaction evaluation procedure which is the most commonly used approach for assessing liquefaction potential worldwide. Within the context of the simplified procedure, the spatial variation in the seismic demand imposed on the soil traditionally has been assumed to be solely a function of the spatial variation of the peak amplitude of the groun...

  13. Temporal changes of spatial soil moisture patterns: controlling factors explained with a multidisciplinary approach

    Science.gov (United States)

    Martini, Edoardo; Wollschläger, Ute; Kögler, Simon; Behrens, Thorsten; Dietrich, Peter; Reinstorf, Frido; Schmidt, Karsten; Weiler, Markus; Werban, Ulrike; Zacharias, Steffen

    2016-04-01

    Characterizing the spatial patterns of soil moisture is critical for hydrological and meteorological models, as soil moisture is a key variable that controls matter and energy fluxes and soil-vegetation-atmosphere exchange processes. Deriving detailed process understanding at the hillslope scale is not trivial, because of the temporal variability of local soil moisture dynamics. Nevertheless, it remains a challenge to provide adequate information on the temporal variability of soil moisture and its controlling factors. Recent advances in wireless sensor technology allow monitoring of soil moisture dynamics with high temporal resolution at varying scales. In addition, mobile geophysical methods such as electromagnetic induction (EMI) have been widely used for mapping soil water content at the field scale with high spatial resolution, as being related to soil apparent electrical conductivity (ECa). The objective of this study was to characterize the spatial and temporal pattern of soil moisture at the hillslope scale and to infer the controlling hydrological processes, integrating well established and innovative sensing techniques, as well as new statistical methods. We combined soil hydrological and pedological expertise with geophysical measurements and methods from digital soil mapping for designing a wireless soil moisture monitoring network. For a hillslope site within the Schäfertal catchment (Central Germany), soil water dynamics were observed during 14 months, and soil ECa was mapped on seven occasions whithin this period of time using an EM38-DD device. Using the Spearman rank correlation coefficient, we described the temporal persistence of a dry and a wet characteristic state of soil moisture as well as the switching mechanisms, inferring the local properties that control the observed spatial patterns and the hydrological processes driving the transitions. Based on this, we evaluated the use of EMI for mapping the spatial pattern of soil moisture under

  14. Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression

    Directory of Open Access Journals (Sweden)

    Boris Kauhl

    2016-11-01

    Full Text Available Abstract Background The provision of general practitioners (GPs in Germany still relies mainly on the ratio of inhabitants to GPs at relatively large scales and barely accounts for an increased prevalence of chronic diseases among the elderly and socially underprivileged populations. Type 2 Diabetes Mellitus (T2DM is one of the major cost-intensive diseases with high rates of potentially preventable complications. Provision of healthcare and access to preventive measures is necessary to reduce the burden of T2DM. However, current studies on the spatial variation of T2DM in Germany are mostly based on survey data, which do not only underestimate the true prevalence of T2DM, but are also only available on large spatial scales. The aim of this study is therefore to analyse the spatial distribution of T2DM at fine geographic scales and to assess location-specific risk factors based on data of the AOK health insurance. Methods To display the spatial heterogeneity of T2DM, a bivariate, adaptive kernel density estimation (KDE was applied. The spatial scan statistic (SaTScan was used to detect areas of high risk. Global and local spatial regression models were then constructed to analyze socio-demographic risk factors of T2DM. Results T2DM is especially concentrated in rural areas surrounding Berlin. The risk factors for T2DM consist of proportions of 65–79 year olds, 80 + year olds, unemployment rate among the 55–65 year olds, proportion of employees covered by mandatory social security insurance, mean income tax, and proportion of non-married couples. However, the strength of the association between T2DM and the examined socio-demographic variables displayed strong regional variations. Conclusion The prevalence of T2DM varies at the very local level. Analyzing point data on T2DM of northeastern Germany’s largest health insurance provider thus allows very detailed, location-specific knowledge about increased medical needs. Risk factors

  15. Spatial quality, location theory and spatial planning

    NARCIS (Netherlands)

    Assink, Mathijs; Groenendijk, Nico

    2009-01-01

    This paper deals with spatial quality as a possible factor in location choices made by companies. Actual location decisions as well as location theory have changed over time. In the industrial era primary “hard” cost factors were dominant, to be supplemented by agglomeration factors ever since the

  16. Spatial Intensity Duration Frequency Relationships Using Hierarchical Bayesian Analysis for Urban Areas

    Science.gov (United States)

    Rupa, Chandra; Mujumdar, Pradeep

    2016-04-01

    In urban areas, quantification of extreme precipitation is important in the design of storm water drains and other infrastructure. Intensity Duration Frequency (IDF) relationships are generally used to obtain design return level for a given duration and return period. Due to lack of availability of extreme precipitation data for sufficiently large number of years, estimating the probability of extreme events is difficult. Typically, a single station data is used to obtain the design return levels for various durations and return periods, which are used in the design of urban infrastructure for the entire city. In an urban setting, the spatial variation of precipitation can be high; the precipitation amounts and patterns often vary within short distances of less than 5 km. Therefore it is crucial to study the uncertainties in the spatial variation of return levels for various durations. In this work, the extreme precipitation is modeled spatially using the Bayesian hierarchical analysis and the spatial variation of return levels is studied. The analysis is carried out with Block Maxima approach for defining the extreme precipitation, using Generalized Extreme Value (GEV) distribution for Bangalore city, Karnataka state, India. Daily data for nineteen stations in and around Bangalore city is considered in the study. The analysis is carried out for summer maxima (March - May), monsoon maxima (June - September) and the annual maxima rainfall. In the hierarchical analysis, the statistical model is specified in three layers. The data layer models the block maxima, pooling the extreme precipitation from all the stations. In the process layer, the latent spatial process characterized by geographical and climatological covariates (lat-lon, elevation, mean temperature etc.) which drives the extreme precipitation is modeled and in the prior level, the prior distributions that govern the latent process are modeled. Markov Chain Monte Carlo (MCMC) algorithm (Metropolis Hastings

  17. Impact of the neutron detector choice on Bell and Glasstone spatial correction factor for subcriticality measurement

    Energy Technology Data Exchange (ETDEWEB)

    Talamo, Alberto, E-mail: alby@anl.gov [Argonne National Laboratory, 9700S. Cass Avenue, Argonne, IL 60439 (United States); Gohar, Y.; Cao, Y.; Zhong, Z. [Argonne National Laboratory, 9700S. Cass Avenue, Argonne, IL 60439 (United States); Kiyavitskaya, H.; Bournos, V.; Fokov, Y.; Routkovskaya, C. [Joint Institute for Power and Nuclear Research-Sosny, National Academy of Sciences of Belarus, 99 acad. Krasin str., Minsk 220109 (Belarus)

    2012-03-11

    In subcritical assemblies, the Bell and Glasstone spatial correction factor is used to correct the measured reactivity from different detector positions. In addition to the measuring position, several other parameters affect the correction factor: the detector material, the detector size, and the energy-angle distribution of source neutrons. The effective multiplication factor calculated by computer codes in criticality mode slightly differs from the average value obtained from the measurements in the different experimental channels of the subcritical assembly, which are corrected by the Bell and Glasstone spatial correction factor. Generally, this difference is due to (1) neutron counting errors; (2) geometrical imperfections, which are not simulated in the calculational model, and (3) quantities and distributions of material impurities, which are missing from the material definitions. This work examines these issues and it focuses on the detector choice and the calculation methodologies. The work investigated the YALINA Booster subcritical assembly of Belarus, which has been operated with three different fuel enrichments in the fast zone either: high (90%) and medium (36%), medium (36%), or low (21%) enriched uranium fuel.

  18. Impact of the neutron detector choice on Bell and Glasstone spatial correction factor for subcriticality measurement

    International Nuclear Information System (INIS)

    Talamo, Alberto; Gohar, Y.; Cao, Y.; Zhong, Z.; Kiyavitskaya, H.; Bournos, V.; Fokov, Y.; Routkovskaya, C.

    2012-01-01

    In subcritical assemblies, the Bell and Glasstone spatial correction factor is used to correct the measured reactivity from different detector positions. In addition to the measuring position, several other parameters affect the correction factor: the detector material, the detector size, and the energy-angle distribution of source neutrons. The effective multiplication factor calculated by computer codes in criticality mode slightly differs from the average value obtained from the measurements in the different experimental channels of the subcritical assembly, which are corrected by the Bell and Glasstone spatial correction factor. Generally, this difference is due to (1) neutron counting errors; (2) geometrical imperfections, which are not simulated in the calculational model, and (3) quantities and distributions of material impurities, which are missing from the material definitions. This work examines these issues and it focuses on the detector choice and the calculation methodologies. The work investigated the YALINA Booster subcritical assembly of Belarus, which has been operated with three different fuel enrichments in the fast zone either: high (90%) and medium (36%), medium (36%), or low (21%) enriched uranium fuel.

  19. The Structure of Spatial Ability Items: A Faceted Analysis.

    Science.gov (United States)

    Guttman, Ruth; Shoham, Ilana

    1982-01-01

    Eight spatial tests assembled with a mapping sentence of four content facets (rule type, dimensionality, presence or absence of rotation, and test format) were administered to 800 individuals. Smallest Space Analysis of an intercorrelation matrix yielded three facets which formed distinct regions in a two-dimensional projection of a…

  20. Spatial prevalence of intellectual disability and related socio-demographic factors in Iran, using GWR: Case study (2006

    Directory of Open Access Journals (Sweden)

    Ali Goli

    2014-01-01

    Full Text Available Background: Although intellectual disability (ID is a common disability in Iran, there is no investigation on the spatial distribution pattern of these patients in national level and the spatial maps for recognition the areas with higher prevalence of IDs and local neighborhoods of these regions or effect of socio-demographic factor on this scattering is not still available. This proposition motivated us to assess the population with ID in our country. Methods: In a cross-sectional study, we applied Moran′s Index (Moran′s I which includes information about the strength of the neighboring association between counties, as global univariate distribution assessment. A geographically weighted regression was used to explore relation between ID patient′s prevalence and some socio-demographic factors (migration and illiteracy rate, physician number (PN/10,000 people and health-care centers (HCCs/10,000 people. Results: We found that spatial clusters of ID patients exist among Iran counties (Moran′s I = 0.36, P 0.3. Conclusions: According to the results, our Initial hypothesis about the existence of spatial clusters in distribution of people with ID in Iran was proven. Spatial autocorrelation between migration and illiteracy rate and prevalence of patients with ID was shown and was in agreement with our hypothesis. However, our supposition that the prevalence should have inverse relationship with PN and HCC was rejected.

  1. Environmental risk of leptospirosis infections in the Netherlands: Spatial modelling of environmental risk factors of leptospirosis in the Netherlands.

    Directory of Open Access Journals (Sweden)

    Ente J J Rood

    Full Text Available Leptospirosis is a globally emerging zoonotic disease, associated with various climatic, biotic and abiotic factors. Mapping and quantifying geographical variations in the occurrence of leptospirosis and the surrounding environment offer innovative methods to study disease transmission and to identify associations between the disease and the environment. This study aims to investigate geographic variations in leptospirosis incidence in the Netherlands and to identify associations with environmental factors driving the emergence of the disease. Individual case data derived over the period 1995-2012 in the Netherlands were geocoded and aggregated by municipality. Environmental covariate data were extracted for each municipality and stored in a spatial database. Spatial clusters were identified using kernel density estimations and quantified using local autocorrelation statistics. Associations between the incidence of leptospirosis and the local environment were determined using Simultaneous Autoregressive Models (SAR explicitly modelling spatial dependence of the model residuals. Leptospirosis incidence rates were found to be spatially clustered, showing a marked spatial pattern. Fitting a spatial autoregressive model significantly improved model fit and revealed significant association between leptospirosis and the coverage of arable land, built up area, grassland and sabulous clay soils. The incidence of leptospirosis in the Netherlands could effectively be modelled using a combination of soil and land-use variables accounting for spatial dependence of incidence rates per municipality. The resulting spatially explicit risk predictions provide an important source of information which will benefit clinical awareness on potential leptospirosis infections in endemic areas.

  2. Spatial Analysis of Depots for Advanced Biomass Processing

    Energy Technology Data Exchange (ETDEWEB)

    Hilliard, Michael R. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Brandt, Craig C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Webb, Erin [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Sokhansanj, Shahabaddine [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Eaton, Laurence M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Martinez Gonzalez, Maria I. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2014-09-01

    The objective of this work was to perform a spatial analysis of the total feedstock cost at the conversion reactor for biomass supplied by a conventional system and an advanced system with depots to densify biomass into pellets. From these cost estimates, the conditions (feedstock cost and availability) for which advanced processing depots make it possible to achieve cost and volume targets can be identified.

  3. Factors affecting spatial variation of annual apparent Q₁₀ of soil respiration in two warm temperate forests.

    Directory of Open Access Journals (Sweden)

    Junwei Luan

    Full Text Available A range of factors has been identified that affect the temperature sensitivity (Q₁₀ values of the soil-to-atmosphere CO₂ flux. However, the factors influencing the spatial distribution of Q₁₀ values within warm temperate forests are poorly understood. In this study, we examined the spatial variation of Q₁₀ values and its controlling factors in both a naturally regenerated oak forest (OF and a pine plantation (PP. Q₁₀ values were determined based on monthly soil respiration (R(S measurements at 35 subplots for each stand from Oct. 2008 to Oct. 2009. Large spatial variation of Q₁₀ values was found in both OF and PP, with their respective ranges from 1.7 to 5.12 and from 2.3 to 6.21. In PP, fine root biomass (FR (R = 0.50, P = 0.002, non-capillary porosity (NCP (R = 0.37, P = 0.03, and the coefficients of variation of soil temperature at 5 cm depth (CV of T₅ (R = -0.43, P = 0.01 well explained the spatial variance of Q₁₀. In OF, carbon pool lability reflected by light fractionation method (LLFOC well explained the spatial variance of Q₁₀ (R = -0.35, P = 0.04. Regardless of forest type, LLFOC and FR correlation with the Q₁₀ values were significant and marginally significant, respectively; suggesting a positive relationship between substrate availability and apparent Q₁₀ values. Parameters related to gas diffusion, such as average soil water content (SWC and NCP, negatively or positively explained the spatial variance of Q₁₀ values. Additionally, we observed significantly higher apparent Q₁₀ values in PP compared to OF, which might be partly attributed to the difference in soil moisture condition and diffusion ability, rather than different substrate availabilities between forests. Our results suggested that both soil chemical and physical characters contributed to the observed large Q₁₀ value variation.

  4. Hydrological and environmental variables outperform spatial factors in structuring species, trait composition, and beta diversity of pelagic algae.

    Science.gov (United States)

    Wu, Naicheng; Qu, Yueming; Guse, Björn; Makarevičiūtė, Kristė; To, Szewing; Riis, Tenna; Fohrer, Nicola

    2018-03-01

    There has been increasing interest in algae-based bioassessment, particularly, trait-based approaches are increasingly suggested. However, the main drivers, especially the contribution of hydrological variables, of species composition, trait composition, and beta diversity of algae communities are less studied. To link species and trait composition to multiple factors (i.e., hydrological variables, local environmental variables, and spatial factors) that potentially control species occurrence/abundance and to determine their relative roles in shaping species composition, trait composition, and beta diversities of pelagic algae communities, samples were collected from a German lowland catchment, where a well-proven ecohydrological modeling enabled to predict long-term discharges at each sampling site. Both trait and species composition showed significant correlations with hydrological, environmental, and spatial variables, and variation partitioning revealed that the hydrological and local environmental variables outperformed spatial variables. A higher variation of trait composition (57.0%) than species composition (37.5%) could be explained by abiotic factors. Mantel tests showed that both species and trait-based beta diversities were mostly related to hydrological and environmental heterogeneity with hydrological contributing more than environmental variables, while purely spatial impact was less important. Our findings revealed the relative importance of hydrological variables in shaping pelagic algae community and their spatial patterns of beta diversities, emphasizing the need to include hydrological variables in long-term biomonitoring campaigns and biodiversity conservation or restoration. A key implication for biodiversity conservation was that maintaining the instream flow regime and keeping various habitats among rivers are of vital importance. However, further investigations at multispatial and temporal scales are greatly needed.

  5. Multiscale analysis of the spatial variability of heavy metals and organic matter in soils and groundwater across Spain

    Science.gov (United States)

    Luque-Espinar, J. A.; Pardo-Igúzquiza, E.; Grima-Olmedo, J.; Grima-Olmedo, C.

    2018-06-01

    During the last years there has been an increasing interest in assessing health risks caused by exposure to contaminants found in soil, air, and water, like heavy metals or emerging contaminants. This work presents a study on the spatial patterns and interaction effects among relevant heavy metals (Sb, As and Pb) that may occur together in different minerals. Total organic carbon (TOC) have been analyzed too because it is an essential component in the regulatory mechanisms that control the amount of metal in soils. Even more, exposure to these elements is associated with a number of diseases and environmental problems. These metals can have both natural and anthropogenic origins. A key component of any exposure study is a reliable model of the spatial distribution the elements studied. A geostatistical analysis have been performed in order to show that selected metals are auto-correlated and cross-correlated and type and magnitude of such cross-correlation varies depending on the spatial scale under consideration. After identifying general trends, we analyzed the residues left after subtracting the trend from the raw variables. Three scales of variability were identified (compounds or factors) with scales of 5, 35 and 135 km. The first factor (F1) basically identifies anomalies of natural origin but, in some places, of anthropogenics origin as well. The other two are related to geology (F2 and F3) although F3 represents more clearly geochemical background related to large lithological groups. Likewise, mapping of two major structures indicates that significant faults have influence on the distribution of the studied elements. Finally, influence of soil and lithology on groundwater by means of contingency analysis was assessed.

  6. Analysis of human factors on urban heat island and simulation of urban thermal environment in Lanzhou city, China

    Science.gov (United States)

    Pan, Jinghu

    2015-01-01

    Urban heat island (UHI) effect is a global phenomenon caused by urbanization. Because of the number and complexity of factors contributing to the urban thermal environment, traditional statistical methods are insufficient for acquiring data and analyzing the impact of human activities on the thermal environment, especially for identifying which factors are dominant. The UHI elements were extracted using thermal infrared remote sensing data to retrieve the land surface temperatures of Lanzhou city, and then adopting an object-oriented fractal net evolution approach to create an image segmentation of the land surface temperature (LST). The effects of urban expansion on the urban thermal environment were quantitatively analyzed. A comprehensive evaluation system of the urban thermal environment was constructed, the spatial pattern of the urban thermal environment in Lanzhou was assessed, and principal influencing factors were identified using spatial principal component analysis (SPCA) and multisource spatial data. We found that in the last 20 years, the UHI effect in Lanzhou city has been strengthened, as the UHI ratio index has increased from 0.385 in 1993 to 0.579 in 2001 and to 0.653 in 2011. The UHI expansion had a spatiotemporal consistency with the urban expansion. The four major factors that affect the spatial pattern of the urban thermal environment in Lanzhou can be ranked in the following order: landscape configuration, anthropogenic heat release, urban construction, and gradient from man-made to natural land cover. These four together accounted for 91.27% of the variance. A linear model was thus successfully constructed, implying that SPCA is helpful in identifying major contributors to UHI. Regression analysis indicated that the instantaneous LST and the simulated thermal environment have a good linear relationship, the correlation coefficient between the two reached 0.8011, highly significant at a confidence level of 0.001.

  7. Integrating Spatial and Attribute Characteristics of Extended Voronoi Diagrams in Spatial Patterning Research: A Case Study of Wuhan City in China

    Directory of Open Access Journals (Sweden)

    Zuohua Miao

    2016-07-01

    Full Text Available Rapid urbanization has caused numerous problems, and the urban spatial structure has been a hot topic in sustainable development management. Urban spatial structure is affected by a series of factors. Thus, the research model should synthetically consider the spatial and non-spatial relationship of every element. Here, we propose an extended Voronoi diagram for exploring the urban land spatial pattern. In essence, we first used a principal component analysis method to construct attribute evaluation indicators and obtained the attribute distance for each indicator. Second, we integrated spatial and attribute distances to extend the comparison distance for Voronoi diagrams, and then, we constructed the Voronoi aggregative homogeneous map of the study area. Finally, we make a spatial autocorrelation analysis by using GeoDA and SPSS software. Results show that: (1 the residential land cover aggregation is not significant, but spatial diffusion is obvious; (2 the commercial land cover aggregation is considerable; and (3 the spatial agglomeration degree of the industrial land cover is increased and mainly located in urban fringes. According to the neo-Marxist theory, we briefly analyzed the driving forces for shaping the urban spatial structure. To summarize, our approach yields important insights into the urban spatial structure characterized by attribute similarity with geospatial proximity, which contributes to a better understanding of the urban growth mechanism. In addition, it explicitly identifies ongoing urban transformations, potentially supporting the planning for sustainable urban land use and protection.

  8. Spatial analysis of factors influencing long-term stress in the grizzly bear (Ursus arctos) population of Alberta, Canada.

    Science.gov (United States)

    Bourbonnais, Mathieu L; Nelson, Trisalyn A; Cattet, Marc R L; Darimont, Chris T; Stenhouse, Gordon B

    2013-01-01

    Non-invasive measures for assessing long-term stress in free ranging mammals are an increasingly important approach for understanding physiological responses to landscape conditions. Using a spatially and temporally expansive dataset of hair cortisol concentrations (HCC) generated from a threatened grizzly bear (Ursus arctos) population in Alberta, Canada, we quantified how variables representing habitat conditions and anthropogenic disturbance impact long-term stress in grizzly bears. We characterized spatial variability in male and female HCC point data using kernel density estimation and quantified variable influence on spatial patterns of male and female HCC stress surfaces using random forests. Separate models were developed for regions inside and outside of parks and protected areas to account for substantial differences in anthropogenic activity and disturbance within the study area. Variance explained in the random forest models ranged from 55.34% to 74.96% for males and 58.15% to 68.46% for females. Predicted HCC levels were higher for females compared to males. Generally, high spatially continuous female HCC levels were associated with parks and protected areas while low-to-moderate levels were associated with increased anthropogenic disturbance. In contrast, male HCC levels were low in parks and protected areas and low-to-moderate in areas with increased anthropogenic disturbance. Spatial variability in gender-specific HCC levels reveal that the type and intensity of external stressors are not uniform across the landscape and that male and female grizzly bears may be exposed to, or perceive, potential stressors differently. We suggest observed spatial patterns of long-term stress may be the result of the availability and distribution of foods related to disturbance features, potential sexual segregation in available habitat selection, and may not be influenced by sources of mortality which represent acute traumas. In this wildlife system and others

  9. Evidence for fish dispersal from spatial analysis of stream network topology

    Science.gov (United States)

    Hitt, N.P.; Angermeier, P.L.

    2008-01-01

    Developing spatially explicit conservation strategies for stream fishes requires an understanding of the spatial structure of dispersal within stream networks. We explored spatial patterns of stream fish dispersal by evaluating how the size and proximity of connected streams (i.e., stream network topology) explained variation in fish assemblage structure and how this relationship varied with local stream size. We used data from the US Environmental Protection Agency's Environmental Monitoring and Assessment Program in wadeable streams of the Mid-Atlantic Highlands region (n = 308 sites). We quantified stream network topology with a continuous analysis based on the rate of downstream flow accumulation from sites and with a discrete analysis based on the presence of mainstem river confluences (i.e., basin area >250 km2) within 20 fluvial km (fkm) from sites. Continuous variation in stream network topology was related to local species richness within a distance of ???10 fkm, suggesting an influence of fish dispersal within this spatial grain. This effect was explained largely by catostomid species, cyprinid species, and riverine species, but was not explained by zoogeographic regions, ecoregions, sampling period, or spatial autocorrelation. Sites near mainstem river confluences supported greater species richness and abundance of catostomid, cyprinid, and ictalurid fishes than did sites >20 fkm from such confluences. Assemblages at sites on the smallest streams were not related to stream network topology, consistent with the hypothesis that local stream size regulates the influence of regional dispersal. These results demonstrate that the size and proximity of connected streams influence the spatial distribution of fish and suggest that these influences can be incorporated into the designs of stream bioassessments and reserves to enhance management efficacy. ?? 2008 by The North American Benthological Society.

  10. Relative importance of management, meteorological and environmental factors in the spatial distribution of Fasciola hepatica in dairy cattle in a temperate climate zone.

    Science.gov (United States)

    Bennema, S C; Ducheyne, E; Vercruysse, J; Claerebout, E; Hendrickx, G; Charlier, J

    2011-02-01

    Fasciola hepatica, a trematode parasite with a worldwide distribution, is the cause of important production losses in the dairy industry. Diagnosis is hampered by the fact that the infection is mostly subclinical. To increase awareness and develop regionally adapted control methods, knowledge on the spatial distribution of economically important infection levels is needed. Previous studies modelling the spatial distribution of F. hepatica are mostly based on single cross-sectional samplings and have focussed on climatic and environmental factors, often ignoring management factors. This study investigated the associations between management, climatic and environmental factors affecting the spatial distribution of infection with F. hepatica in dairy herds in a temperate climate zone (Flanders, Belgium) over three consecutive years. A bulk-tank milk antibody ELISA was used to measure F. hepatica infection levels in a random sample of 1762 dairy herds in the autumns of 2006, 2007 and 2008. The infection levels were included in a Geographic Information System together with meteorological, environmental and management parameters. Logistic regression models were used to determine associations between possible risk factors and infection levels. The prevalence and spatial distribution of F. hepatica was relatively stable, with small interannual differences in prevalence and location of clusters. The logistic regression model based on both management and climatic/environmental factors included the factors: annual rainfall, mowing of pastures, proportion of grazed grass in the diet and length of grazing season as significant predictors and described the spatial distribution of F. hepatica better than the model based on climatic/environmental factors only (annual rainfall, elevation and slope, soil type), with an Area Under the Curve of the Receiver Operating Characteristic of 0.68 compared with 0.62. The results indicate that in temperate climate zones without large climatic

  11. A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.

    Science.gov (United States)

    Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

    2014-03-01

    GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

  12. Climate Change Adaptation via U.S. Land Use Transitions: A Spatial Econometric Analysis

    OpenAIRE

    Cho, Sung Ju; McCarl, Bruce A.; Wu, Ximing

    2015-01-01

    Climate change, coupled with biofuels development and other factors may well be changing US land usage patterns. We use a spatial econometric approach to estimate the drivers of US land use transitions in recent years. We consider transitions between six major land uses: agricultural land, forest, grassland, water, urban, and other uses. To examine drivers, we use a two-step linearized, spatial, multinomial logit model and estimate land use transition probabilities. Our results indicate that ...

  13. Spatial memory tasks in rodents: what do they model?

    Science.gov (United States)

    Morellini, Fabio

    2013-10-01

    The analysis of spatial learning and memory in rodents is commonly used to investigate the mechanisms underlying certain forms of human cognition and to model their dysfunction in neuropsychiatric and neurodegenerative diseases. Proper interpretation of rodent behavior in terms of spatial memory and as a model of human cognitive functions is only possible if various navigation strategies and factors controlling the performance of the animal in a spatial task are taken into consideration. The aim of this review is to describe the experimental approaches that are being used for the study of spatial memory in rats and mice and the way that they can be interpreted in terms of general memory functions. After an introduction to the classification of memory into various categories and respective underlying neuroanatomical substrates, I explain the concept of spatial memory and its measurement in rats and mice by analysis of their navigation strategies. Subsequently, I describe the most common paradigms for spatial memory assessment with specific focus on methodological issues relevant for the correct interpretation of the results in terms of cognitive function. Finally, I present recent advances in the use of spatial memory tasks to investigate episodic-like memory in mice.

  14. Spatial correlation analysis of urban traffic state under a perspective of community detection

    Science.gov (United States)

    Yang, Yanfang; Cao, Jiandong; Qin, Yong; Jia, Limin; Dong, Honghui; Zhang, Aomuhan

    2018-05-01

    Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.

  15. Micro-epidemiology and spatial heterogeneity of P. vivax parasitaemia in riverine communities of the Peruvian Amazon: A multilevel analysis.

    Science.gov (United States)

    Carrasco-Escobar, Gabriel; Gamboa, Dionicia; Castro, Marcia C; Bangdiwala, Shrikant I; Rodriguez, Hugo; Contreras-Mancilla, Juan; Alava, Freddy; Speybroeck, Niko; Lescano, Andres G; Vinetz, Joseph M; Rosas-Aguirre, Angel; Llanos-Cuentas, Alejandro

    2017-08-14

    Malaria has steadily increased in the Peruvian Amazon over the last five years. This study aimed to determine the parasite prevalence and micro-geographical heterogeneity of Plasmodium vivax parasitaemia in communities of the Peruvian Amazon. Four cross-sectional active case detection surveys were conducted between May and July 2015 in four riverine communities in Mazan district. Analysis of 2785 samples of 820 individuals nested within 154 households for Plasmodium parasitaemia was carried out using light microscopy and qPCR. The spatio-temporal distribution of Plasmodium parasitaemia, dominated by P. vivax, was shown to cluster at both household and community levels. Of enrolled individuals, 47% had at least one P. vivax parasitaemia and 10% P. falciparum, by qPCR, both of which were predominantly sub-microscopic and asymptomatic. Spatial analysis detected significant clustering in three communities. Our findings showed that communities at small-to-moderate spatial scales differed in P. vivax parasite prevalence, and multilevel Poisson regression models showed that such differences were influenced by factors such as age, education, and location of households within high-risk clusters, as well as factors linked to a local micro-geographic context, such as travel and occupation. Complex transmission patterns were found to be related to human mobility among communities in the same micro-basin.

  16. Determining the Number of Factors in P-Technique Factor Analysis

    Science.gov (United States)

    Lo, Lawrence L.; Molenaar, Peter C. M.; Rovine, Michael

    2017-01-01

    Determining the number of factors is a critical first step in exploratory factor analysis. Although various criteria and methods for determining the number of factors have been evaluated in the usual between-subjects R-technique factor analysis, there is still question of how these methods perform in within-subjects P-technique factor analysis. A…

  17. Socio-ecological factors and hand, foot and mouth disease in dry climate regions: a Bayesian spatial approach in Gansu, China

    Science.gov (United States)

    Gou, Faxiang; Liu, Xinfeng; Ren, Xiaowei; Liu, Dongpeng; Liu, Haixia; Wei, Kongfu; Yang, Xiaoting; Cheng, Yao; Zheng, Yunhe; Jiang, Xiaojuan; Li, Juansheng; Meng, Lei; Hu, Wenbiao

    2017-01-01

    The influence of socio-ecological factors on hand, foot and mouth disease (HFMD) were explored in this study using Bayesian spatial modeling and spatial patterns identified in dry regions of Gansu, China. Notified HFMD cases and socio-ecological data were obtained from the China Information System for Disease Control and Prevention, Gansu Yearbook and Gansu Meteorological Bureau. A Bayesian spatial conditional autoregressive model was used to quantify the effects of socio-ecological factors on the HFMD and explore spatial patterns, with the consideration of its socio-ecological effects. Our non-spatial model suggests temperature (relative risk (RR) 1.15, 95 % CI 1.01-1.31), GDP per capita (RR 1.19, 95 % CI 1.01-1.39) and population density (RR 1.98, 95 % CI 1.19-3.17) to have a significant effect on HFMD transmission. However, after controlling for spatial random effects, only temperature (RR 1.25, 95 % CI 1.04-1.53) showed significant association with HFMD. The spatial model demonstrates temperature to play a major role in the transmission of HFMD in dry regions. Estimated residual variation after taking into account the socio-ecological variables indicated that high incidences of HFMD were mainly clustered in the northwest of Gansu. And, spatial structure showed a unique distribution after taking account of socio-ecological effects.

  18. Cross-Industry Spatially Localized Innovation Networks

    Directory of Open Access Journals (Sweden)

    Aleksandr Evseevich Karlik

    2016-12-01

    Full Text Available This article’s objective is to develop conceptual approach to the study of key decision-making factors of cross-industry spatially localized innovation networks regularities by the application of quantitative and qualitative data of St. Petersburg Innovation and Technology Cluster of Machinery Manufacturing and Metalworking. The paper is based on the previous research findings which conclude that such networks have a set of opportunities and constraints for innovation. The hypothesis is that in the clusters, representing a special type of these networks, the spatial proximity partly offsets the negative impact of industrial distance. The authors propose a structural and logical model of strategic decision-making to analyze these effects on innovation. They specify network’s influences on performance: cognitive diversity; knowledge and expertise; structural autonomy and equivalence. The model is applied to spatially localized cross-industry cluster and then improved in accordance with the obtained results for accounting resource flows. It allowed to take into account the dynamics of innovation activity and to develop the practical implications in the particular business context. The analysis identified the peculiarities of spatially localized crossindustry innovation cooperation in perspective of the combinations of tangible resources, information and other intangible resources for the renewal of mature industries. The research results can be used in business as well as in industrial and regional economic policy. In the conclusion, the article outlines future research directions: a comprehensive empirical study with the analysis of data on the factors of cross-industry cooperation which were identified in this paper with testing of causal relations; the developing an approach to the study of spatially localized networks based on the exchange of primary resources in the economic system stability framework.

  19. A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis

    Directory of Open Access Journals (Sweden)

    An Gie Yong

    2013-10-01

    Full Text Available The following paper discusses exploratory factor analysis and gives an overview of the statistical technique and how it is used in various research designs and applications. A basic outline of how the technique works and its criteria, including its main assumptions are discussed as well as when it should be used. Mathematical theories are explored to enlighten students on how exploratory factor analysis works, an example of how to run an exploratory factor analysis on SPSS is given, and finally a section on how to write up the results is provided. This will allow readers to develop a better understanding of when to employ factor analysis and how to interpret the tables and graphs in the output.

  20. Applying spatial analysis tools in public health: an example using SaTScan to detect geographic targets for colorectal cancer screening interventions.

    Science.gov (United States)

    Sherman, Recinda L; Henry, Kevin A; Tannenbaum, Stacey L; Feaster, Daniel J; Kobetz, Erin; Lee, David J

    2014-03-20

    Epidemiologists are gradually incorporating spatial analysis into health-related research as geocoded cases of disease become widely available and health-focused geospatial computer applications are developed. One health-focused application of spatial analysis is cluster detection. Using cluster detection to identify geographic areas with high-risk populations and then screening those populations for disease can improve cancer control. SaTScan is a free cluster-detection software application used by epidemiologists around the world to describe spatial clusters of infectious and chronic disease, as well as disease vectors and risk factors. The objectives of this article are to describe how spatial analysis can be used in cancer control to detect geographic areas in need of colorectal cancer screening intervention, identify issues commonly encountered by SaTScan users, detail how to select the appropriate methods for using SaTScan, and explain how method selection can affect results. As an example, we used various methods to detect areas in Florida where the population is at high risk for late-stage diagnosis of colorectal cancer. We found that much of our analysis was underpowered and that no single method detected all clusters of statistical or public health significance. However, all methods detected 1 area as high risk; this area is potentially a priority area for a screening intervention. Cluster detection can be incorporated into routine public health operations, but the challenge is to identify areas in which the burden of disease can be alleviated through public health intervention. Reliance on SaTScan's default settings does not always produce pertinent results.

  1. Environmentally Friendly Concept in Spatial Regulation

    Directory of Open Access Journals (Sweden)

    T Taryono

    2004-01-01

    Full Text Available Spatial order of a region include purpose of structure and interrelatedness of spatial order which as a unity of development mechanism. Spatial order can’t be part from law basic that is UUD’45 and GBHN, in order to the management can be integrated and keep the environment. Spatial order also take note of physical factor and non physical factor. Physical factor consist of soil, water, flora, and fauna. Non physical factors consist of environment, social, economic, and soon. The principle of arrangement of spatial order in a region include national region, regional and local, and a region as an administrative baoundary, that is local government authority, like province, district, subdistrict, and village. The effort for spatial ordering of the environment, for example ordering resource, arrangement of allocation and location, arrangement of environmental aesthetic  and arrangement of environmental quality.

  2. Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

    Science.gov (United States)

    Alonso, Carmelo; Tarquis, Ana M.; Zúñiga, Ignacio; Benito, Rosa M.

    2017-03-01

    Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution. From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.

  3. Global Rice Watch: Spatial-temporal dynamics, driving factors, and impacts of paddy rice agriculture in the world

    Science.gov (United States)

    Xiao, X.; Dong, J.; Zhang, G.; Xin, F.; Li, X.

    2017-12-01

    Paddy rice croplands account for more than 12% of the global cropland areas, and provide food to feed more than 50% of the world population. Spatial patterns and temporal dynamics of paddy rice croplands have changed remarkably in the past decades, driven by growing human population and their changing diet structure, land use (e.g., urbanization, industrialization), climate, markets, and technologies. In this presentation, we will provide a comprehensive review of our current knowledge on (1) the spatial patterns and temporal dynamics of paddy rice croplands from agricultural statistics data and remote sensing approaches; (2) major driving factors for the observed changes in paddy rice areas, including social, economic, climate, land use, markets, crop breeding technology, and farming technology; and (3) major impacts on atmospheric methane concentration, land surface temperature, water resources and use, and so on. We will highlight the results from a few case studies in China and monsoon Asia. We will also call for a global synthesis analysis of paddy rice agriculture, and invite researchers to join the effort to write and edit a book that provides comprehensive and updated knowledge on paddy rice agriculture.

  4. Spatial analysis on human brucellosis incidence in mainland China: 2004–2010

    Science.gov (United States)

    Zhang, Junhui; Yin, Fei; Zhang, Tao; Yang, Chao; Zhang, Xingyu; Feng, Zijian; Li, Xiaosong

    2014-01-01

    Objectives China has experienced a sharply increasing rate of human brucellosis in recent years. Effective spatial monitoring of human brucellosis incidence is very important for successful implementation of control and prevention programmes. The purpose of this paper is to apply exploratory spatial data analysis (ESDA) methods and the empirical Bayes (EB) smoothing technique to monitor county-level incidence rates for human brucellosis in mainland China from 2004 to 2010 by examining spatial patterns. Methods ESDA methods were used to characterise spatial patterns of EB smoothed incidence rates for human brucellosis based on county-level data obtained from the China Information System for Disease Control and Prevention (CISDCP) in mainland China from 2004 to 2010. Results EB smoothed incidence rates for human brucellosis were spatially dependent during 2004–2010. The local Moran test identified significantly high-risk clusters of human brucellosis (all p values brucellosis incidence. PMID:24713215

  5. Spatially-Heterodyned Holography

    Science.gov (United States)

    Thomas, Clarence E [Knoxville, TN; Hanson, Gregory R [Clinton, TN

    2006-02-21

    A method of recording a spatially low-frequency heterodyne hologram, including spatially heterodyne fringes for Fourier analysis, includes: splitting a laser beam into a reference beam and an object beam; interacting the object beam with an object; focusing the reference beam and the object beam at a focal plane of a digital recorder to form a spatially low-frequency heterodyne hologram including spatially heterodyne fringes for Fourier analysis; digital recording the spatially low-frequency heterodyne hologram; Fourier transforming axes of the recorded spatially low-frequency heterodyne hologram including spatially heterodyne fringes in Fourier space to sit on top of a heterodyne carrier frequency defined by an angle between the reference beam and the object beam; cutting off signals around an origin; and performing an inverse Fourier transform.

  6. Spatial analysis of the Rio de Janeiro metropolitan area and social and environmental management issues

    DEFF Research Database (Denmark)

    Ribeiro, Gustavo

    2005-01-01

    and infrastructural data. Through these three levels of spatial analysis it is possible to develop and to support a more comprehensible study of urban development of the Rio de Janeiro Metropolitan Area. The aim of this study is (a) to develop an alternative spatial analysis leading to a more comprehensive...... understanding of the urban development process and its correlation not just with political-administrative borders but also to ecological systems: (b) to identify the correlations between infrastructure and socio-economical data in the Rio de Janeiro Metropolitan Area (c) to evaluate urban development dynamics...... in the period between 1990 and 2000, based on the application of the above-mentioned data to the three spatial levels in question. This paper highlights the role of the hydrographical systems of the Rio de Janeiro Metropolitan Area as an important spatial unit of analysis to understand the metropolitan urban...

  7. Infant mortality in South Africa - distribution, associations and policy implications, 2007: an ecological spatial analysis

    Directory of Open Access Journals (Sweden)

    Sartorius Benn KD

    2011-11-01

    antenatal HIV sero-prevalence, previous sibling mortality and maternal mortality were found to be the most attributable respectively. Conclusions This study demonstrates the usefulness of advanced spatial analysis to both quantify excess infant mortality risk at the lowest administrative unit, as well as the use of Bayesian modelling to quantify determinant significance given spatial correlation. The "novel" integration of determinant prevalence at the sub-district and coefficient estimates to estimate attributable fractions further elucidates the "high impact" factors in particular areas and has considerable potential to be applied in other locations. The usefulness of the paper, therefore, not only suggests where to intervene geographically, but also what specific interventions policy makers should prioritize in order to reduce the infant mortality burden in specific administration areas.

  8. Explorative analysis of long time series of very high resolution spatial rainfall

    DEFF Research Database (Denmark)

    Thomassen, Emma Dybro; Sørup, Hjalte Jomo Danielsen; Scheibel, Marc

    2017-01-01

    . For each method a set of 17 variables are used to describe the properties of each event, e.g. duration, maximum volumes, spatial coverage and heterogeneity, and movement of cells. A total of 5-9 dimensions can be found in the data, which can be interpreted as a rough indication of how many independent...... simple scaling across the set of variables, i.e. the level of each variable varies signicantly, but not the overall structure of the spatial precipitation. The analysis show that there is a good potential for making a spatial weather generator for high spatio-temporal precipitation for precipitation...

  9. Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas.

    Science.gov (United States)

    Bao, Jie; Liu, Pan; Yu, Hao; Xu, Chengcheng

    2017-09-01

    The primary objective of this study was to investigate how to incorporate human activity information in spatial analysis of crashes in urban areas using Twitter check-in data. This study used the data collected from the City of Los Angeles in the United States to illustrate the procedure. The following five types of data were collected: crash data, human activity data, traditional traffic exposure variables, road network attributes and social-demographic data. A web crawler by Python was developed to collect the venue type information from the Twitter check-in data automatically. The human activities were classified into seven categories by the obtained venue types. The collected data were aggregated into 896 Traffic Analysis Zones (TAZ). Geographically weighted regression (GWR) models were developed to establish a relationship between the crash counts reported in a TAZ and various contributing factors. Comparative analyses were conducted to compare the performance of GWR models which considered traditional traffic exposure variables only, Twitter-based human activity variables only, and both traditional traffic exposure and Twitter-based human activity variables. The model specification results suggested that human activity variables significantly affected the crash counts in a TAZ. The results of comparative analyses suggested that the models which considered both traditional traffic exposure and human activity variables had the best goodness-of-fit in terms of the highest R 2 and lowest AICc values. The finding seems to confirm the benefits of incorporating human activity information in spatial analysis of crashes using Twitter check-in data. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Spatial distribution patterns of soil mite communities and their relationships with edaphic factors in a 30-year tillage cornfield in northeast China.

    Science.gov (United States)

    Liu, Jie; Gao, Meixiang; Liu, Jinwen; Guo, Yuxi; Liu, Dong; Zhu, Xinyu; Wu, Donghui

    2018-01-01

    Spatial distribution is an important topic in community ecology and a key to understanding the structure and dynamics of populations and communities. However, the available information related to the spatial patterns of soil mite communities in long-term tillage agroecosystems remains insufficient. In this study, we examined the spatial patterns of soil mite communities to explain the spatial relationships between soil mite communities and soil parameters. Soil fauna were sampled three times (August, September and October 2015) at 121 locations arranged regularly within a 400 m × 400 m monitoring plot. Additionally, we estimated the physical and chemical parameters of the same sampling locations. The distribution patterns of the soil mite community and the edaphic parameters were analyzed using a range of geostatistical tools. Moran's I coefficient showed that, during each sampling period, the total abundance of the soil mite communities and the abundance of the dominant mite populations were spatially autocorrelated. The soil mite communities demonstrated clear patchy distribution patterns within the study plot. These patterns were sampling period-specific. Cross-semivariograms showed both negative and positive cross-correlations between soil mite communities and environmental factors. Mantel tests showed a significant and positive relationship between soil mite community and soil organic matter and soil pH only in August. This study demonstrated that in the cornfield, the soil mite distribution exhibited strong or moderate spatial dependence, and the mites formed patches with sizes less than one hundred meters. In addition, in this long-term tillage agroecosystem, soil factors had less influence on the observed pattern of soil mite communities. Further experiments that take into account human activity and spatial factors should be performed to study the factors that drive the spatial distribution of soil microarthropods.

  11. Spatial pattern of heavy metals accumulation risk in urban soils of Beijing and its influencing factors

    International Nuclear Information System (INIS)

    Liu, Rui; Wang, Meie; Chen, Weiping; Peng, Chi

    2016-01-01

    Accumulations of heavy metals in urban soils are highly spatial heterogeneity and affected by multiple factors including soil properties, land use and pattern, population and climatic conditions. We studied accumulation risks of Cd, Cu, Pb and Zn in unban soils of Beijing and their influencing based on the regression tree analysis and a GIS-based overlay model. Result shows that Zinc causes the most extensive soil pollution and Cu result in the most acute soil pollution. The soil's organic carbon content and CEC and population growth are the most significant factors affecting heavy metal accumulation. Other influence factors in land use pattern, urban landscape, and wind speed also contributed, but less pronounced. The soils in areas with higher degree of urbanization and surrounded by intense vehicular traffics have higher accumulation risk of Cd, Cu, Pb, and Zn. - Highlights: • Zn accumulations were the most extensive and Cu accumulations were the most acute. • Accumulations of Cd, Cu, Pb and Zn in urban soils were caused by different sets of influence factors. • Soil's organic carbon content and CEC and population growth were the most significant factors. • Accumulation risks were highly related with urbanization level and human activities. - A combined approach of employing geographical information systems and regression tree analyses identify the potential risks of accumulation Cd, Cu, Pb, and Zn in urban soils according to soil properties, urban land use patterns, urban landscape, demographics, and microclimatic conditions.

  12. SRXRF analysis with spatial resolution of dental calculus

    International Nuclear Information System (INIS)

    Sanchez, Hector Jorge; Perez, Carlos Alberto; Grenon, Miriam

    2000-01-01

    This work presents elemental-composition studies of dental calculus by X-ray fluorescence analysis using synchrotron radiation. The intrinsic characteristics of synchrotron light allow for a semi-quantitative analysis with spatial resolution. The experiments were carried out in the high-vacuum station of the XRF beamline at the Synchrotron Light National Laboratory (Campinas, Brazil). All the measurements were performed in conventional geometry (45 deg. + 45 deg.) and the micro-collimation was attained via a pair of orthogonal slits mounted in the beamline. In this way, pixels of 50 μmx50 μm were obtained keeping a high flux of photons on the sample. Samples of human dental calculus were measured in different positions along their growing axis, in order to determine variations of the compositions in the pattern of deposit. Intensity ratios of minor elements and traces were obtained, and linear profiles and surface distributions were determined. As a general summary, we can conclude that μXRF experiments with spatial resolution on dental calculus are feasible with simple collimation and adequate positioning systems, keeping a high flux of photon. These results open interesting perspectives for the future station of the line, devoted to μXRF, which will reach resolutions of the order of 10 μm

  13. SRXRF analysis with spatial resolution of dental calculus

    Science.gov (United States)

    Sánchez, Héctor Jorge; Pérez, Carlos Alberto; Grenón, Miriam

    2000-09-01

    This work presents elemental-composition studies of dental calculus by X-ray fluorescence analysis using synchrotron radiation. The intrinsic characteristics of synchrotron light allow for a semi-quantitative analysis with spatial resolution. The experiments were carried out in the high-vacuum station of the XRF beamline at the Synchrotron Light National Laboratory (Campinas, Brazil). All the measurements were performed in conventional geometry (45°+45°) and the micro-collimation was attained via a pair of orthogonal slits mounted in the beamline. In this way, pixels of 50 μm×50 μm were obtained keeping a high flux of photons on the sample. Samples of human dental calculus were measured in different positions along their growing axis, in order to determine variations of the compositions in the pattern of deposit. Intensity ratios of minor elements and traces were obtained, and linear profiles and surface distributions were determined. As a general summary, we can conclude that μXRF experiments with spatial resolution on dental calculus are feasible with simple collimation and adequate positioning systems, keeping a high flux of photon. These results open interesting perspectives for the future station of the line, devoted to μXRF, which will reach resolutions of the order of 10 μm.

  14. Spatial analysis of factors influencing long-term stress in the grizzly bear (Ursus arctos population of Alberta, Canada.

    Directory of Open Access Journals (Sweden)

    Mathieu L Bourbonnais

    Full Text Available Non-invasive measures for assessing long-term stress in free ranging mammals are an increasingly important approach for understanding physiological responses to landscape conditions. Using a spatially and temporally expansive dataset of hair cortisol concentrations (HCC generated from a threatened grizzly bear (Ursus arctos population in Alberta, Canada, we quantified how variables representing habitat conditions and anthropogenic disturbance impact long-term stress in grizzly bears. We characterized spatial variability in male and female HCC point data using kernel density estimation and quantified variable influence on spatial patterns of male and female HCC stress surfaces using random forests. Separate models were developed for regions inside and outside of parks and protected areas to account for substantial differences in anthropogenic activity and disturbance within the study area. Variance explained in the random forest models ranged from 55.34% to 74.96% for males and 58.15% to 68.46% for females. Predicted HCC levels were higher for females compared to males. Generally, high spatially continuous female HCC levels were associated with parks and protected areas while low-to-moderate levels were associated with increased anthropogenic disturbance. In contrast, male HCC levels were low in parks and protected areas and low-to-moderate in areas with increased anthropogenic disturbance. Spatial variability in gender-specific HCC levels reveal that the type and intensity of external stressors are not uniform across the landscape and that male and female grizzly bears may be exposed to, or perceive, potential stressors differently. We suggest observed spatial patterns of long-term stress may be the result of the availability and distribution of foods related to disturbance features, potential sexual segregation in available habitat selection, and may not be influenced by sources of mortality which represent acute traumas. In this wildlife

  15. Spatial pattern of Amazonian timber species using cartesian and spatial coordinates method

    Directory of Open Access Journals (Sweden)

    Tiago Monteiro Condé

    2016-06-01

    Full Text Available Geographic information system (GIS applied to forest analysis permit the recognition and analysis of spatial patterns of species in two and three dimensional. The aim of this study to demonstrate the efficiency of cartesian and spatial coordinates method (MCCE, method of correcting UTM coordinates of trees location in accordance with the location of field or Cartesian (X ,Y, combined with natural neighbor index (ANND in recognition and analysis of spatial distribution patterns of four commercial timber species in forest management in Caracaraí, Roraima State, Brazil. Simulations were performed on 9 ha, divided into 100 plots of 100 m2 each. Collected data were DBH > 10 cm, commercial and total heights, cartesian coordinates (X,Y and spatial coordinates (UTM. Random spatial patterns were observed in Eschweilera bracteosa and Manilkara huberi. The dispersed and rare spatial patterns were observed in Dinizia excelsa and Cedrelinga cateniformis. MCCE proved to be an efficient method in the recognition and analysis of spatial patterns of native species from Amazon rain forest, as forest planning becomes easier by 2D and 3D simulations.

  16. Spatial Analysis of Political Capital Citation Using Remote Sensing ...

    African Journals Online (AJOL)

    Spatial Analysis of Political Capital Citation Using Remote Sensing and GIS; A Case Study of Lokoja. ... The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader). If you would like more information about how to print, save, ...

  17. Denitrification potential of riparian soils in relation to multiscale spatial environmental factors: a case study of a typical watershed, China.

    Science.gov (United States)

    Wei, Jianbing; Feng, Hao; Cheng, Quanguo; Gao, Shiqian; Liu, Haiyan

    2017-02-01

    The objective of this study was to test the hypothesis that environmental regulators of riparian zone soil denitrification potential differ according to spatial scale within a watershed; consequently, a second objective was to provide spatial strategies for conserving and restoring the purification function of runoff in riparian ecosystems. The results show that soil denitrification in riparian zones was more heterogeneous at the profile scale than at the cross-section and landscape scales. At the profile scale, biogeochemical factors (including soil total organic carbon, total nitrogen, and nitrate-nitrogen) were the major direct regulators of the spatial distribution of soil denitrification enzyme activity (DEA). At the cross-section scale, factors included distance from river bank and vegetation density, while landscape-scale factors, including topographic index, elevation, and land use types, indirectly regulated the spatial distribution of DEA. At the profile scale, soil DEA was greatest in the upper soil layers. At the cross-section scale, maximum soil DEA occurred in the mid-part of the riparian zone. At the landscape scale, soil DEA showed an increasing trend towards downstream sites, except for those in urbanized areas.

  18. An easy guide to factor analysis

    CERN Document Server

    Kline, Paul

    2014-01-01

    Factor analysis is a statistical technique widely used in psychology and the social sciences. With the advent of powerful computers, factor analysis and other multivariate methods are now available to many more people. An Easy Guide to Factor Analysis presents and explains factor analysis as clearly and simply as possible. The author, Paul Kline, carefully defines all statistical terms and demonstrates step-by-step how to work out a simple example of principal components analysis and rotation. He further explains other methods of factor analysis, including confirmatory and path analysis, a

  19. Bovine spongiform encephalopathy and spatial analysis of the feed industry.

    Science.gov (United States)

    Paul, Mathilde; Abrial, David; Jarrige, Nathalie; Rican, Stéphane; Garrido, Myriam; Calavas, Didier; Ducrot, Christian

    2007-06-01

    In France, despite the ban of meat-and-bone meal (MBM) in cattle feed, bovine spongiform encephalopathy (BSE) was detected in hundreds of cattle born after the ban. To study the role of MBM, animal fat, and dicalcium phosphate on the risk for BSE after the feed ban, we conducted a spatial analysis of the feed industry. We used data from 629 BSE cases as well as data on use of each byproduct and market area of the feed factories. We mapped risk for BSE in 951 areas supplied by the same factories and connection with use of byproducts. A disease map of BSE with covariates was built with the hierarchical Bayesian modeling methods, based on Poisson distribution with spatial smoothing. Only use of MBM was spatially linked to risk for BSE, which highlights cross-contamination as the most probable source of infection after the feed ban.

  20. Strategic Placing of Field Hospitals Using Spatial Analysis

    OpenAIRE

    Rydén, Magnus

    2011-01-01

    Humanitarian help organisations today may benefit on improving their location analysis when placing field hospitals in countries hit by a disasters or catastrophe. The main objective of this thesis is to develop and evaluate a spatial decision support method for strategic placing of field hospitals for two time perspectives, long term (months) and short term (weeks). Specifically, the possibility of combining existing infrastructure and satellite data is examined to derive a suitability map f...

  1. Development of the Spatial Ability Test for Middle School Students

    Science.gov (United States)

    Yildiz, Sevda Göktepe; Özdemir, Ahmet Sükrü

    2017-01-01

    The purpose of this study was to develop a test to determine spatial ability of middle school students. The participants were 704 middle school students (6th, 7th and 8th grade) who were studying at different schools from Istanbul. Item analysis, exploratory and confirmatory factor analysis, reliability analysis were used to analyse the data.…

  2. Structural studies of formic acid using partial form-factor analysis

    International Nuclear Information System (INIS)

    Swan, G.; Dore, J.C.; Bellissent-Funel, M.C.

    1993-01-01

    Neutron diffraction measurements have been made of liquid formic acid using H/D isotopic substitution. Data are recorded for samples of DCOOD, HCOOD and a (H/D)COOD mixture (α D =0.36). A first-order difference method is used to determine the intra-molecular contribution through the introduction of a partial form-factor analysis technique incorporating a hydrogen-bond term. The method improves the sensitivity of the parameters defining the molecular geometry and avoids some of the ambiguities arising from terms involving spatial overlap of inter- and intra-molecular features. The possible application to other systems is briefly reviewed. (authors). 8 figs., 2 tabs., 8 refs

  3. Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach.

    Science.gov (United States)

    Wong, Man Sing; Ho, Hung Chak; Yang, Lin; Shi, Wenzhong; Yang, Jinxin; Chan, Ta-Chien

    2017-07-24

    Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city. In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city. The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation. Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

  4. Spatial analysis of toxic emissions in LCA: a sub-continental nested USEtox model with freshwater archetypes.

    Science.gov (United States)

    Kounina, Anna; Margni, Manuele; Shaked, Shanna; Bulle, Cécile; Jolliet, Olivier

    2014-08-01

    This paper develops continent-specific factors for the USEtox model and analyses the accuracy of different model architectures, spatial scales and archetypes in evaluating toxic impacts, with a focus on freshwater pathways. Inter-continental variation is analysed by comparing chemical fate and intake fractions between sub-continental zones of two life cycle impact assessment models: (1) the nested USEtox model parameterized with sub-continental zones and (2) the spatially differentiated IMPACTWorld model with 17 interconnected sub-continental regions. Substance residence time in water varies by up to two orders of magnitude among the 17 zones assessed with IMPACTWorld and USEtox, and intake fraction varies by up to three orders of magnitude. Despite this variation, the nested USEtox model succeeds in mimicking the results of the spatially differentiated model, with the exception of very persistent volatile pollutants that can be transported to polar regions. Intra-continental variation is analysed by comparing fate and intake fractions modelled with the a-spatial (one box) IMPACT Europe continental model vs. the spatially differentiated version of the same model. Results show that the one box model might overestimate chemical fate and characterisation factors for freshwater eco-toxicity of persistent pollutants by up to three orders of magnitude for point source emissions. Subdividing Europe into three archetypes, based on freshwater residence time (how long it takes water to reach the sea), improves the prediction of fate and intake fractions for point source emissions, bringing them within a factor five compared to the spatial model. We demonstrated that a sub-continental nested model such as USEtox, with continent-specific parameterization complemented with freshwater archetypes, can thus represent inter- and intra-continental spatial variations, whilst minimizing model complexity. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. SPATIAL ANALYSIS TO SUPPORT GEOGRAPHIC TARGETING OF GENOTYPES TO ENVIRONMENTS

    Directory of Open Access Journals (Sweden)

    Glenn eHyman

    2013-03-01

    Full Text Available Crop improvement efforts have benefited greatly from advances in available data, computing technology and methods for targeting genotypes to environments. These advances support the analysis of genotype by environment interactions to understand how well a genotype adapts to environmental conditions. This paper reviews the use of spatial analysis to support crop improvement research aimed at matching genotypes to their most appropriate environmental niches. Better data sets are now available on soils, weather and climate, elevation, vegetation, crop distribution and local conditions where genotypes are tested in experimental trial sites. The improved data are now combined with spatial analysis methods to compare environmental conditions across sites, create agro-ecological region maps and assess environment change. Climate, elevation and vegetation data sets are now widely available, supporting analyses that were much more difficult even five or ten years ago. While detailed soil data for many parts of the world remains difficult to acquire for crop improvement studies, new advances in digital soil mapping are likely to improve our capacity. Site analysis and matching and regional targeting methods have advanced in parallel to data and technology improvements. All these developments have increased our capacity to link genotype to phenotype and point to a vast potential to improve crop adaptation efforts.

  6. A Spatial Analysis of the Potato Cyst Nematode Globodera pallida in Idaho.

    Science.gov (United States)

    Dandurand, Louise-Marie; Contina, Jean Bertrand; Knudsen, Guy R

    2018-03-13

    The potato cyst nematode (PCN), Globodera pallida, is a globally regulated and quarantine potato pest. It was detected for the first time in the U.S. in the state of Idaho in 2006. A spatial analysis was performed to: (i) understand the spatial arrangement of PCN infested fields in southern Idaho using spatial point pattern analysis; and (ii) evaluate the potential threat of PCN for entry to new areas using spatial interpolation techniques. Data point locations, cyst numbers and egg viability values for each infested field were collected by USDA-APHIS during 2006-2014. Results showed the presence of spatially clustered PCN infested fields (P = 0.003). We determined that the spread of PCN grew in diameter from the original center of infestation toward the southwest as an ellipsoidal-shaped cluster. Based on the aggregated spatial pattern of distribution and the low extent level of PCN infested fields in southern Idaho, we determined that PCN spread followed a contagion effect scenario, where nearby infested fields contributed to the infestation of new fields, probably through soil contaminated agricultural equipment or tubers. We determined that the recent PCN presence in southern Idaho is unlikely to be associated with new PCN entry from outside the state of Idaho. The relative aggregation of PCN infested fields, the low number of cysts recovered, and the low values in egg viability facilitate quarantine activities and confine this pest to a small area, which, in 2017, is estimated to be 1,233 hectares. The tools and methods provided in this study should facilitate comprehensive approaches to improve PCN control and eradication programs as well as to raise public awareness about this economically important potato pest.

  7. Schools, Air Pollution, and Active Transportation: An Exploratory Spatial Analysis of Calgary, Canada.

    Science.gov (United States)

    Bertazzon, Stefania; Shahid, Rizwan

    2017-07-25

    An exploratory spatial analysis investigates the location of schools in Calgary (Canada) in relation to air pollution and active transportation options. Air pollution exhibits marked spatial variation throughout the city, along with distinct spatial patterns in summer and winter; however, all school locations lie within low to moderate pollution levels. Conversely, the study shows that almost half of the schools lie in low walkability locations; likewise, transitability is low for 60% of schools, and only bikability is widespread, with 93% of schools in very bikable locations. School locations are subsequently categorized by pollution exposure and active transportation options. This analysis identifies and maps schools according to two levels of concern: schools in car-dependent locations and relatively high pollution; and schools in locations conducive of active transportation, yet exposed to relatively high pollution. The findings can be mapped and effectively communicated to the public, health practitioners, and school boards. The study contributes with an explicitly spatial approach to the intra-urban public health literature. Developed for a moderately polluted city, the methods can be extended to more severely polluted environments, to assist in developing spatial public health policies to improve respiratory outcomes, neurodevelopment, and metabolic and attention disorders in school-aged children.

  8. Schools, Air Pollution, and Active Transportation: An Exploratory Spatial Analysis of Calgary, Canada

    Science.gov (United States)

    Bertazzon, Stefania; Shahid, Rizwan

    2017-01-01

    An exploratory spatial analysis investigates the location of schools in Calgary (Canada) in relation to air pollution and active transportation options. Air pollution exhibits marked spatial variation throughout the city, along with distinct spatial patterns in summer and winter; however, all school locations lie within low to moderate pollution levels. Conversely, the study shows that almost half of the schools lie in low walkability locations; likewise, transitability is low for 60% of schools, and only bikability is widespread, with 93% of schools in very bikable locations. School locations are subsequently categorized by pollution exposure and active transportation options. This analysis identifies and maps schools according to two levels of concern: schools in car-dependent locations and relatively high pollution; and schools in locations conducive of active transportation, yet exposed to relatively high pollution. The findings can be mapped and effectively communicated to the public, health practitioners, and school boards. The study contributes with an explicitly spatial approach to the intra-urban public health literature. Developed for a moderately polluted city, the methods can be extended to more severely polluted environments, to assist in developing spatial public health policies to improve respiratory outcomes, neurodevelopment, and metabolic and attention disorders in school-aged children. PMID:28757577

  9. Residual analysis for spatial point processes

    DEFF Research Database (Denmark)

    Baddeley, A.; Turner, R.; Møller, Jesper

    We define residuals for point process models fitted to spatial point pattern data, and propose diagnostic plots based on these residuals. The techniques apply to any Gibbs point process model, which may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Ou...... or covariate effects. Q-Q plots of the residuals are effective in diagnosing interpoint interaction. Some existing ad hoc statistics of point patterns (quadrat counts, scan statistic, kernel smoothed intensity, Berman's diagnostic) are recovered as special cases....

  10. Empirical Research on Spatial Diffusion Process of Knowledge Spillovers

    Science.gov (United States)

    Jin, Xuehui

    2018-02-01

    Firstly, this paper gave a brief review of the core issues of previous studies on spatial distribution of knowledge spillovers. That laid the theoretical foundation for further research. Secondly, this paper roughly described the diffusion process of solar patents in Bejing-Tianjin-Hebei and the Pearl River Delta regions by means of correlation analysis based on patent information of the application date and address of patentee. After that, this paper introduced the variables of spatial distance, knowledge absorptive capacity, knowledge gap and pollution control and built the empirical model of patent, and then collecting data to test them. The results showed that knowledge absorptive capacity was the most significant factor than the other three, followed by the knowledge gap. The influence of spatial distance on knowledge spillovers was limited and the most weak influence factor was pollution control.

  11. Spatial heterogeneity analysis of brain activation in fMRI

    Directory of Open Access Journals (Sweden)

    Lalit Gupta

    2014-01-01

    Full Text Available In many brain diseases it can be qualitatively observed that spatial patterns in blood oxygenation level dependent (BOLD activation maps appear more (diffusively distributed than in healthy controls. However, measures that can quantitatively characterize this spatial distributiveness in individual subjects are lacking. In this study, we propose a number of spatial heterogeneity measures to characterize brain activation maps. The proposed methods focus on different aspects of heterogeneity, including the shape (compactness, complexity in the distribution of activated regions (fractal dimension and co-occurrence matrix, and gappiness between activated regions (lacunarity. To this end, functional MRI derived activation maps of a language and a motor task were obtained in language impaired children with (Rolandic epilepsy and compared to age-matched healthy controls. Group analysis of the activation maps revealed no significant differences between patients and controls for both tasks. However, for the language task the activation maps in patients appeared more heterogeneous than in controls. Lacunarity was the best measure to discriminate activation patterns of patients from controls (sensitivity 74%, specificity 70% and illustrates the increased irregularity of gaps between activated regions in patients. The combination of heterogeneity measures and a support vector machine approach yielded further increase in sensitivity and specificity to 78% and 80%, respectively. This illustrates that activation distributions in impaired brains can be complex and more heterogeneous than in normal brains and cannot be captured fully by a single quantity. In conclusion, heterogeneity analysis has potential to robustly characterize the increased distributiveness of brain activation in individual patients.

  12. Disparities in Spatial Prevalence of Feline Retroviruses due to Data Aggregation: A Case of the Modifiable Areal Unit Problem

    Directory of Open Access Journals (Sweden)

    Bimal K. Chhetri

    2014-01-01

    Full Text Available The knowledge of the spatial distribution feline immunodeficiency virus and feline leukemia virus infections, which are untreatable, can inform on their risk factors and high-risk areas to enhance control. However, when spatial analysis involves aggregated spatial data, results may be influenced by the spatial scale of aggregation, an effect known as the modifiable areal unit problem (MAUP. In this study, area level risk factors for both infections in 28,914 cats tested with ELISA were investigated by multivariable spatial Poisson regression models along with MAUP effect on spatial clustering and cluster detection (for postal codes, counties, and states by Moran’s I test and spatial scan test, respectively. The study results indicate that the significance and magnitude of the association of risk factors with both infections varied with aggregation scale. Further more, Moran’s I test only identified spatial clustering at postal code and county levels of aggregation. Similarly, the spatial scan test indicated that the number, size, and location of clusters varied over aggregation scales. In conclusion, the association between infection and area was influenced by the choice of spatial scale and indicates the importance of study design and data analysis with respect to specific research questions.

  13. Identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis by using the Delphi Technique

    Science.gov (United States)

    Halim, N. Z. A.; Sulaiman, S. A.; Talib, K.; Ng, E. G.

    2018-02-01

    This paper explains the process carried out in identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis. The research was initially a part of a larger research exercise to identify the significance of NDCDB from the legal, technical, role and land-based analysis perspectives. The research methodology of applying the Delphi technique is substantially discussed in this paper. A heterogeneous panel of 14 experts was created to determine the importance of NDCDB from the technical relevance standpoint. Three statements describing the relevant features of NDCDB for spatial analysis were established after three rounds of consensus building. It highlighted the NDCDB’s characteristics such as its spatial accuracy, functions, and criteria as a facilitating tool for spatial analysis. By recognising the relevant features of NDCDB for spatial analysis in this study, practical application of NDCDB for various analysis and purpose can be widely implemented.

  14. [Spatial pattern analysis and associations of Quercus aquifolioides population at different growth stages in Southeast Tibet, China].

    Science.gov (United States)

    Shen, Zhi-qiang; Hua, Min; Dan, Qu; Lu, Jie; Fang, Jiang-ping

    2016-02-01

    This article analyzed the spatial pattern and its correlation of Quercus aquifolioides, Southeast Tibet at different growing stages by using Ripley' s L function in the method of point pattern, analysis. The results showed the diameter structure of Q. aquifolioides population in Southeast Tibet followed a 'single peak' shape and the saplings and medium trees predominated in number in the whole population. The population had a high regeneration rate and was of increase type. In the growth process of Q. aquifolioides from saplings to large trees, saplings and medium trees showed aggregation distribution at.small scale, while large trees showed basically random distribution at whole scale. There was significant correlation between saplings with medium or large trees at small scale, however, there was no correlation between medium and large trees. In the growth process of Q. aquifolioides population from saplings, medium trees to large trees, its spatial pattern developed from aggregative distribution to random distribution. The natural regeneration of Q. aquifolioides population was affected not only by interspecific competition, but also by intraspecific competition. In the similar natural environment, the most important factors affecting the spatial pattern of Q. aquifoioides population were its own biological and ecological characteristics.

  15. Aspects of the incorporation of spatial data into radioecological and restoration analysis

    International Nuclear Information System (INIS)

    Beresford, N.A.; Wright, S.M.; Howard, B.J.; Crout, N.M.J.; Arkhipov, A.; Voigt, G.

    2002-01-01

    In the last decade geographical information systems have been increasingly used to incorporate spatial data into radioecological analysis. This has allowed the development of models with spatially variable outputs. Two main approaches have been adopted in the development of spatial models. Empirical Tag based models applied across a range of spatial scales utilize underlying soil type maps and readily available radioecological data. Soil processes can also be modelled to allow the dynamic prediction of radionuclide soil to plant transfer. We discuss a dynamic semi-mechanistic radiocaesium soil to plant-transfer model, which utilizes readily available spatially variable soil parameters. Both approaches allow the identification of areas that may be vulnerable to radionuclide deposition, therefore enabling the targeting of intervention measures. Improved estimates of radionuclide fluxes and ingestion doses can be achieved by incorporating spatially varying inputs such as agricultural production and dietary habits in to these models. In this paper, aspects of such models, including data requirements, implementation and outputs are discussed and critically evaluated. The relative merits and disadvantages of the two spatial model approaches adopted within radioecology are discussed. We consider the usefulness of such models to aid decision-makers and access the requirements and potential of further application within radiological protection. (author)

  16. Visuo-Spatial Performance in Autism: A Meta-analysis

    OpenAIRE

    Muth, Anne; Honekopp, Johannes; Falter, Christine

    2014-01-01

    Visuo-spatial skills are believed to be enhanced in autism spectrum disorders (ASDs). This meta-analysis tests the current state of evidence for Figure Disembedding, Block Design, Mental Rotation and Navon tasks in ASD and neurotypicals. Block Design (d = 0.32) and Figure Disembedding (d = 0.26) showed superior performance for ASD with large heterogeneity that is unaccounted for. No clear differences were found for Mental Rotation. ASD samples showed a stronger local processing preference for...

  17. Environmental, Spatial, and Sociodemographic Factors Associated with Nonfatal Injuries in Indonesia

    Directory of Open Access Journals (Sweden)

    Sri Irianti

    2017-01-01

    Full Text Available Background. The determinants of injuries and their reoccurrence in Indonesia are not well understood, despite their importance in the prevention of injuries. Therefore, this study seeks to investigate the environmental, spatial, and sociodemographic factors associated with the reoccurrence of injuries among Indonesian people. Methods. Data from the 2013 round of the Indonesia Baseline Health Research (IBHR 2013 were analysed using a two-part hurdle regression model. A logit regression model was chosen for the zero-hurdle part, while a zero-truncated negative binomial regression model was selected for the counts part. Odds ratio (OR and incidence rate ratio (IRR were the measures of association, respectively. Results. The results suggest that living in a household with distant drinking water source, residing in slum areas, residing in Eastern Indonesia, having low educational attainment, being men, and being poorer are positively related to the likelihood of experiencing injury. Moreover, being a farmer or fishermen, having low educational attainment, and being men are positively associated with the frequency of injuries. Conclusion. This study would be useful to prioritise injury prevention programs in Indonesia based on the environmental, spatial, and sociodemographic characteristics.

  18. Studies on thermal neutron perturbation factor needed for bulk sample activation analysis

    CERN Document Server

    Csikai, J; Sanami, T; Michikawa, T

    2002-01-01

    The spatial distribution of thermal neutrons produced by an Am-Be source in a graphite pile was measured via the activation foil method. The results obtained agree well with calculated data using the MCNP-4B code. A previous method used for the determination of the average neutron flux within thin absorbing samples has been improved and extended for a graphite moderator. A procedure developed for the determination of the flux perturbation factor renders the thermal neutron activation analysis of bulky samples of unknown composition possible both in hydrogenous and graphite moderators.

  19. Spatial analysis and modeling to assess and map current vulnerability to extreme weather events in the Grijalva - Usumacinta watershed, Mexico

    International Nuclear Information System (INIS)

    Lopez L, D

    2009-01-01

    One of the major concerns over a potential change in climate is that it will cause an increase in extreme weather events. In Mexico, the exposure factors as well as the vulnerability to the extreme weather events have increased during the last three or four decades. In this study spatial analysis and modeling were used to assess and map settlement and crop systems vulnerability to extreme weather events in the Grijalva - Usumacinta watershed. Sensitivity and coping adaptive capacity maps were constructed using decision models; these maps were then combined to produce vulnerability maps. The most vulnerable area in terms of both settlement and crop systems is the highlands, where the sensitivity is high and the adaptive capacity is low. In lowlands, despite the very high sensitivity, the higher adaptive capacity produces only moderate vulnerability. I conclude that spatial analysis and modeling are powerful tools to assess and map vulnerability. These preliminary results can guide the formulation of adaptation policies to an increasing risk of extreme weather events.

  20. Spatial analysis and modeling to assess and map current vulnerability to extreme weather events in the Grijalva - Usumacinta watershed, Mexico

    Energy Technology Data Exchange (ETDEWEB)

    Lopez L, D, E-mail: dlopez@centrogeo.org.m [Centro de Investigacion en GeografIa y Geomatica, Ing. Jorge L. Tamayo A.C., Contoy 137, col. Lomas de Padierna, del Tlalpan, Maxico D.F (Mexico)

    2009-11-01

    One of the major concerns over a potential change in climate is that it will cause an increase in extreme weather events. In Mexico, the exposure factors as well as the vulnerability to the extreme weather events have increased during the last three or four decades. In this study spatial analysis and modeling were used to assess and map settlement and crop systems vulnerability to extreme weather events in the Grijalva - Usumacinta watershed. Sensitivity and coping adaptive capacity maps were constructed using decision models; these maps were then combined to produce vulnerability maps. The most vulnerable area in terms of both settlement and crop systems is the highlands, where the sensitivity is high and the adaptive capacity is low. In lowlands, despite the very high sensitivity, the higher adaptive capacity produces only moderate vulnerability. I conclude that spatial analysis and modeling are powerful tools to assess and map vulnerability. These preliminary results can guide the formulation of adaptation policies to an increasing risk of extreme weather events.

  1. An Integrated Approach Using Spatial Analysis to Study the Risk Factors for Leishmaniasis in Area of Recent Transmission

    Directory of Open Access Journals (Sweden)

    Júlia Alves Menezes

    2015-01-01

    Full Text Available Some epidemiological aspects of leishmaniasis in the municipality of Formiga, Brazil, an important touristic site, were evaluated. Those included phlebotomine sand fly vectors, canine infection, and geoprocessing analysis for determining critical transmission areas. Sand flies (224 insects belonging to ten different species were captured. The most captured species included Lutzomyia longipalpis (35.3%, Lutzomyia cortelezzii (33.5%, and Lutzomyia whitmani (18.3%. A significant correlation between sand fly densities and climatic conditions was detected. Serological diagnosis (DPP and ELISA was performed in 570 dogs indicating a prevalence of 5.8%. After sequencing the main species circulating in the area were Leishmania infantum and Leishmania braziliensis. Spatial analysis demonstrated that vegetation and hydrography may be related to sand fly distribution and infected dogs. The municipality of Formiga has proven leishmaniasis vectors and infected dogs indicating the circulation of the parasite in the city. Correlation of those data with environmental and human cases has identified the critical areas for control interventions (south, northeast, and northwest. In conclusion, there is current transmission of visceral and canine human cases and the city is on the risk for the appearance of cutaneous cases.

  2. GIS-based spatial statistical analysis of risk areas for liver flukes in Surin Province of Thailand.

    Science.gov (United States)

    Rujirakul, Ratana; Ueng-arporn, Naporn; Kaewpitoon, Soraya; Loyd, Ryan J; Kaewthani, Sarochinee; Kaewpitoon, Natthawut

    2015-01-01

    It is urgently necessary to be aware of the distribution and risk areas of liver fluke, Opisthorchis viverrini, for proper allocation of prevention and control measures. This study aimed to investigate the human behavior, and environmental factors influencing the distribution in Surin Province of Thailand, and to build a model using stepwise multiple regression analysis with a geographic information system (GIS) on environment and climate data. The relationship between the human behavior, attitudes (R Square=0.878, and, Adjust R Square=0.849. By GIS analysis, we found Si Narong, Sangkha, Phanom Dong Rak, Mueang Surin, Non Narai, Samrong Thap, Chumphon Buri, and Rattanaburi to have the highest distributions in Surin province. In conclusion, the combination of GIS and statistical analysis can help simulate the spatial distribution and risk areas of liver fluke, and thus may be an important tool for future planning of prevention and control measures.

  3. Spatial analysis of digital technologies and impact on socio - cultural ...

    African Journals Online (AJOL)

    The objective of this study was to determine the spatial distribution of digital technologies and ascertain whether digital technologies have significant impact on socio - cultural values or not. Moran's index and Getis and Ord's statistic were used for cluster and hotspots analysis. The unique locations of digital technologies ...

  4. Analysis of Impact of Geographical Environment and Socio-economic Factors on the Spatial Distribution of Kaohsiung Dengue Fever Epidemic

    Science.gov (United States)

    Hsu, Wei-Yin; Wen, Tzai-Hung; Yu, Hwa-Lung

    2013-04-01

    Taiwan is located in subtropical and tropical regions with high temperature and high humidity in the summer. This kind of climatic condition is the hotbed for the propagation and spread of the dengue vector mosquito. Kaohsiung City has been the worst dengue fever epidemic city in Taiwan. During the study period, from January 1998 to December 2011, Taiwan CDC recorded 7071 locally dengue epidemic cases in Kaohsiung City, and the number of imported case is 118. Our research uses Quantile Regression, a spatial infection disease distribution, to analyze the correlation between dengue epidemic and geographic environmental factors and human society factors in Kaohsiung. According to our experiment statistics, agriculture and natural forest have a positive relation to dengue fever(5.5~34.39 and 3.91~15.52). The epidemic will rise when the ratio for agriculture and natural forest increases. Residential ratio has a negative relation for quantile 0.1 to 0.4(-0.005~-0.78), and a positive relation for quantile 0.5 to0.9(0.01~18.0) . The mean income is also a significant factor in social economy field, and it has a negative relation to dengue fever(-0.01~-0.04). Conclusion from our research is that the main factor affecting the degree of dengue fever in predilection area is the residential proportion and the ratio of agriculture and natural forest plays an important role affecting the degree of dengue fever in non predilection area. Moreover, the serious epidemic area located by regression model is the same as the actual condition in Kaohsiung. This model can be used to predict the serious epidemic area of dengue fever and provide some references for the Health Agencies

  5. Spatial modeling of potential woody biomass flow

    Science.gov (United States)

    Woodam Chung; Nathaniel Anderson

    2012-01-01

    The flow of woody biomass to end users is determined by economic factors, especially the amount available across a landscape and delivery costs of bioenergy facilities. The objective of this study develop methodology to quantify landscape-level stocks and potential biomass flows using the currently available spatial database road network analysis tool. We applied this...

  6. The spatial kinetic analysis of accelerator-driven subcritical reactor

    International Nuclear Information System (INIS)

    Takahashi, H.; An, Y.; Chen, X.

    1998-02-01

    The operation of the accelerator driven reactor with subcritical condition provides a more flexible choice of the reactor materials and of design parameters. A deep subcriticality is chosen sometime from the analysis of point kinetics. When a large reactor is operated in deep subcritical condition by using a localized spallation source, the power distribution has strong spatial dependence, and point kinetics does not provide proper analysis for reactor safety. In order to analyze the spatial and energy dependent kinetic behavior in the subcritical reactor, the authors developed a computation code which is composed of two parts, the first one is for creating the group cross section and the second part solves the multi-group kinetic diffusion equations. The reactor parameters such as the cross section of fission, scattering, and energy transfer among the several energy groups and regions are calculated by using a code modified from the Monte Carlo codes MCNPA and LAHET instead of the usual analytical method of ANISN, TWOTRAN codes. Thus the complicated geometry of the accelerator driven reactor core can be precisely taken into account. The authors analyzed the subcritical minor actinide transmutor studied by Japan Atomic Energy Research Institute (JAERI) using the code

  7. Analysis of syntactic and semantic features for fine-grained event-spatial understanding in outbreak news reports

    Directory of Open Access Journals (Sweden)

    Chanlekha Hutchatai

    2010-03-01

    Full Text Available Abstract Background Previous studies have suggested that epidemiological reasoning needs a fine-grained modelling of events, especially their spatial and temporal attributes. While the temporal analysis of events has been intensively studied, far less attention has been paid to their spatial analysis. This article aims at filling the gap concerning automatic event-spatial attribute analysis in order to support health surveillance and epidemiological reasoning. Results In this work, we propose a methodology that provides a detailed analysis on each event reported in news articles to recover the most specific locations where it occurs. Various features for recognizing spatial attributes of the events were studied and incorporated into the models which were trained by several machine learning techniques. The best performance for spatial attribute recognition is very promising; 85.9% F-score (86.75% precision/85.1% recall. Conclusions We extended our work on event-spatial attribute recognition by focusing on machine learning techniques, which are CRF, SVM, and Decision tree. Our approach avoided the costly development of an external knowledge base by employing the feature sources that can be acquired locally from the analyzed document. The results showed that the CRF model performed the best. Our study indicated that the nearest location and previous event location are the most important features for the CRF and SVM model, while the location extracted from the verb's subject is the most important to the Decision tree model.

  8. Analysis of spatial pattern of settlements in the federal capital ...

    African Journals Online (AJOL)

    Human settlements are important, seemingly static but dynamic, features of the cultural landscape that have attracted several studies due to the important role they play in human life. This paper examined the spatial distribution of settlements in the Federal Capital Territory (FCT) of Nigeria. The analysis uses vector based ...

  9. Reliability Analysis of 6-Component Star Markov Repairable System with Spatial Dependence

    Directory of Open Access Journals (Sweden)

    Liying Wang

    2017-01-01

    Full Text Available Star repairable systems with spatial dependence consist of a center component and several peripheral components. The peripheral components are arranged around the center component, and the performance of each component depends on its spatial “neighbors.” Vector-Markov process is adapted to describe the performance of the system. The state space and transition rate matrix corresponding to the 6-component star Markov repairable system with spatial dependence are presented via probability analysis method. Several reliability indices, such as the availability, the probabilities of visiting the safety, the degradation, the alert, and the failed state sets, are obtained by Laplace transform method and a numerical example is provided to illustrate the results.

  10. Effects of oceanographic factors on spatial distribution of Whale Shark in Cendrawasih Bay National Park, West Papua

    Science.gov (United States)

    Ranintyari, Maulida; Sunarto; Syamsuddin, Mega L.; Astuty, Sri

    2018-05-01

    Whale sharks are a leading species in Cendrawasih Bay due to its benign nature and its regular appearance. Recently, whale sharks are vulnerable to scarcity and even extinction. One of the efforts to maintain the existence of the whale shark population is by knowing its spatial distribution. This study aims to analyze how the oceanographic factors affect the spatial distribution of whale sharks in Cendrawasih Bay National Park. The method used in this research is descriptive with the quantitative approach using the Generalized Additive Model (GAM) analysis. The data consisted of the whale shark monitoring data in TNTC taken by WWF-Indonesia, and image data of sea surface temperature (SST) and chlorophyll-a concentration of Aqua-MODIS, and also sea surface current from Aviso. Analyses were conducted for the period of January 2012 until March 2015. The GAM result indicated that sea surface current was better than the other environment (SST and chlorophyll-a concentration) as an oceanographic predictor of whale shark appearance. High probabilities of the whale shark’s to appear on the surface were observed in sea surface current velocities between 0.30-0.60 m/s, for SST ranged from 30.50-31.80 °C, and for chlorophyll-a concentration ranged from 0.20-0.40 mg/m3.

  11. Factor analysis

    CERN Document Server

    Gorsuch, Richard L

    2013-01-01

    Comprehensive and comprehensible, this classic covers the basic and advanced topics essential for using factor analysis as a scientific tool in psychology, education, sociology, and related areas. Emphasizing the usefulness of the techniques, it presents sufficient mathematical background for understanding and sufficient discussion of applications for effective use. This includes not only theory but also the empirical evaluations of the importance of mathematical distinctions for applied scientific analysis.

  12. A Spatial Analysis of Poverty in Kigali, Rwanda using indicators of ...

    African Journals Online (AJOL)

    A Spatial Analysis of Poverty in Kigali, Rwanda using indicators of household ... conducted by the National Institute of Statistics of Rwanda in 2000-2001. ... The third region of low poverty incident has between 4-12% of its population poor.

  13. Analysis of Participatory Processes in the Formulation of Spatial Plan for Nature Park Medvednica

    Directory of Open Access Journals (Sweden)

    Nataša Lovrić

    2011-12-01

    Full Text Available Background and Purpose: This research aims to assess the stakeholders influence on spatial planning of Nature Park Medvednica, a mountainous protected area adjacent to Zagreb, the capital city of Croatia, which tries to hold on to the pressure of the urbanization. Because of the inexistence of spatial plan which is required with the Croatian laws, its area was significantly decreased in 2009. This kind of research has not been done yet for NP Medvednica, and it will provide a contribution to the process of developing a spatial Plan for NP Medvednica. Material and Methods: The study was conducted in the framework of stakeholder analysis, for which a series of in-depth interviews with - stakeholders were performed, and documents concerning the spatial plan were analysed. The data gained was processed in MAXQDA software for qualitative analysis. Results and Conclusion: The gathered data explains which are the disadvantages of the tree processes of the formulation of the spatial plan and is giving a possible theoretical explanation or a model which can be implied in any decision making process involving stakeholders in natural resources management in within a given political and cultural context. Description of the past and current spatial planning situation of the NP Medvednica was specified and issues and stakeholders concerning the creation of the spatial plan where identified. The key conflict areas that affect the formulation of spatial plan were detected and examined. The level of participation of stakeholders in the context of fulfilment of their own interests was assessed as well as the influence on participation processes of different stakeholder groups on the formulation of the spatial plan. In order to have proper citizens and stakeholders participation some changes in the legislation should take place.

  14. Influence of environmental factors on the spatial distribution and diversity of forest soil in Latvia

    Directory of Open Access Journals (Sweden)

    Raimonds Kasparinskis

    2012-02-01

    Full Text Available This study was carried out to determine the spatial relationships between environmental factors (Quaternary deposits, topographical situation, land cover, forest site types, tree species, soil texture and soil groups, and their prefix qualifiers (according to the international Food and Agricultural Organization soil classification system World Reference Base for Soil Resources [FAO WRB]. The results show that it is possible to establish relationships between the distribution of environmental factors and soil groups by applying the generalized linear models in data statistical analysis, using the R 2.11.1 software for processing data from 113 sampling plots throughout the forest territory of Latvia.A very high diversity of soil groups in a relatively similar geological structure was revealed. For various reasons there is not always close relationship between the soil group, their prefix qualifiers and Quaternary deposits, as well as between forest site types, the dominant tree species and specific soil group and its prefix qualifiers. Close correlation was established between Quaternary deposits, forest site types, dominant tree species and soil groups within nutrient-poor sediments and very rich deposits containing free carbonates. No significant relationship was detected between the CORINE Land Cover 2005 classes, topographical situation and soil group.

  15. Spatial statistical analysis of basal stem root disease under natural field epidemic of oil palm

    Science.gov (United States)

    Kamu, Assis; Phin, Chong Khim; Seman, Idris Abu; Wan, Hoong Hak; Mun, Ho Chong

    2015-02-01

    Oil palm or scientifically known as Elaeis guineensis Jacq. is the most important commodity crop in Malaysia and has greatly contributed to the economy growth of the country. As far as disease is concerned in the industry, Basal Stem Rot (BSR) caused by Ganoderma boninence remains the most important disease. BSR disease is the most widely studied with information available for oil palm disease in Malaysia. However, there is still limited study on the spatial as well as temporal pattern or distribution of the disease especially under natural field epidemic condition in oil palm plantation. The objective of this study is to spatially identify the pattern of BSR disease under natural field epidemic using two geospatial analytical techniques, which are quadrat analysis for the first order properties of partial pattern analysis and nearest-neighbor analysis (NNA) for the second order properties of partial pattern analysis. Two study sites were selected with different age of tree. Both sites are located in Tawau, Sabah and managed by the same company. The results showed that at least one of the point pattern analysis used which is NNA (i.e. the second order properties of partial pattern analysis) has confirmed the disease is complete spatial randomness. This suggests the spread of the disease is not from tree to tree and the age of palm does not play a significance role in determining the spatial pattern of the disease. From the spatial pattern of the disease, it would help in the disease management program and for the industry in the future. The statistical modelling is expected to help in identifying the right model to estimate the yield loss of oil palm due to BSR disease in the future.

  16. Spatial vulnerability assessments by regression kriging

    Science.gov (United States)

    Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor

    2016-04-01

    information representing IEW or GRP forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency and measured GRP values respectively. An efficient spatial prediction methodology was applied to construct reliable maps, namely regression kriging (RK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Application of RK also provides the possibility of inherent accuracy assessment. The resulting maps are characterized by global and local measures of its accuracy. Additionally the method enables interval estimation for spatial extension of the areas of predefined risk categories. All of these outputs provide useful contribution to spatial planning, action planning and decision making. Acknowledgement: Our work was partly supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

  17. [Spatial analysis of childhood obesity and overweight in Peru, 2014].

    Science.gov (United States)

    Hernández-Vásquez, Akram; Bendezú-Quispe, Guido; Díaz-Seijas, Deysi; Santero, Marilina; Minckas, Nicole; Azañedo, Diego; Antiporta, Daniel A

    2016-01-01

    To estimate regional prevalence and identify the spatial patterns of the degree of overweight and obesity by districts in under five years children in Peru during 2014. Analysis of the information reported by the Information System Nutritional Status (SIEN) of the number of cases of overweight and obesity in children under five years recorded during 2014. Regional prevalence for overweight and obesity, and their respective confidence intervals to 95% were calculated. Moran index was used to determine patterns of grouping districts with high prevalence of overweight and/or obesity. Data from 1834 districts and 2,318,980 children under five years were analyzed. 158,738 cases (6.84%; CI 95%: 6.81 to 6.87) were overweight, while 56,125 (2.42%; CI 95%: 2.40 to 2.44) obesity. The highest prevalence of overweight were identified in the regions of Tacna (13.9%), Moquegua (11.8%), Callao (10.4%), Lima (10.2%) and Ica (9.3%), and in the same regions for obesity with 5.3%; 4.3%; 4.0%; 4.0% and 3.8% respectively. The spatial analysis found grouping districts of high prevalence in 10% of all districts for both overweight and obesity, identifying 199 districts for overweight (126 urban and 73 rural), and 184 for obesity (136 urban and 48 rural). The highest prevalence of overweight and obesity were identified in the Peruvian coast regions. Moreover, these regions are predominantly exhibited a spatial clustering of districts with high prevalence of overweight and obesity.

  18. Linking the spatial patterns of organisms and abiotic factors to ecosystem function and management: insights from semi-arid environments

    Directory of Open Access Journals (Sweden)

    F. T. Maestre

    2006-12-01

    Full Text Available Numerous theoretical and modeling studies have demonstrated the ecological significance of the spatial patterning of organisms on ecosystem functioning and dynamics. However, there is a paucity of empirical evidence that quantitatively shows how changes in the spatial patterns of the organisms forming biotic communities are directly related to ecosystem structure and functioning. In this article, I review a series of experiments and observational studies conducted in semi-arid environments from Spain (degraded calcareous shrubland, steppes dominated by Stipa tenacissima, and gypsum shrublands to: 1 evaluate whether the spatial patterns of the dominant biotic elements in the community are linked to ecosystem structure and functioning, and 2 test if these patterns, and those of abiotic factors, can be used to improve ecosystem restoration. In the semiarid steppes we found a significant positive relationship between the spatial pattern of the perennial plant community and: i the water status of S. tenacissima and ii perennial species richness and diversity. Experimental plantings conducted in these steppes showed that S. tenacissima facilitated the establishment of shrub seedlings, albeit the magnitude and direction of this effect was dependent on rainfall conditions during the first yr after planting. In the gypsum shrubland, a significant, direct relationship between the spatial pattern of the biological soil crusts and surrogates of ecosystem functioning (soil bulk density and respiration was found. In a degraded shrubland with very low vegetation cover, the survival of an introduced population of the shrub Pistacia lentiscus showed marked spatial patterns, which were related to the spatial patterns of soil properties such as soil compaction and sand content. These results provide empirical evidence on the importance of spatial patterns for maintaining ecosystem structure and functioning in semi-arid ecosystems

  19. A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka

    Directory of Open Access Journals (Sweden)

    Müller Daniel

    2011-05-01

    Full Text Available Abstract Background The deprived physical environments present in slums are well-known to have adverse health effects on their residents. However, little is known about the health effects of the social environments in slums. Moreover, neighbourhood quantitative spatial analyses of the mental health status of slum residents are still rare. The aim of this paper is to study self-rated mental health data in several slums of Dhaka, Bangladesh, by accounting for neighbourhood social and physical associations using spatial statistics. We hypothesised that mental health would show a significant spatial pattern in different population groups, and that the spatial patterns would relate to spatially-correlated health-determining factors (HDF. Methods We applied a spatial epidemiological approach, including non-spatial ANOVA/ANCOVA, as well as global and local univariate and bivariate Moran's I statistics. The WHO-5 Well-being Index was used as a measure of self-rated mental health. Results We found that poor mental health (WHO-5 scores Conclusions Spatial patterns of mental health were detected and could be partly explained by spatially correlated HDF. We thereby showed that the socio-physical neighbourhood was significantly associated with health status, i.e., mental health at one location was spatially dependent on the mental health and HDF prevalent at neighbouring locations. Furthermore, the spatial patterns point to severe health disparities both within and between the slums. In addition to examining health outcomes, the methodology used here is also applicable to residuals of regression models, such as helping to avoid violating the assumption of data independence that underlies many statistical approaches. We assume that similar spatial structures can be found in other studies focussing on neighbourhood effects on health, and therefore argue for a more widespread incorporation of spatial statistics in epidemiological studies.

  20. Exploratory Bi-factor Analysis: The Oblique Case

    OpenAIRE

    Jennrich, Robert L.; Bentler, Peter M.

    2011-01-01

    Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford (1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler (2011) introduced an exploratory form of bi-factor analysis that does not require one to provide an explicit bi-factor structure a priori. They use exploratory factor analysis and a bi-factor rotation criterion designed to produce a rotated loading mat...

  1. Soil pH Errors Propagation from Measurements to Spatial Predictions - Cost Benefit Analysis and Risk Assessment Implications for Practitioners and Modelers

    Science.gov (United States)

    Owens, P. R.; Libohova, Z.; Seybold, C. A.; Wills, S. A.; Peaslee, S.; Beaudette, D.; Lindbo, D. L.

    2017-12-01

    The measurement errors and spatial prediction uncertainties of soil properties in the modeling community are usually assessed against measured values when available. However, of equal importance is the assessment of errors and uncertainty impacts on cost benefit analysis and risk assessments. Soil pH was selected as one of the most commonly measured soil properties used for liming recommendations. The objective of this study was to assess the error size from different sources and their implications with respect to management decisions. Error sources include measurement methods, laboratory sources, pedotransfer functions, database transections, spatial aggregations, etc. Several databases of measured and predicted soil pH were used for this study including the United States National Cooperative Soil Survey Characterization Database (NCSS-SCDB), the US Soil Survey Geographic (SSURGO) Database. The distribution of errors among different sources from measurement methods to spatial aggregation showed a wide range of values. The greatest RMSE of 0.79 pH units was from spatial aggregation (SSURGO vs Kriging), while the measurement methods had the lowest RMSE of 0.06 pH units. Assuming the order of data acquisition based on the transaction distance i.e. from measurement method to spatial aggregation the RMSE increased from 0.06 to 0.8 pH units suggesting an "error propagation". This has major implications for practitioners and modeling community. Most soil liming rate recommendations are based on 0.1 pH unit increments, while the desired soil pH level increments are based on 0.4 to 0.5 pH units. Thus, even when the measured and desired target soil pH are the same most guidelines recommend 1 ton ha-1 lime, which translates in 111 ha-1 that the farmer has to factor in the cost-benefit analysis. However, this analysis need to be based on uncertainty predictions (0.5-1.0 pH units) rather than measurement errors (0.1 pH units) which would translate in 555-1,111 investment that

  2. Research on spatial Model and analysis algorithm for nuclear weapons' damage effects

    International Nuclear Information System (INIS)

    Liu Xiaohong; Meng Tao; Du Maohua; Wang Weili; Ji Wanfeng

    2011-01-01

    In order to realize the three dimension visualization of nuclear weapons' damage effects. Aiming at the characteristics of the damage effects data, a new model-MRPCT model is proposed, and this model can carry out the modeling of the three dimension spatial data of the nuclear weapons' damage effects. For the sake of saving on the memory, linear coding method is used to store the MRPCT model. On the basis of Morton code, spatial analysis of the damage effects is completed. (authors)

  3. Spatially adaptive mixture modeling for analysis of FMRI time series.

    Science.gov (United States)

    Vincent, Thomas; Risser, Laurent; Ciuciu, Philippe

    2010-04-01

    Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM

  4. Local Spatial Analysis and Dynamic Simulation of Childhood Obesity and Neighbourhood Walkability in a Major Canadian City.

    Science.gov (United States)

    Shahid, Rizwan; Bertazzon, Stefania

    2015-01-01

    Body weight is an important indicator of current and future health and it is even more critical in children, who are tomorrow's adults. This paper analyzes the relationship between childhood obesity and neighbourhood walkability in Calgary, Canada. A multivariate analytical framework recognizes that childhood obesity is also associated with many factors, including socioeconomic status, foodscapes, and environmental factors, as well as less measurable factors, such as individual preferences, that could not be included in this analysis. In contrast with more conventional global analysis, this research employs localized analysis and assesses need-based interventions. The one-size-fit-all strategy may not effectively control obesity rates, since each neighbourhood has unique characteristics that need to be addressed individually. This paper presents an innovative framework combining local analysis with simulation modeling to analyze childhood obesity. Spatial models generally do not deal with simulation over time, making it cumbersome for health planners and policy makers to effectively design and implement interventions and to quantify their impact over time. This research fills this gap by integrating geographically weighted regression (GWR), which identifies vulnerable neighbourhoods and critical factors for childhood obesity, with simulation modeling, which evaluates the impact of the suggested interventions on the targeted neighbourhoods. Neighbourhood walkability was chosen as a potential target for localized interventions, owing to the crucial role of walking in developing a healthy lifestyle, as well as because increasing walkability is relatively more feasible and less expensive then modifying other factors, such as income. Simulation results suggest that local walkability interventions can achieve measurable declines in childhood obesity rates. The results are encouraging, as improvements are likely to compound over time. The results demonstrate that the

  5. Local Spatial Analysis and Dynamic Simulation of Childhood Obesity and Neighbourhood Walkability in a Major Canadian City

    Directory of Open Access Journals (Sweden)

    Rizwan Shahid

    2015-09-01

    Full Text Available Body weight is an important indicator of current and future health and it is even more critical in children, who are tomorrow’s adults. This paper analyzes the relationship between childhood obesity and neighbourhood walkability in Calgary, Canada. A multivariate analytical framework recognizes that childhood obesity is also associated with many factors, including socioeconomic status, foodscapes, and environmental factors, as well as less measurable factors, such as individual preferences, that could not be included in this analysis. In contrast with more conventional global analysis, this research employs localized analysis and assesses need-based interventions. The one-size-fit-all strategy may not effectively control obesity rates, since each neighbourhood has unique characteristics that need to be addressed individually. This paper presents an innovative framework combining local analysis with simulation modeling to analyze childhood obesity. Spatial models generally do not deal with simulation over time, making it cumbersome for health planners and policy makers to effectively design and implement interventions and to quantify their impact over time. This research fills this gap by integrating geographically weighted regression (GWR, which identifies vulnerable neighbourhoods and critical factors for childhood obesity, with simulation modeling, which evaluates the impact of the suggested interventions on the targeted neighbourhoods. Neighbourhood walkability was chosen as a potential target for localized interventions, owing to the crucial role of walking in developing a healthy lifestyle, as well as because increasing walkability is relatively more feasible and less expensive then modifying other factors, such as income. Simulation results suggest that local walkability interventions can achieve measurable declines in childhood obesity rates. The results are encouraging, as improvements are likely to compound over time. The results

  6. Digital Speckle Photography of Subpixel Displacements of Speckle Structures Based on Analysis of Their Spatial Spectra

    Science.gov (United States)

    Maksimova, L. A.; Ryabukho, P. V.; Mysina, N. Yu.; Lyakin, D. V.; Ryabukho, V. P.

    2018-04-01

    We have investigated the capabilities of the method of digital speckle interferometry for determining subpixel displacements of a speckle structure formed by a displaceable or deformable object with a scattering surface. An analysis of spatial spectra of speckle structures makes it possible to perform measurements with a subpixel accuracy and to extend the lower boundary of the range of measurements of displacements of speckle structures to the range of subpixel values. The method is realized on the basis of digital recording of the images of undisplaced and displaced speckle structures, their spatial frequency analysis using numerically specified constant phase shifts, and correlation analysis of spatial spectra of speckle structures. Transformation into the frequency range makes it possible to obtain quantities to be measured with a subpixel accuracy from the shift of the interference-pattern minimum in the diffraction halo by introducing an additional phase shift into the complex spatial spectrum of the speckle structure or from the slope of the linear plot of the function of accumulated phase difference in the field of the complex spatial spectrum of the displaced speckle structure. The capabilities of the method have been investigated in natural experiment.

  7. Geo-Spatial Social Network Analysis of Social Media to Mitigate Disasters

    Science.gov (United States)

    Carley, K. M.

    2017-12-01

    Understanding the spatial layout of human activity can afford a better understanding many phenomena - such as local cultural, the spread of ideas, and the scope of a disaster. Today, social media is one of the key sensors for acquiring information on socio-cultural activity, some with cues as to the geo-location. We ask, What can be learned by putting such data on maps? For example, are people who chat on line more likely to be near each other? Can Twitter data support disaster planning or early warning? In this talk, such issues are examined using data collected via Twitter and analyzed using ORA. ORA is a network analysis and visualization system. It supports not just social networks (who is interacting with whom), but also high dimensional networks with many types of nodes (e.g. people, organizations, resources, activities …) and relations, geo-spatial network analysis, dynamic network analysis, & geo-temporal analysis. Using ORA lessons learned from five case studies are considered: Arab Spring, Tsunami warning in Padang Indonesia, Twitter around Fukushima in Japan, Typhoon Haiyan (Yolanda), & regional conflict. Using Padang Indonesia data, we characterize the strengths and limitations of social media data to support disaster planning & early warning, identify at risk areas & issues of concern, and estimate where people are and which areas are impacted. Using Fukushima Japanese data, social media is used to estimate geo-spatial regularities in movement and communication that can inform disaster response and risk estimation. Using Arab Spring data, we find that the spread of bots & extremists varies by country and time, to the extent that using twitter to understand who is important or what ideas are critical can be compromised. Bots and extremists can exploit disaster messaging to create havoc and facilitate criminal activity e.g. human trafficking. Event discovery mechanisms support isolating geo-epi-centers for key events become crucial. Spatial inference

  8. Risk Factors and Spatial Clusters of Cryptosporidium Infection among School-Age Children in a Rural Region of Eastern China.

    Science.gov (United States)

    Zheng, Hao; He, Jianfeng; Wang, Li; Zhang, Rong; Ding, Zhen; Hu, Wenbiao

    2018-05-06

    The epidemiological features of Cryptosporidium infection among school-age children in China still remain unclear. Hereby, a cross-sectional study of 1637 children aged 3⁻9 years was designed to investigate the risk factors and spatial clusters of Cryptosporidium infection in a rural region of Eastern China. Stool specimens collected from participants were examined using the auramine-phenol and modified acid-fast staining. Univariable and multivariable analyses were performed to identify the risk factors of Cryptospordium infection. The spatial clusters were analyzed by a discrete Poisson model using SaTScan software. Our results showed that the overall prevalence of Cryptosporidium infection was 11‰ in the research region. At the age of 3⁻6 years (odds ratios (OR) = 3.072, 95% confidence intervals (CI) : 1.001⁻9.427), not washing hands before eating and after defecation (OR = 3.003, 95% CI: 1.060⁻8.511) were recognized as risk factors. Furthermore, a high-risk spatial cluster (relative risk = 4.220, p = 0.025) was identified. These findings call for effective sustainable interventions including family and school-based hygienic education to reduce the prevalence of Cryptosporidium infection. Therefore, an early warning system based spatiotemporal models with risk factors is required to further improve the effectiveness and efficiency of cryptosporidiosis control in the future.

  9. Analysis of thrips distribution: application of spatial statistics and Kriging

    Science.gov (United States)

    John Aleong; Bruce L. Parker; Margaret Skinner; Diantha Howard

    1991-01-01

    Kriging is a statistical technique that provides predictions for spatially and temporally correlated data. Observations of thrips distribution and density in Vermont soils are made in both space and time. Traditional statistical analysis of such data assumes that the counts taken over space and time are independent, which is not necessarily true. Therefore, to analyze...

  10. Analysis of the spatial distribution of dengue cases in the city of Rio de Janeiro, 2011 and 2012

    Directory of Open Access Journals (Sweden)

    Silvia Carvalho

    2017-08-01

    Full Text Available ABSTRACT OBJECTIVE Analyze the spatial distribution of classical dengue and severe dengue cases in the city of Rio de Janeiro. METHODS Exploratory study, considering cases of classical dengue and severe dengue with laboratory confirmation of the infection in the city of Rio de Janeiro during the years 2011/2012. The georeferencing technique was applied for the cases notified in the Notification Increase Information System in the period of 2011 and 2012. For this process, the fields “street” and “number” were used. The ArcGis10 program’s Geocoding tool’s automatic process was performed. The spatial analysis was done through the kernel density estimator. RESULTS Kernel density pointed out hotspots for classic dengue that did not coincide geographically with severe dengue and were in or near favelas. The kernel ratio did not show a notable change in the spatial distribution pattern observed in the kernel density analysis. The georeferencing process showed a loss of 41% of classic dengue registries and 17% of severe dengue registries due to the address in the Notification Increase Information System form. CONCLUSIONS The hotspots near the favelas suggest that the social vulnerability of these localities can be an influencing factor for the occurrence of this aggravation since there is a deficiency of the supply and access to essential goods and services for the population. To reduce this vulnerability, interventions must be related to macroeconomic policies.

  11. Spatially resolved spectroscopy analysis of the XMM-Newton large program on SN1006

    Science.gov (United States)

    Li, Jiang-Tao; Decourchelle, Anne; Miceli, Marco; Vink, Jacco; Bocchino, Fabrizio

    2016-04-01

    We perform analysis of the XMM-Newton large program on SN1006 based on our newly developed methods of spatially resolved spectroscopy analysis. We extract spectra from low and high resolution meshes. The former (3596 meshes) is used to roughly decompose the thermal and non-thermal components and characterize the spatial distributions of different parameters, such as temperature, abundances of different elements, ionization age, and electron density of the thermal component, as well as photon index and cutoff frequency of the non-thermal component. On the other hand, the low resolution meshes (583 meshes) focus on the interior region dominated by the thermal emission and have enough counts to well characterize the Si lines. We fit the spectra from the low resolution meshes with different models, in order to decompose the multiple plasma components at different thermal and ionization states and compare their spatial distributions. In this poster, we will present the initial results of this project.

  12. A Bayesian method to mine spatial data sets to evaluate the vulnerability of human beings to catastrophic risk.

    Science.gov (United States)

    Li, Lianfa; Wang, Jinfeng; Leung, Hareton; Zhao, Sisi

    2012-06-01

    Vulnerability of human beings exposed to a catastrophic disaster is affected by multiple factors that include hazard intensity, environment, and individual characteristics. The traditional approach to vulnerability assessment, based on the aggregate-area method and unsupervised learning, cannot incorporate spatial information; thus, vulnerability can be only roughly assessed. In this article, we propose Bayesian network (BN) and spatial analysis techniques to mine spatial data sets to evaluate the vulnerability of human beings. In our approach, spatial analysis is leveraged to preprocess the data; for example, kernel density analysis (KDA) and accumulative road cost surface modeling (ARCSM) are employed to quantify the influence of geofeatures on vulnerability and relate such influence to spatial distance. The knowledge- and data-based BN provides a consistent platform to integrate a variety of factors, including those extracted by KDA and ARCSM to model vulnerability uncertainty. We also consider the model's uncertainty and use the Bayesian model average and Occam's Window to average the multiple models obtained by our approach to robust prediction of the risk and vulnerability. We compare our approach with other probabilistic models in the case study of seismic risk and conclude that our approach is a good means to mining spatial data sets for evaluating vulnerability. © 2012 Society for Risk Analysis.

  13. Geo-spatial multi-criteria analysis for wave energy conversion system deployment

    Energy Technology Data Exchange (ETDEWEB)

    Nobre, Ana; Pacheco, Miguel [Data Centre, Instituto Hidrografico, Portuguese Navy, Rua das Trinas 49, 1249-093 Lisboa (Portugal); Jorge, Raquel; Lopes, M.F.P.; Gato, L.M.C. [IDMEC, Instituto Superior Tecnico, Technical University of Lisbon, Avenida Rovisco Pais 1, 1049-001, Lisboa (Portugal)

    2009-01-15

    The growing requirements for renewable energy production lead to the development of a new series of systems, including wave energy conversion systems. Due to their sensitivity and the impact of the aggressive marine environment, the selection of the most adequate location for these systems is a major and very important task. Several factors, such as technological limitations, environmental conditions, administrative and logistic conditions, have to be taken into account in order to support the decision for best location. This paper describes a geo-spatial multi-criteria analysis methodology, based on geographic information systems technology, for identification of the best location to deploy a wave energy farm. This methodology is not conversion system dependent and therefore can be easily customized for different systems and implementation conditions. Selection factors can include, for example, ocean depth, sea bottom type, existing underwater cables, marine protected areas, ports location, shoreline, power grid location, military exercise areas, climatology of wave significant height, period and power. A case study demonstrating this methodology is presented, for an area offshore the Portuguese southwest coast. The system output allows a clear differential identification of the best spots for implementing a wave energy farm. It is not just a simple Boolean result showing valid and invalid locations, but a layer with a valued suitability for farm deployment. (author)

  14. Urban Transmission of American Cutaneous Leishmaniasis in Argentina: Spatial Analysis Study

    Science.gov (United States)

    Gil, José F.; Nasser, Julio R.; Cajal, Silvana P.; Juarez, Marisa; Acosta, Norma; Cimino, Rubén O.; Diosque, Patricio; Krolewiecki, Alejandro J.

    2010-01-01

    We used kernel density and scan statistics to examine the spatial distribution of cases of pediatric and adult American cutaneous leishmaniasis in an urban disease-endemic area in Salta Province, Argentina. Spatial analysis was used for the whole population and stratified by women > 14 years of age (n = 159), men > 14 years of age (n = 667), and children < 15 years of age (n = 213). Although kernel density for adults encompassed nearly the entire city, distribution in children was most prevalent in the peripheral areas of the city. Scan statistic analysis for adult males, adult females, and children found 11, 2, and 8 clusters, respectively. Clusters for children had the highest odds ratios (P < 0.05) and were located in proximity of plantations and secondary vegetation. The data from this study provide further evidence of the potential urban transmission of American cutaneous leishmaniasis in northern Argentina. PMID:20207869

  15. Sparse modeling of spatial environmental variables associated with asthma.

    Science.gov (United States)

    Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

    2015-02-01

    Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. ANALYSIS OF PRO-POOR GROWTH AMONG THE MUNICIPAL DISTRICTS OF THE STATE OF CEARÁ - BRAZIL: SPATIAL APPROACH

    Directory of Open Access Journals (Sweden)

    Wellington Ribeiro Justo

    2014-04-01

    Full Text Available This article investigates the for-poor growth among the municipal districts of the State of Ceará in 2003. Initially it explores the recent literature of the theme as well as of the spatial econometric. Soon after it makes the spatial analysis of the variables through maps and in more robust way through the Exploratory Analysis of Given Spatial (AEDE being used the LISA methodology (Local Indicators of Spatial Association and the statistics I of Moran. Estimates the elasticity income-poverty and elasticity inequality-poverty. The tests indicated the need to incorporate variables in the estimates that learn the spatial externalities. The results suggest for-poor growth in the municipal districts from Ceará in agreement with results aggregate for the Ceará state appointed in recent literature.

  17. Factors affecting construction performance: exploratory factor analysis

    Science.gov (United States)

    Soewin, E.; Chinda, T.

    2018-04-01

    The present work attempts to develop a multidimensional performance evaluation framework for a construction company by considering all relevant measures of performance. Based on the previous studies, this study hypothesizes nine key factors, with a total of 57 associated items. The hypothesized factors, with their associated items, are then used to develop questionnaire survey to gather data. The exploratory factor analysis (EFA) was applied to the collected data which gave rise 10 factors with 57 items affecting construction performance. The findings further reveal that the items constituting ten key performance factors (KPIs) namely; 1) Time, 2) Cost, 3) Quality, 4) Safety & Health, 5) Internal Stakeholder, 6) External Stakeholder, 7) Client Satisfaction, 8) Financial Performance, 9) Environment, and 10) Information, Technology & Innovation. The analysis helps to develop multi-dimensional performance evaluation framework for an effective measurement of the construction performance. The 10 key performance factors can be broadly categorized into economic aspect, social aspect, environmental aspect, and technology aspects. It is important to understand a multi-dimension performance evaluation framework by including all key factors affecting the construction performance of a company, so that the management level can effectively plan to implement an effective performance development plan to match with the mission and vision of the company.

  18. An innovative expression model of human health risk based on the quantitative analysis of soil metals sources contribution in different spatial scales.

    Science.gov (United States)

    Zhang, Yimei; Li, Shuai; Wang, Fei; Chen, Zhuang; Chen, Jie; Wang, Liqun

    2018-09-01

    Toxicity of heavy metals from industrialization poses critical concern, and analysis of sources associated with potential human health risks is of unique significance. Assessing human health risk of pollution sources (factored health risk) concurrently in the whole and the sub region can provide more instructive information to protect specific potential victims. In this research, we establish a new expression model of human health risk based on quantitative analysis of sources contribution in different spatial scales. The larger scale grids and their spatial codes are used to initially identify the level of pollution risk, the type of pollution source and the sensitive population at high risk. The smaller scale grids and their spatial codes are used to identify the contribution of various sources of pollution to each sub region (larger grid) and to assess the health risks posed by each source for each sub region. The results of case study show that, for children (sensitive populations, taking school and residential area as major region of activity), the major pollution source is from the abandoned lead-acid battery plant (ALP), traffic emission and agricultural activity. The new models and results of this research present effective spatial information and useful model for quantifying the hazards of source categories and human health a t complex industrial system in the future. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. Algorithms for large scale singular value analysis of spatially variant tomography systems

    International Nuclear Information System (INIS)

    Cao-Huu, Tuan; Brownell, G.; Lachiver, G.

    1996-01-01

    The problem of determining the eigenvalues of large matrices occurs often in the design and analysis of modem tomography systems. As there is an interest in solving systems containing an ever-increasing number of variables, current research effort is being made to create more robust solvers which do not depend on some special feature of the matrix for convergence (e.g. block circulant), and to improve the speed of already known and understood solvers so that solving even larger systems in a reasonable time becomes viable. Our standard techniques for singular value analysis are based on sparse matrix factorization and are not applicable when the input matrices are large because the algorithms cause too much fill. Fill refers to the increase of non-zero elements in the LU decomposition of the original matrix A (the system matrix). So we have developed iterative solutions that are based on sparse direct methods. Data motion and preconditioning techniques are critical for performance. This conference paper describes our algorithmic approaches for large scale singular value analysis of spatially variant imaging systems, and in particular of PCR2, a cylindrical three-dimensional PET imager 2 built at the Massachusetts General Hospital (MGH) in Boston. We recommend the desirable features and challenges for the next generation of parallel machines for optimal performance of our solver

  20. A factor analysis to detect factors influencing building national brand

    Directory of Open Access Journals (Sweden)

    Naser Azad

    Full Text Available Developing a national brand is one of the most important issues for development of a brand. In this study, we present factor analysis to detect the most important factors in building a national brand. The proposed study uses factor analysis to extract the most influencing factors and the sample size has been chosen from two major auto makers in Iran called Iran Khodro and Saipa. The questionnaire was designed in Likert scale and distributed among 235 experts. Cronbach alpha is calculated as 84%, which is well above the minimum desirable limit of 0.70. The implementation of factor analysis provides six factors including “cultural image of customers”, “exciting characteristics”, “competitive pricing strategies”, “perception image” and “previous perceptions”.

  1. Cluster: A New Application for Spatial Analysis of Pixelated Data for Epiphytotics.

    Science.gov (United States)

    Nelson, Scot C; Corcoja, Iulian; Pethybridge, Sarah J

    2017-12-01

    Spatial analysis of epiphytotics is essential to develop and test hypotheses about pathogen ecology, disease dynamics, and to optimize plant disease management strategies. Data collection for spatial analysis requires substantial investment in time to depict patterns in various frames and hierarchies. We developed a new approach for spatial analysis of pixelated data in digital imagery and incorporated the method in a stand-alone desktop application called Cluster. The user isolates target entities (clusters) by designating up to 24 pixel colors as nontargets and moves a threshold slider to visualize the targets. The app calculates the percent area occupied by targeted pixels, identifies the centroids of targeted clusters, and computes the relative compass angle of orientation for each cluster. Users can deselect anomalous clusters manually and/or automatically by specifying a size threshold value to exclude smaller targets from the analysis. Up to 1,000 stochastic simulations randomly place the centroids of each cluster in ranked order of size (largest to smallest) within each matrix while preserving their calculated angles of orientation for the long axes. A two-tailed probability t test compares the mean inter-cluster distances for the observed versus the values derived from randomly simulated maps. This is the basis for statistical testing of the null hypothesis that the clusters are randomly distributed within the frame of interest. These frames can assume any shape, from natural (e.g., leaf) to arbitrary (e.g., a rectangular or polygonal field). Cluster summarizes normalized attributes of clusters, including pixel number, axis length, axis width, compass orientation, and the length/width ratio, available to the user as a downloadable spreadsheet. Each simulated map may be saved as an image and inspected. Provided examples demonstrate the utility of Cluster to analyze patterns at various spatial scales in plant pathology and ecology and highlight the

  2. Socioeconomic Drivers of Environmental Pollution in China: A Spatial Econometric Analysis

    Directory of Open Access Journals (Sweden)

    Jianmin Liu

    2017-01-01

    Full Text Available This paper studies the environmental pollution and its impacts in China using prefecture-level cities and municipalities data. Moran’s I, the widely used spatial autocorrelation index, provides a fairly strong pattern of spatial clustering of environmental pollution and suggests a fairly high stability of the positive spatial correlation. To investigate the driving forces of environmental pollution and explore the relationship between fiscal decentralization, economic growth, and environmental pollution, spatial Durbin model is used for this analysis. The result shows that fiscal decentralization of local unit plays a significant role in promoting the environmental pollution and the feedback effect of fiscal decentralization on environmental pollution is also positive, though it is not significant. The relationship of GDP per capita and environmental pollution shows inverted U-shaped curve. Due to the scale effect of secondary industry, the higher the level of secondary industry development in a unit is, the easier it is to attract the secondary industry in adjacent units, which mitigates the environmental pollution in adjacent units. Densely populated areas tend to deteriorate local environment, but environmental regulation in densely populated areas is often tighter than other areas, which reduces environmental pollution to a certain extent.

  3. Spatial Analysis Along Networks Statistical and Computational Methods

    CERN Document Server

    Okabe, Atsuyuki

    2012-01-01

    In the real world, there are numerous and various events that occur on and alongside networks, including the occurrence of traffic accidents on highways, the location of stores alongside roads, the incidence of crime on streets and the contamination along rivers. In order to carry out analyses of those events, the researcher needs to be familiar with a range of specific techniques. Spatial Analysis Along Networks provides a practical guide to the necessary statistical techniques and their computational implementation. Each chapter illustrates a specific technique, from Stochastic Point Process

  4. Influence of land use and meteorological factors on the spatial distribution of Toxocara canis and Toxocara cati eggs in soil in urban areas.

    Science.gov (United States)

    Gao, Xiang; Wang, Hongbin; Li, Jianxin; Qin, Hongyu; Xiao, Jianhua

    2017-01-15

    Soil which has been contaminated by Toxocara spp. eggs is considered as one of the main infection sources of Toxocariasis in animals and humans. The present study conducted a detailed investigation into the spatial patterns of Toxocara canis (T. canis) and Toxocara cati (T. cati) eggs in soil in urban area of northeastern Mainland China, and assessed the inter-relationships between meteorological factors, land use and the distribution of the Toxocara spp. eggs. Polymerase chain reaction (PCR) was used for the determination of T. canis and T. cati eggs contamination in soil samples. Between April 2014 and May 2015, 9420 soil samples were subjected to PCR examination and 7027 sheep (74.6%) were determined to be positive for T. canis and T. cati eggs. Subsequently, we evaluated the effect of land use, and meteorological factors on the spatial distribution of T. canis and T. cati eggs based on a maximum entropy model. Jackknife analysis revealed that the area of residential land, wood and grass land and precipitation may influence the occurrence of T. canis and T. cati eggs in soil. Our findings indicate that land use and meteorological factors may be important variables affecting transmission of Toxocariasis and should be taken into account in the development of future surveillance programmes for Toxocariasis. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Spatial and temporal epidemiological analysis in the Big Data era.

    Science.gov (United States)

    Pfeiffer, Dirk U; Stevens, Kim B

    2015-11-01

    Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle

  6. Feasibility of Triticale (Tritico secale wittmack. X Cropping in Agricultural Lands of Gorgan County by Spatial Analysis Tools

    Directory of Open Access Journals (Sweden)

    maral Niazmoradi

    2017-12-01

    agricultural lands of Gorgan county by spatial analysis of GIS and evaluation of environmental variables. Material and Methods One of the most important areas for crop production in Golestan province located in north of Iran, is the Gorgan region. This area is located between latitude 54° 12.9' N and 54° 44.9' N and longitude 36° 30.6' E and 36° 58.8' E.Almost every farm in Gorgan region are rainfed. In order to spatial feasibility of triticale cropping in Gorgan county, agroecological requirements of triticale was identified and classified from scientific resources. Some environmental factors including, climatic variables such as precipitation and temperature in scale of annual, monthly and seasonal, topographic variables such as elevation, slope aspects and slops percent, and some soil characteristics as texture, EC, pH, fertility, K, P and organic matter were evaluated using geostatistics and classic methods and then thematic layers were produced in ArcGIS media. In multi-criteria assessment process, one of the most important steps is to determine each criteria weight. Generally, AHP is suitable to determine the weights of assessment factors. In this respect, the first step is to construct an AHP model consisting objectives, criteria, sub-criteria, and alternatives. Then these digital layers were classified based on ecological requirements table of triticale and they were overlaid and interpolated in GIS media and final layer were classified to four classes of high suitable, suitable, less suitable and non-suitable. The suitability analysis was based on matching between land qualities/characteristics and crop requirements. It was accomplished by weighted overlay technique (WOT in GIS. Results and Discussion The results showed that 28.8 thousand ha (44.92% of agricultural lands were located in the high suitable class (S1. This zone was observed occasionally in north and northwest parts of studied region. This zone had the high fertility, high organic matter percent (2

  7. GeoXp : An R Package for Exploratory Spatial Data Analysis

    Directory of Open Access Journals (Sweden)

    Thibault Laurent

    2012-04-01

    Full Text Available We present GeoXp, an R package implementing interactive graphics for exploratory spatial data analysis. We use a data set concerning public schools of the French MidiPyrenees region to illustrate the use of these exploratory techniques based on the coupling between a statistical graph and a map. Besides elementary plots like boxplots,histograms or simple scatterplots, GeoXp also couples maps with Moran scatterplots, variogram clouds, Lorenz curves and other graphical tools. In order to make the most of the multidimensionality of the data, GeoXp includes dimension reduction techniques such as principal components analysis and cluster analysis whose results are also linked to the map.

  8. Factor Analysis for Finding Invariant Neural Descriptors of Human Emotions

    Directory of Open Access Journals (Sweden)

    Vitor Pereira

    2018-01-01

    Full Text Available A major challenge in decoding human emotions from electroencephalogram (EEG data is finding representations that are invariant to inter- and intrasubject differences. Most of the previous studies are focused in building an individual discrimination model for every subject (subject dependent model. Building subject-independent models is a harder problem due to the high data variability between different subjects and different experiments with the same subject. This paper explores, for the first time, the Factor Analysis as an efficient technique to extract temporal and spatial EEG features suitable to build brain-computer interface for decoding human emotions across various subjects. Our findings show that early waves (temporal window of 200–400 ms after the stimulus onset carry more information about the valence of the emotion. Also, spatial location of features, with a stronger impact on the emotional valence, occurs in the parietal and occipital regions of the brain. All discrimination models (NN, SVM, kNN, and RF demonstrate better discrimination rate of the positive valence. These results match closely experimental psychology hypothesis that, during early periods after the stimulus presentation, the brain response—to images with highly positive valence—is stronger.

  9. Fine scale spatial variability of microbial pesticide degradation in soil: scales, controlling factors, and implications

    DEFF Research Database (Denmark)

    Dechesne, Arnaud; Badawi, N.; Aamand, Jens

    2014-01-01

    across pesticide classes: they include some soil characteristics (pH) and some agricultural management practices (pesticide application, tillage), while other potential controlling factors have more conflicting effects depending on the site or the pesticide. Evidence demonstrating the importance......Pesticide biodegradation is a soil microbial function of critical importance for modern agriculture and its environmental impact. While it was once assumed that this activity was homogeneously distributed at the field scale, mounting evidence indicates that this is rarely the case. Here, we...... critically examine the literature on spatial variability of pesticide biodegradation in agricultural soil. We discuss the motivations, methods, and main findings of the primary literature. We found significant diversity in the approaches used to describe and quantify spatial heterogeneity, which complicates...

  10. Estimation of physiological parameters using knowledge-based factor analysis of dynamic nuclear medicine image sequences

    International Nuclear Information System (INIS)

    Yap, J.T.; Chen, C.T.; Cooper, M.

    1995-01-01

    The authors have previously developed a knowledge-based method of factor analysis to analyze dynamic nuclear medicine image sequences. In this paper, the authors analyze dynamic PET cerebral glucose metabolism and neuroreceptor binding studies. These methods have shown the ability to reduce the dimensionality of the data, enhance the image quality of the sequence, and generate meaningful functional images and their corresponding physiological time functions. The new information produced by the factor analysis has now been used to improve the estimation of various physiological parameters. A principal component analysis (PCA) is first performed to identify statistically significant temporal variations and remove the uncorrelated variations (noise) due to Poisson counting statistics. The statistically significant principal components are then used to reconstruct a noise-reduced image sequence as well as provide an initial solution for the factor analysis. Prior knowledge such as the compartmental models or the requirement of positivity and simple structure can be used to constrain the analysis. These constraints are used to rotate the factors to the most physically and physiologically realistic solution. The final result is a small number of time functions (factors) representing the underlying physiological processes and their associated weighting images representing the spatial localization of these functions. Estimation of physiological parameters can then be performed using the noise-reduced image sequence generated from the statistically significant PCs and/or the final factor images and time functions. These results are compared to the parameter estimation using standard methods and the original raw image sequences. Graphical analysis was performed at the pixel level to generate comparable parametric images of the slope and intercept (influx constant and distribution volume)

  11. The geography of post-disaster mental health: spatial patterning of psychological vulnerability and resilience factors in New York City after Hurricane Sandy.

    Science.gov (United States)

    Gruebner, Oliver; Lowe, Sarah R; Sampson, Laura; Galea, Sandro

    2015-06-10

    Only very few studies have investigated the geographic distribution of psychological resilience and associated mental health outcomes after natural or man made disasters. Such information is crucial for location-based interventions that aim to promote recovery in the aftermath of disasters. The purpose of this study therefore was to investigate geographic variability of (1) posttraumatic stress (PTS) and depression in a Hurricane Sandy affected population in NYC and (2) psychological vulnerability and resilience factors among affected areas in NYC boroughs. Cross-sectional telephone survey data were collected 13 to 16 months post-disaster from household residents (N = 418 adults) in NYC communities that were most heavily affected by the hurricane. The Posttraumatic Stress Checklist for DSM-5 (PCL-5) was applied for measuring posttraumatic stress and the nine-item Patient Health Questionnaire (PHQ-9) was used for measuring depression. We applied spatial autocorrelation and spatial regimes regression analyses, to test for spatial clusters of mental health outcomes and to explore whether associations between vulnerability and resilience factors and mental health differed among New York City's five boroughs. Mental health problems clustered predominantly in neighborhoods that are geographically more exposed towards the ocean indicating a spatial variation of risk within and across the boroughs. We further found significant variation in associations between vulnerability and resilience factors and mental health. Race/ethnicity (being Asian or non-Hispanic black) and disaster-related stressors were vulnerability factors for mental health symptoms in Queens, and being employed and married were resilience factors for these symptoms in Manhattan and Staten Island. In addition, parental status was a vulnerability factor in Brooklyn and a resilience factor in the Bronx. We conclude that explanatory characteristics may manifest as psychological vulnerability and resilience

  12. Spatial analysis of Northern Goshawk Territories in the Black Hills, South Dakota

    Science.gov (United States)

    Klaver, Robert W.; Backlund, Douglas; Bartelt, Paul E.; Erickson, Michael G.; Knowles, Craig J.; Knowles, Pamela R.; Wimberly, Michael

    2012-01-01

    The Northern Goshawk (Accipiter gentilis) is the largest of the three North American species ofAccipiter and is more closely associated with older forests than are the other species. Its reliance on older forests has resulted in concerns about its status, extensive research into its habitat relationships, and litigation. Our objective was to model the spatial patterns of goshawk territories in the Black Hills, South Dakota, to make inferences about the underlying processes. We used a modification of Ripley's K function that accounts for inhomogeneous intensity to determine whether territoriality or habitat determined the spacing of goshawks in the Black Hills, finding that habitat conditions rather than territoriality were the determining factor. A spatial model incorporating basal area of trees in a stand of forest, canopy cover, age of trees >23 cm in diameter, number of trees per hectare, and geographic coordinates provided good fit to the spatial patterns of territories. There was no indication of repulsion at close distances that would imply spacing was determined by territoriality. These findings contrast with those for the Kaibab Plateau, Arizona, where territoriality is an important limiting factor. Forest stands where the goshawk nested historically are now younger and have trees of smaller diameter, probably having been modified by logging, fire, and insects. These results have important implications for the goshawk's ecology in the Black Hills with respect to mortality, competition, forest fragmentation, and nest-territory protection.

  13. Spatial Analysis Of Human Capital Structures

    Directory of Open Access Journals (Sweden)

    Gajdos Artur

    2014-12-01

    Full Text Available The main purpose of this paper is to analyse the interdependence between labour productivity and the occupational structure of human capital in a spatial cross-section. Research indicates (see Fischer 2009 the possibility to assess the impact of the quality of human capital (measured by means of the level of education on labour productivity in a spatial cross-section.

  14. An operational modal analysis method in frequency and spatial domain

    Science.gov (United States)

    Wang, Tong; Zhang, Lingmi; Tamura, Yukio

    2005-12-01

    A frequency and spatial domain decomposition method (FSDD) for operational modal analysis (OMA) is presented in this paper, which is an extension of the complex mode indicator function (CMIF) method for experimental modal analysis (EMA). The theoretical background of the FSDD method is clarified. Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density (PSD) is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.

  15. Spatial Analysis GIS Model for Identifying the Risk Induced by Landslides. A Case Study: A.T.U. of Șieu

    Directory of Open Access Journals (Sweden)

    Dorel Colniţă

    2016-11-01

    Full Text Available The risk induced by landslides on residential infrastructure, transport infrastructure and agricultural land causes problems of local management that need to be solved by reducing negative effects and decrease the frequency of their occurrence. This study followed the development and implementation of a model for identifying the risk induced by landslides through the analysis of spatial occurrence probability for landslides at the administrative territorial unit of Șieu, following the semi-quantitative method governed in Romania by G.D. no 447/2003 and then through the exposure of housing infrastructure at landslides was possible to frame landslides on risk classes. The entire approach was based on GIS spatial analysis, creating a specific detailed database of causing and triggering factors of landslides and not at least, a database for risk receptors, in this study, represented by the constructions of villages associated with the studied administrative territorial units. The final result of the model highlights the framing of constructions on qualitative risk classes at landslides, revealing the elements of infrastructure that need post and pre event measures of protection.

  16. Analysis of the impact of immigration on labour market using spatial models

    Science.gov (United States)

    Polonyankina, Tatiana

    2017-07-01

    This paper investigates the impact of immigration on employment and unemployment of a host country. The question to answer is: How does employment/unemployment in the host country change after an increase in number of immigrants? The analysis is taking into account only legal immigrants in recession period. The model is combining classical regression of cross-sectional data with spatial econometrics models where cross-section dependencies are captured by a spatial matrix. The intention is by using spatial models analyse the sensitivity of employment/unemployment rate on change in a share of immigration in a region. The used panel data are based on the Labour force survey and on available macro data in Eurostat for 3 European countries (Germany, Austria and Czech Republic) grouped into cells by NUTS regions in a recession period.

  17. Alaskan soil carbon stocks: spatial variability and dependence on environmental factors

    Directory of Open Access Journals (Sweden)

    U. Mishra

    2012-09-01

    Full Text Available The direction and magnitude of soil organic carbon (SOC changes in response to climate change depend on the spatial and vertical distributions of SOC. We estimated spatially resolved SOC stocks from surface to C horizon, distinguishing active-layer and permafrost-layer stocks, based on geospatial analysis of 472 soil profiles and spatially referenced environmental variables for Alaska. Total Alaska state-wide SOC stock was estimated to be 77 Pg, with 61% in the active-layer, 27% in permafrost, and 12% in non-permafrost soils. Prediction accuracy was highest for the active-layer as demonstrated by highest ratio of performance to deviation (1.5. Large spatial variability was predicted, with whole-profile, active-layer, and permafrost-layer stocks ranging from 1–296 kg C m−2, 2–166 kg m−2, and 0–232 kg m−2, respectively. Temperature and soil wetness were found to be primary controllers of whole-profile, active-layer, and permafrost-layer SOC stocks. Secondary controllers, in order of importance, were found to be land cover type, topographic attributes, and bedrock geology. The observed importance of soil wetness rather than precipitation on SOC stocks implies that the poor representation of high-latitude soil wetness in Earth system models may lead to large uncertainty in predicted SOC stocks under future climate change scenarios. Under strict caveats described in the text and assuming temperature changes from the A1B Intergovernmental Panel on Climate Change emissions scenario, our geospatial model indicates that the equilibrium average 2100 Alaska active-layer depth could deepen by 11 cm, resulting in a thawing of 13 Pg C currently in permafrost. The equilibrium SOC loss associated with this warming would be highest under continuous permafrost (31%, followed by discontinuous (28%, isolated (24.3%, and sporadic (23.6% permafrost areas. Our high-resolution mapping of soil carbon stock reveals the

  18. Investigation of the marked and long-standing spatial inhomogeneity of the Hungarian suicide rate: a spatial regression approach.

    Science.gov (United States)

    Balint, Lajos; Dome, Peter; Daroczi, Gergely; Gonda, Xenia; Rihmer, Zoltan

    2014-02-01

    In the last century Hungary had astonishingly high suicide rates characterized by marked regional within-country inequalities, a spatial pattern which has been quite stable over time. To explain the above phenomenon at the level of micro-regions (n=175) in the period between 2005 and 2011. Our dependent variable was the age and gender standardized mortality ratio (SMR) for suicide while explanatory variables were factors which are supposed to influence suicide risk, such as measures of religious and political integration, travel time accessibility of psychiatric services, alcohol consumption, unemployment and disability pensionery. When applying the ordinary least squared regression model, the residuals were found to be spatially autocorrelated, which indicates the violation of the assumption on the independence of error terms and - accordingly - the necessity of application of a spatial autoregressive (SAR) model to handle this problem. According to our calculations the SARlag model was a better way (versus the SARerr model) of addressing the problem of spatial autocorrelation, furthermore its substantive meaning is more convenient. SMR was significantly associated with the "political integration" variable in a negative and with "lack of religious integration" and "disability pensionery" variables in a positive manner. Associations were not significant for the remaining explanatory variables. Several important psychiatric variables were not available at the level of micro-regions. We conducted our analysis on aggregate data. Our results may draw attention to the relevance and abiding validity of the classic Durkheimian suicide risk factors - such as lack of social integration - apropos of the spatial pattern of Hungarian suicides. © 2013 Published by Elsevier B.V.

  19. Factor analysis of multivariate data

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, A.A.; Mahadevan, R.

    A brief introduction to factor analysis is presented. A FORTRAN program, which can perform the Q-mode and R-mode factor analysis and the singular value decomposition of a given data matrix is presented in Appendix B. This computer program, uses...

  20. Relationships between hand laterality and verbal and spatial skills in 436 healthy adults balanced for handedness.

    Science.gov (United States)

    Mellet, E; Jobard, G; Zago, L; Crivello, F; Petit, L; Joliot, M; Mazoyer, B; Tzourio-Mazoyer, N

    2014-01-01

    The relationship between manual laterality and cognitive skills remains highly controversial. Some studies have reported that strongly lateralised participants had higher cognitive performance in verbal and visuo-spatial domains compared to non-lateralised participants; however, others found the opposite. Moreover, some have suggested that familial sinistrality and sex might interact with individual laterality factors to alter cognitive skills. The present study addressed these issues in 237 right-handed and 199 left-handed individuals. Performance tests covered various aspects of verbal and spatial cognition. A principal component analysis yielded two verbal and one spatial factor scores. Participant laterality assessments included handedness, manual preference strength, asymmetry of motor performance, and familial sinistrality. Age, sex, education level, and brain volume were also considered. No effect of handedness was found, but the mean factor scores in verbal and spatial domains increased with right asymmetry in motor performance. Performance was reduced in participants with a familial history of left-handedness combined with a non-maximal preference strength in the dominant hand. These results elucidated some discrepancies among previous findings in laterality factors and cognitive skills. Laterality factors had small effects compared to the adverse effects of age for spatial cognition and verbal memory, the positive effects of education for all three domains, and the effect of sex for spatial cognition.

  1. Importance of spatial factors and temporal scales in environmental risk assessment in marine ecosystems

    International Nuclear Information System (INIS)

    Grebenkov, A.; Linkov, I.; Andrizhievski, A.; Lukashevich, A.; Trifonov, A.

    2004-01-01

    Coastal areas adjacent to the Black Sea, particularly in Crimea, have suffered from inappropriate human activities, poorly regulated industry and former naval bases. Industrial and municipal wastewater pollutants draining into the three major European rivers (the Danube, Dniestr, and Dnieper) and dumping in the open sea result in an enormous increase in contamination level of ecosystems of the Black Sea. In spite of this, Crimea and its adjacent waters is still a globally important center of biological diversity, with an enormous and exciting range of habitats within a comparatively small area. The problem now is to evaluate economically feasible remediation and ecologically sustainable cleanup/reuse alternatives for the most contaminated sites of this area. One of the principal methodological components of such evaluation is a risk-based decision protocol that provides support in analysis of ecological value and reuse options for a chosen site. This paper presents the results of development of a spatially explicit risk assessment technique to be implemented as a part of the decision-making process and gives an example of its application to contaminated marine ecosystems. The model is suggested that takes into account several principal assumptions: (i) spatial heterogeneity of contamination of forage is known and mapped within known location of receptor's habitat, and (ii) the receptor movement and timescale are determined by location, volume and attractiveness of local habitat and forage resources. This implies two models: Spatially Explicit Exposure Assessment Model that calculates internal exposure resulting from ingestion of contaminated feeds, and Probabilistic Receptor Migration Model that generates motivation of behaviour of a receptor while feeding. In the first model, time-dependent accumulation of contamination in receptor tissue is defined by the differential balance equation that takes into account forage consumption rate and excretion rate. In the

  2. Neighborhood social capital and crime victimization: comparison of spatial regression analysis and hierarchical regression analysis.

    Science.gov (United States)

    Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro

    2012-11-01

    Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright

  3. The spatial expression and regulation of transcription factors IDEF1 and IDEF2

    Science.gov (United States)

    Kobayashi, Takanori; Ogo, Yuko; Aung, May Sann; Nozoye, Tomoko; Itai, Reiko Nakanishi; Nakanishi, Hiromi; Yamakawa, Takashi; Nishizawa, Naoko K.

    2010-01-01

    Background and Aims Under conditions of low iron availability, rice plants induce genes involved in iron uptake and utilization. The iron deficiency-responsive cis-acting element binding factors 1 and 2 (IDEF1 and IDEF2) regulate transcriptional response to iron deficiency in rice roots. Clarification of the functions of IDEF1 and IDEF2 could uncover the gene regulation mechanism. Methods Spatial patterns of IDEF1 and IDEF2 expression were analysed by histochemical staining of IDEF1 and IDEF2 promoter-GUS transgenic rice lines. Expression patterns of the target genes of IDEF1 and IDEF2 were analysed using transformants with induced or repressed expression of IDEF1 or IDEF2 grown in iron-rich or in iron-deficient solutions for 1 d. Key Results IDEF1 and IDEF2 were highly expressed in the basal parts of the lateral roots and vascular bundles. IDEF1 and IDEF2 expression was dominant in leaf mesophyll and vascular cells, respectively. These expression patterns were similar under both iron-deficient and iron-sufficient conditions. IDEF1 was strongly expressed in pollen, ovaries, the aleurone layer and embryo. IDEF2 was expressed in pollen, ovaries and the dorsal vascular region of the endosperm. During seed germination, IDEF1 and IDEF2 were expressed in the endosperm and embryo. Expression of IDEF1 target genes was regulated in iron-rich roots similar to early iron-deficiency stages. In addition, the expression patterns of IDEF2 target genes were similar between iron-rich conditions and early or subsequent iron deficiency. Conclusions IDEF1 and IDEF2 are constitutively expressed during both vegetative and reproductive stages. The spatial expression patterns of IDEF1 and IDEF2 overlap with their target genes in restricted cell types, but not in all cells. The spatial expression patterns and gene regulation of IDEF1 and IDEF2 in roots are generally conserved under conditions of iron sufficiency and deficiency, suggesting complicated interactions with unknown factors for

  4. Few-mode fiber, splice and SDM component characterization by spatially-diverse optical vector network analysis

    DEFF Research Database (Denmark)

    Rommel, Simon; Mendinueta, José Manuel Delgado; Klaus, Werner

    2017-01-01

    This paper discusses spatially diverse optical vector network analysis for space division multiplexing (SDM) component and system characterization, which is becoming essential as SDM is widely considered to increase the capacity of optical communication systems. Characterization of a 108-channel ...... in the few-mode multi-core fiber and their impact on system IL and MDL are analyzed, finding splices to cause significant mode-mixing and to be non-negligible in system capacity analysis.......This paper discusses spatially diverse optical vector network analysis for space division multiplexing (SDM) component and system characterization, which is becoming essential as SDM is widely considered to increase the capacity of optical communication systems. Characterization of a 108-channel...... photonic lantern spatial multiplexer, coupled to a 36-core 3-mode fiber, is experimentally demonstrated, extracting the full impulse response and complex transfer function matrices as well as insertion loss (IL) and mode-dependent loss (MDL) data. Moreover, the mode-mixing behavior of fiber splices...

  5. Factor analysis and scintigraphy

    International Nuclear Information System (INIS)

    Di Paola, R.; Penel, C.; Bazin, J.P.; Berche, C.

    1976-01-01

    The goal of factor analysis is usually to achieve reduction of a large set of data, extracting essential features without previous hypothesis. Due to the development of computerized systems, the use of largest sampling, the possibility of sequential data acquisition and the increase of dynamic studies, the problem of data compression can be encountered now in routine. Thus, results obtained for compression of scintigraphic images were first presented. Then possibilities given by factor analysis for scan processing were discussed. At last, use of this analysis for multidimensional studies and specially dynamic studies were considered for compression and processing [fr

  6. Modern methodology and applications in spatial-temporal modeling

    CERN Document Server

    Matsui, Tomoko

    2015-01-01

    This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component an...

  7. Road infrastructure, spatial spillover and county economic growth

    Science.gov (United States)

    Hu, Zhenhua; Luo, Shuang

    2017-09-01

    This paper analyzes the spatial spillover effect of road infrastructure on the economic growth of poverty-stricken counties, based on the spatial Durbin model, by using the panel data of 37 poor counties in Hunan province from 2006 to 2015. The results showed that there is a significant spatial dependence of economic growth in Poor Counties. Road infrastructure has a positive impact on economic growth, and the results will be overestimated without considering spatial factors. Considering the spatial factors, the road infrastructure will promote the economic growth of the surrounding areas through the spillover effect, but the spillover effect is restricted by the distance factor. Capital investment is the biggest factor of economic growth in poor counties, followed by urbanization, labor force and regional openness.

  8. Spatial analysis of radiocesium food contamination in rural settlements of Belarus

    International Nuclear Information System (INIS)

    Krivoruchko, K.; Makejchik, A.

    1997-01-01

    An analysis of 53207 records of cesium 137 contents in 83 types of food products obtained in 1993 in Belarus was carried out. Internal exposure from intake of eight selected food components has been estimated. To map the non-uniformly distributed data, different geostatistical approaches are used. The results of spatial analysis of long term internal dose loads on populations under high radiation risk can be used in decision making. (authors). 9 refs., 1 tab., 4 figs

  9. ANÁLISE ESPACIAL DE DADOS GEOGRÁFICOS: A UTILIZAÇÃO DA EXPLORATORY SPATIAL DATA ANALYSIS - ESDA PARA IDENTIFICAÇÃO DE ÁREAS CRÍTICAS DE ACIDENTES DE TRÂNSITO NO MUNICÍPIO DE SÃO CARLOS (SP / Spatial analysis of Geographic Data: the use of the Exploratory Spatial Data Analysis - ESDA for identification of critical areas of traffic accidents in the São Carlos (SP

    Directory of Open Access Journals (Sweden)

    Msc. Luciano dos Santos

    2006-12-01

    Full Text Available The Spatial Analysis is one of the some forms of if understanding as some spatial events arerelationships. Currently, this technique to see being incorporated for diverse areas of the knowledgesuch as, the health areas, has carried and transit. This work has as objective to demonstrate theapplication of the Exploratory Spatial Data Analysis - ESDA, for the identification of criticalareas of traffic accidents in cities of average transport, having as study area the São Carlos - SP -Brazil. In this study it was possible to demonstrate to the viability of use of this technique, since itwas possible to incorporate new parameters of analysis of the accidents being provided new waysfor identification of problematic areas, facilitating its analysis.

  10. An analysis of spatial representativeness of air temperature monitoring stations

    Science.gov (United States)

    Liu, Suhua; Su, Hongbo; Tian, Jing; Wang, Weizhen

    2018-05-01

    Surface air temperature is an essential variable for monitoring the atmosphere, and it is generally acquired at meteorological stations that can provide information about only a small area within an r m radius ( r-neighborhood) of the station, which is called the representable radius. In studies on a local scale, ground-based observations of surface air temperatures obtained from scattered stations are usually interpolated using a variety of methods without ascertaining their effectiveness. Thus, it is necessary to evaluate the spatial representativeness of ground-based observations of surface air temperature before conducting studies on a local scale. The present study used remote sensing data to estimate the spatial distribution of surface air temperature using the advection-energy balance for air temperature (ADEBAT) model. Two target stations in the study area were selected to conduct an analysis of spatial representativeness. The results showed that one station (AWS 7) had a representable radius of about 400 m with a possible error of less than 1 K, while the other station (AWS 16) had the radius of about 250 m. The representable radius was large when the heterogeneity of land cover around the station was small.

  11. Model Interpretation of Topological Spatial Analysis for the Visually Impaired (Blind Implemented in Google Maps

    Directory of Open Access Journals (Sweden)

    Marcelo Franco Porto

    2013-06-01

    Full Text Available The technological innovations promote the availability of geographic information on the Internet through Web GIS such as Google Earth and Google Maps. These systems contribute to the teaching and diffusion of geographical knowledge that instigates the recognition of the space we live in, leading to the creation of a spatial identity. In these products available on the Web, the interpretation and analysis of spatial information gives priority to one of the human senses: vision. Due to the fact that this representation of information is transmitted visually (image and vectors, a portion of the population is excluded from part of this knowledge because categories of analysis of geographic data such as borders, territory, and space can only be understood by people who can see. This paper deals with the development of a model of interpretation of topological spatial analysis based on the synthesis of voice and sounds that can be used by the visually impaired (blind.The implementation of a prototype in Google Maps and the usability tests performed are also examined. For the development work it was necessary to define the model of topological spatial analysis, focusing on computational implementation, which allows users to interpret the spatial relationships of regions (countries, states and municipalities, recognizing its limits, neighborhoods and extension beyond their own spatial relationships . With this goal in mind, several interface and usability guidelines were drawn up to be used by the visually impaired (blind. We conducted a detailed study of the Google Maps API (Application Programming Interface, which was the environment selected for prototype development, and studied the information available for the users of that system. The prototype was developed based on the synthesis of voice and sounds that implement the proposed model in C # language and in .NET environment. To measure the efficiency and effectiveness of the prototype, usability

  12. Preliminary frequency-domain analysis for the reconstructed spatial resolution of muon tomography

    Science.gov (United States)

    Yu, B.; Zhao, Z.; Wang, X.; Wang, Y.; Wu, D.; Zeng, Z.; Zeng, M.; Yi, H.; Luo, Z.; Yue, X.; Cheng, J.

    2014-11-01

    Muon tomography is an advanced technology to non-destructively detect high atomic number materials. It exploits the multiple Coulomb scattering information of muon to reconstruct the scattering density image of the traversed object. Because of the statistics of muon scattering, the measurement error of system and the data incompleteness, the reconstruction is always accompanied with a certain level of interference, which will influence the reconstructed spatial resolution. While statistical noises can be reduced by extending the measuring time, system parameters determine the ultimate spatial resolution that one system can reach. In this paper, an effective frequency-domain model is proposed to analyze the reconstructed spatial resolution of muon tomography. The proposed method modifies the resolution analysis in conventional computed tomography (CT) to fit the different imaging mechanism in muon scattering tomography. The measured scattering information is described in frequency domain, then a relationship between the measurements and the original image is proposed in Fourier domain, which is named as "Muon Central Slice Theorem". Furthermore, a preliminary analytical expression of the ultimate reconstructed spatial is derived, and the simulations are performed for validation. While the method is able to predict the ultimate spatial resolution of a given system, it can also be utilized for the optimization of system design and construction.

  13. Preliminary frequency-domain analysis for the reconstructed spatial resolution of muon tomography

    International Nuclear Information System (INIS)

    Yu, B.; Zhao, Z.; Wang, X.; Wang, Y.; Wu, D.; Zeng, Z.; Zeng, M.; Yi, H.; Luo, Z.; Yue, X.; Cheng, J.

    2014-01-01

    Muon tomography is an advanced technology to non-destructively detect high atomic number materials. It exploits the multiple Coulomb scattering information of muon to reconstruct the scattering density image of the traversed object. Because of the statistics of muon scattering, the measurement error of system and the data incompleteness, the reconstruction is always accompanied with a certain level of interference, which will influence the reconstructed spatial resolution. While statistical noises can be reduced by extending the measuring time, system parameters determine the ultimate spatial resolution that one system can reach. In this paper, an effective frequency-domain model is proposed to analyze the reconstructed spatial resolution of muon tomography. The proposed method modifies the resolution analysis in conventional computed tomography (CT) to fit the different imaging mechanism in muon scattering tomography. The measured scattering information is described in frequency domain, then a relationship between the measurements and the original image is proposed in Fourier domain, which is named as M uon Central Slice Theorem . Furthermore, a preliminary analytical expression of the ultimate reconstructed spatial is derived, and the simulations are performed for validation. While the method is able to predict the ultimate spatial resolution of a given system, it can also be utilized for the optimization of system design and construction

  14. Ultrasonic motion analysis system - measurement of temporal and spatial gait parameters

    NARCIS (Netherlands)

    Huitema, RB; Hof, AL; Postema, K

    The duration of stance and swing phase and step and stride length are important parameters in human gait. In this technical note a low-cost ultrasonic motion analysis system is described that is capable of measuring these temporal and spatial parameters while subjects walk on the floor. By using the

  15. Spatial analysis of hypocenter to fault relationships for determining fault process zone width in Japan

    International Nuclear Information System (INIS)

    Arnold, Bill Walter; Roberts, Barry L.; McKenna, Sean Andrew; Coburn, Timothy C.

    2004-01-01

    Preliminary investigation areas (PIA) for a potential repository of high-level radioactive waste must be evaluated by NUMO with regard to a number of qualifying factors. One of these factors is related to earthquakes and fault activity. This study develops a spatial statistical assessment method that can be applied to the active faults in Japan to perform such screening evaluations. This analysis uses the distribution of seismicity near faults to define the width of the associated process zone. This concept is based on previous observations of aftershock earthquakes clustered near active faults and on the assumption that such seismic activity is indicative of fracturing and associated impacts on bedrock integrity. Preliminary analyses of aggregate data for all of Japan confirmed that the frequency of earthquakes is higher near active faults. Data used in the analysis were obtained from NUMO and consist of three primary sources: (1) active fault attributes compiled in a spreadsheet, (2) earthquake hypocenter data, and (3) active fault locations. Examination of these data revealed several limitations with regard to the ability to associate fault attributes from the spreadsheet to locations of individual fault trace segments. In particular, there was no direct link between attributes of the active faults in the spreadsheet and the active fault locations in the GIS database. In addition, the hypocenter location resolution in the pre-1983 data was less accurate than for later data. These pre-1983 hypocenters were eliminated from further analysis

  16. Spatial analysis of hypocenter to fault relationships for determining fault process zone width in Japan.

    Energy Technology Data Exchange (ETDEWEB)

    Arnold, Bill Walter; Roberts, Barry L.; McKenna, Sean Andrew; Coburn, Timothy C. (Abilene Christian University, Abilene, TX)

    2004-09-01

    Preliminary investigation areas (PIA) for a potential repository of high-level radioactive waste must be evaluated by NUMO with regard to a number of qualifying factors. One of these factors is related to earthquakes and fault activity. This study develops a spatial statistical assessment method that can be applied to the active faults in Japan to perform such screening evaluations. This analysis uses the distribution of seismicity near faults to define the width of the associated process zone. This concept is based on previous observations of aftershock earthquakes clustered near active faults and on the assumption that such seismic activity is indicative of fracturing and associated impacts on bedrock integrity. Preliminary analyses of aggregate data for all of Japan confirmed that the frequency of earthquakes is higher near active faults. Data used in the analysis were obtained from NUMO and consist of three primary sources: (1) active fault attributes compiled in a spreadsheet, (2) earthquake hypocenter data, and (3) active fault locations. Examination of these data revealed several limitations with regard to the ability to associate fault attributes from the spreadsheet to locations of individual fault trace segments. In particular, there was no direct link between attributes of the active faults in the spreadsheet and the active fault locations in the GIS database. In addition, the hypocenter location resolution in the pre-1983 data was less accurate than for later data. These pre-1983 hypocenters were eliminated from further analysis.

  17. The Impact of the Brain-Derived Neurotrophic Factor Gene on Trauma and Spatial Processing

    Directory of Open Access Journals (Sweden)

    Jessica K. Miller

    2017-11-01

    Full Text Available The influence of genes and the environment on the development of Post-Traumatic Stress Disorder (PTSD continues to motivate neuropsychological research, with one consistent focus being the Brain-Derived Neurotrophic Factor (BDNF gene, given its impact on the integrity of the hippocampal memory system. Research into human navigation also considers the BDNF gene in relation to hippocampal dependent spatial processing. This speculative paper brings together trauma and spatial processing for the first time and presents exploratory research into their interactions with BDNF. We propose that quantifying the impact of BDNF on trauma and spatial processing is critical and may well explain individual differences in clinical trauma treatment outcomes and in navigation performance. Research has already shown that the BDNF gene influences PTSD severity and prevalence as well as navigation behaviour. However, more data are required to demonstrate the precise hippocampal dependent processing mechanisms behind these influences in different populations and environmental conditions. This paper provides insight from recent studies and calls for further research into the relationship between allocentric processing, trauma processing and BDNF. We argue that research into these neural mechanisms could transform PTSD clinical practice and professional support for individuals in trauma-exposing occupations such as emergency response, law enforcement and the military.

  18. The Impact of the Brain-Derived Neurotrophic Factor Gene on Trauma and Spatial Processing.

    Science.gov (United States)

    Miller, Jessica K; McDougall, Siné; Thomas, Sarah; Wiener, Jan

    2017-11-27

    The influence of genes and the environment on the development of Post-Traumatic Stress Disorder (PTSD) continues to motivate neuropsychological research, with one consistent focus being the Brain-Derived Neurotrophic Factor (BDNF) gene, given its impact on the integrity of the hippocampal memory system. Research into human navigation also considers the BDNF gene in relation to hippocampal dependent spatial processing. This speculative paper brings together trauma and spatial processing for the first time and presents exploratory research into their interactions with BDNF. We propose that quantifying the impact of BDNF on trauma and spatial processing is critical and may well explain individual differences in clinical trauma treatment outcomes and in navigation performance. Research has already shown that the BDNF gene influences PTSD severity and prevalence as well as navigation behaviour. However, more data are required to demonstrate the precise hippocampal dependent processing mechanisms behind these influences in different populations and environmental conditions. This paper provides insight from recent studies and calls for further research into the relationship between allocentric processing, trauma processing and BDNF. We argue that research into these neural mechanisms could transform PTSD clinical practice and professional support for individuals in trauma-exposing occupations such as emergency response, law enforcement and the military.

  19. Hydrology Affects Environmental and Spatial Structuring of Microalgal Metacommunities in Tropical Pacific Coast Wetlands.

    Directory of Open Access Journals (Sweden)

    Carmen Rojo

    Full Text Available The alternating climate between wet and dry periods has important effects on the hydrology and therefore on niche-based processes of water bodies in tropical areas. Additionally, assemblages of microorganism can show spatial patterns, in the form of a distance decay relationship due to their size or life form. We aimed to test spatial and environmental effects, modulated by a seasonal flooding climatic pattern, on the distribution of microalgae in 30 wetlands of a tropical dry forest region: the Pacific coast of Costa Rica and Nicaragua. Three surveys were conducted corresponding to the beginning, the highest peak, and the end of the hydrological year during the wet season, and species abundance and composition of planktonic and benthic microalgae was determined. Variation partitioning analysis (as explained by spatial distance or environmental factors was applied to each seasonal dataset by means of partial redundancy analysis. Our results show that microalgal assemblages were structured by spatial and environmental factors depending on the hydrological period of the year. At the onset of hydroperiod and during flooding, neutral effects dominated community dynamics, but niche-based local effects resulted in more structured algal communities at the final periods of desiccating water bodies. Results suggest that climate-mediated effects on hydrology can influence the relative role of spatial and environmental factors on metacommunities of microalgae. Such variability needs to be accounted in order to describe accurately community dynamics in tropical coastal wetlands.

  20. Hydrology Affects Environmental and Spatial Structuring of Microalgal Metacommunities in Tropical Pacific Coast Wetlands.

    Science.gov (United States)

    Rojo, Carmen; Mesquita-Joanes, Francesc; Monrós, Juan S; Armengol, Javier; Sasa, Mahmood; Bonilla, Fabián; Rueda, Ricardo; Benavent-Corai, José; Piculo, Rubén; Segura, M Matilde

    2016-01-01

    The alternating climate between wet and dry periods has important effects on the hydrology and therefore on niche-based processes of water bodies in tropical areas. Additionally, assemblages of microorganism can show spatial patterns, in the form of a distance decay relationship due to their size or life form. We aimed to test spatial and environmental effects, modulated by a seasonal flooding climatic pattern, on the distribution of microalgae in 30 wetlands of a tropical dry forest region: the Pacific coast of Costa Rica and Nicaragua. Three surveys were conducted corresponding to the beginning, the highest peak, and the end of the hydrological year during the wet season, and species abundance and composition of planktonic and benthic microalgae was determined. Variation partitioning analysis (as explained by spatial distance or environmental factors) was applied to each seasonal dataset by means of partial redundancy analysis. Our results show that microalgal assemblages were structured by spatial and environmental factors depending on the hydrological period of the year. At the onset of hydroperiod and during flooding, neutral effects dominated community dynamics, but niche-based local effects resulted in more structured algal communities at the final periods of desiccating water bodies. Results suggest that climate-mediated effects on hydrology can influence the relative role of spatial and environmental factors on metacommunities of microalgae. Such variability needs to be accounted in order to describe accurately community dynamics in tropical coastal wetlands.

  1. Multidimensional Analysis and Location Intelligence Application for Spatial Data Warehouse Hotspot in Indonesia using SpagoBI

    Science.gov (United States)

    Uswatun Hasanah, Gamma; Trisminingsih, Rina

    2016-01-01

    Spatial data warehouse refers to data warehouse which has a spatial component that represents the geographic location of the position or an object on the Earth's surface. Spatial data warehouse can be visualized in the form of a crosstab tables, graphs, and maps. Spatial data warehouse of hotspot in Indonesia has been constructed by researchers from FIRM NASA 2006-2015. This research develops multidimensional analysis module and location intelligence module using SpagoBI. The multidimensional analysis module is able to visualize online analytical processing (OLAP). The location intelligence module creates dynamic map visualization in map zone and map point. Map zone can display the different colors based on the number of hotspot in each region and map point can display different sizes of the point to represent the number of hotspots in each region. This research is expected to facilitate users in the presentation of hotspot data as needed.

  2. Wild Fire Risk Map in the Eastern Steppe of Mongolia Using Spatial Multi-Criteria Analysis

    Science.gov (United States)

    Nasanbat, Elbegjargal; Lkhamjav, Ochirkhuyag

    2016-06-01

    Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.

  3. Ecological and spatial factors drive intra- and interspecific variation in exposure of subarctic predatory bird nestlings to persistent organic pollutants.

    Science.gov (United States)

    Eulaers, Igor; Jaspers, Veerle L B; Bustnes, Jan O; Covaci, Adrian; Johnsen, Trond V; Halley, Duncan J; Moum, Truls; Ims, Rolf A; Hanssen, Sveinn A; Erikstad, Kjell E; Herzke, Dorte; Sonne, Christian; Ballesteros, Manuel; Pinxten, Rianne; Eens, Marcel

    2013-07-01

    contamination). In conclusion, the combined analysis of POPs and stable isotopes in body feathers from fully-grown nestlings has identified ecological and spatial factors that may drive POP exposure over the larger part of the nestling stage. This methodological approach further promotes the promising use of nestling predatory bird body feathers as a non-destructive sampling strategy to integrate various toxicological and ecological proxies. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. Spatial-temporal analysis of wind power forecast errors for West-Coast Norway

    Energy Technology Data Exchange (ETDEWEB)

    Revheim, Paal Preede; Beyer, Hans Georg [Agder Univ. (UiA), Grimstad (Norway). Dept. of Engineering Sciences

    2012-07-01

    In this paper the spatial-temporal structure of forecast errors for wind power in West-Coast Norway is analyzed. Starting on the qualitative analysis of the forecast error reduction, with respect to single site data, for the lumped conditions of groups of sites the spatial and temporal correlations of the wind power forecast errors within and between the same groups are studied in detail. Based on this, time-series regression models to be used to analytically describe the error reduction are set up. The models give an expected reduction in forecast error between 48.4% and 49%. (orig.)

  5. Input-output analysis of CO2 emissions embodied in trade. The effects of spatial aggregation

    International Nuclear Information System (INIS)

    Su, Bin; Ang, B.W.

    2010-01-01

    Energy-related CO 2 emissions embodied in international trade have been widely studied by researchers using the environmental input-output analysis framework. It is well known that both sector aggregation and spatial aggregation affect the results obtained in such studies. With regard to the latter, past studies are often conducted at the national level irrespective of country or economy size. For a large economy with the needed data, studies may be conducted at different levels of spatial aggregation. We examine this problem analytically by extending the work of Su et al. ([Su, B., Huang, H.C., Ang, B.W., Zhou, P., 2010. Input-output analysis of CO 2 emissions embodied in trade: The effects of sector aggregation. Energy Economics 32 (1), 166-175.]) on sector aggregation. We present a numerical example using the data of China and by dividing the country into eight regions. It is found that the results are highly dependent on spatial aggregation. Our study shows that for a large country like China it is meaningful to look into the effect of spatial aggregation. (author)

  6. Spatial and spatio-temporal analysis of malaria in the state of Acre, western Amazon, Brazil

    Directory of Open Access Journals (Sweden)

    Leonardo Augusto Kohara Melchior

    2016-11-01

    Full Text Available Since 2005, the State of Acre, western Amazon, Brazil, has reported the highest annual parasite incidence (API of malaria among the Brazilian states. This study examines malaria incidence in Acre using spatial and spatio-temporal analysis based on an ecological time series study analyzing malaria cases and deaths for the time period 1992- 2014 and using secondary data. API indexes were calculated by age, sex, parasite species, ratio of Plasmodium vivax to P. falciparum malaria, malaria mortality rate and case fatality rate. SaTScan was used to detect spatial and spatio-temporal clusters of malaria cases and data were represented in the form of choropleth maps. A high-risk cluster of malaria was detected in Vale do Juruá and three low-risk clusters in Vale do Acre for both parasite species. Those younger than 19 years of age and females showed a high incidence of malaria in Vale do Juruá, but working-age males were the most affected in Vale do Acre. The malaria mortality rate showed a decreasing trend across the state, while the case fatality rate increased only in the micro-region of Rio Branco during the study period. We conclude that malaria is a focal disease in Acre showing different spatial and spatio-temporal patterns of cases and deaths that vary by age, sex, and parasite species. Malaria incidence is thought to be influenced by factors related to regional characteristics; therefore, appropriate disease and vector control strategies must be implemented at each locality.

  7. EFFECTS OF HETEROGENIETY ON SPATIAL PATTERN ANALYSIS OF WILD PISTACHIO TREES IN ZAGROS WOODLANDS, IRAN

    Directory of Open Access Journals (Sweden)

    Y. Erfanifard

    2014-10-01

    Full Text Available Vegetation heterogeneity biases second-order summary statistics, e.g., Ripley's K-function, applied for spatial pattern analysis in ecology. Second-order investigation based on Ripley's K-function and related statistics (i.e., L- and pair correlation function g is widely used in ecology to develop hypothesis on underlying processes by characterizing spatial patterns of vegetation. The aim of this study was to demonstrate effects of underlying heterogeneity of wild pistachio (Pistacia atlantica Desf. trees on the second-order summary statistics of point pattern analysis in a part of Zagros woodlands, Iran. The spatial distribution of 431 wild pistachio trees was accurately mapped in a 40 ha stand in the Wild Pistachio & Almond Research Site, Fars province, Iran. Three commonly used second-order summary statistics (i.e., K-, L-, and g-functions were applied to analyse their spatial pattern. The two-sample Kolmogorov-Smirnov goodness-of-fit test showed that the observed pattern significantly followed an inhomogeneous Poisson process null model in the study region. The results also showed that heterogeneous pattern of wild pistachio trees biased the homogeneous form of K-, L-, and g-functions, demonstrating a stronger aggregation of the trees at the scales of 0–50 m than actually existed and an aggregation at scales of 150–200 m, while regularly distributed. Consequently, we showed that heterogeneity of point patterns may bias the results of homogeneous second-order summary statistics and we also suggested applying inhomogeneous summary statistics with related null models for spatial pattern analysis of heterogeneous vegetations.

  8. Effects of Heterogeniety on Spatial Pattern Analysis of Wild Pistachio Trees in Zagros Woodlands, Iran

    Science.gov (United States)

    Erfanifard, Y.; Rezayan, F.

    2014-10-01

    Vegetation heterogeneity biases second-order summary statistics, e.g., Ripley's K-function, applied for spatial pattern analysis in ecology. Second-order investigation based on Ripley's K-function and related statistics (i.e., L- and pair correlation function g) is widely used in ecology to develop hypothesis on underlying processes by characterizing spatial patterns of vegetation. The aim of this study was to demonstrate effects of underlying heterogeneity of wild pistachio (Pistacia atlantica Desf.) trees on the second-order summary statistics of point pattern analysis in a part of Zagros woodlands, Iran. The spatial distribution of 431 wild pistachio trees was accurately mapped in a 40 ha stand in the Wild Pistachio & Almond Research Site, Fars province, Iran. Three commonly used second-order summary statistics (i.e., K-, L-, and g-functions) were applied to analyse their spatial pattern. The two-sample Kolmogorov-Smirnov goodness-of-fit test showed that the observed pattern significantly followed an inhomogeneous Poisson process null model in the study region. The results also showed that heterogeneous pattern of wild pistachio trees biased the homogeneous form of K-, L-, and g-functions, demonstrating a stronger aggregation of the trees at the scales of 0-50 m than actually existed and an aggregation at scales of 150-200 m, while regularly distributed. Consequently, we showed that heterogeneity of point patterns may bias the results of homogeneous second-order summary statistics and we also suggested applying inhomogeneous summary statistics with related null models for spatial pattern analysis of heterogeneous vegetations.

  9. Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data.

    Science.gov (United States)

    Medina, José M; Díaz, José A; Vukusic, Pete

    2015-04-20

    Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage.

  10. Environmental Risk Assessment: Spatial Analysis of Chemical Hazards and Risks in South Korea

    Science.gov (United States)

    Yu, H.; Heo, S.; Kim, M.; Lee, W. K.; Jong-Ryeul, S.

    2017-12-01

    This study identified chemical hazard and risk levels in Korea by analyzing the spatial distribution of chemical factories and accidents. The number of chemical factories and accidents in 5-km2 grids were used as the attribute value for spatial analysis. First, semi-variograms were conducted to examine spatial distribution patterns and to identify spatial autocorrelation of chemical factories and accidents. Semi-variograms explained that the spatial distribution of chemical factories and accidents were spatially autocorrelated. Second, the results of the semi-variograms were used in Ordinary Kriging to estimate chemical hazard and risk level. The level values were extracted from the Ordinary Kriging result and their spatial similarity was examined by juxtaposing the two values with respect to their location. Six peaks were identified in both the hazard and risk estimation result, and the peaks correlated with major cities in Korea. Third, the estimated hazard and risk levels were classified with geometrical interval and could be classified into four quadrants: Low Hazard and Low Risk (LHLR), Low Hazard and High Risk (LHHR), High Hazard and Low Risk (HHLR), and High Hazard and High Risk (HHHR). The 4 groups identified different chemical safety management issues in Korea; relatively safe LHLR group, many chemical reseller factories were found in HHLR group, chemical transportation accidents were in the LHHR group, and an abundance of factories and accidents were in the HHHR group. Each quadrant represented different safety management obstacles in Korea, and studying spatial differences can support the establishment of an efficient risk management plan.

  11. Spatial epidemiology of eastern equine encephalitis in Florida.

    Science.gov (United States)

    Vander Kelen, Patrick T; Downs, Joni A; Stark, Lillian M; Loraamm, Rebecca W; Anderson, James H; Unnasch, Thomas R

    2012-11-05

    Eastern Equine Encephalitis virus (EEEV) is an alphavirus with high pathogenicity in both humans and horses. Florida continues to have the highest occurrence of human cases in the USA, with four fatalities recorded in 2010. Unlike other states, Florida supports year-round EEEV transmission. This research uses GIS to examine spatial patterns of documented horse cases during 2005-2010 in order to understand the relationships between habitat and transmission intensity of EEEV in Florida. Cumulative incidence rates of EEE in horses were calculated for each county. Two cluster analyses were performed using density-based spatial clustering of applications with noise (DBSCAN). The first analysis was based on regional clustering while the second focused on local clustering. Ecological associations of EEEV were examined using compositional analysis and Euclidean distance analysis to determine if the proportion or proximity of certain habitats played a role in transmission. The DBSCAN algorithm identified five distinct regional spatial clusters that contained 360 of the 438 horse cases. The local clustering resulted in 18 separate clusters containing 105 of the 438 cases. Both the compositional analysis and Euclidean distance analysis indicated that the top five habitats positively associated with horse cases were rural residential areas, crop and pastureland, upland hardwood forests, vegetated non-forested wetlands, and tree plantations. This study demonstrates that in Florida tree plantations are a focus for epizootic transmission of EEEV. It appears both the abundance and proximity of tree plantations are factors associated with increased risk of EEE in horses and therefore humans. This association helps to explain why there is are spatially distinct differences in the amount of EEE horse cases across Florida.

  12. Helmintic infections in water buffaloes on Italian farms: a spatial analysis

    Directory of Open Access Journals (Sweden)

    Laura Rinaldi

    2009-05-01

    Full Text Available The present paper reports the results of a cross-sectional survey aimed at obtaining up-to-date information on the spatial distribution of different groups and/or species of helminths in water buffaloes from central Italy. Geographical information systems (GIS and spatial analysis were used to plan the sampling procedures, to display the results as maps and to detect spatial clusters of helminths in the study area. The survey was conducted on 127 water buffalo farms, which were selected in the study area using a grid sampling approach, followed by proportional allocation. Faecal samples (n. = 1,883 collected from the 127 farms were examined using the Flotac dual technique. Gastrointestinal strongyles were the most frequent helminths (33.1% on the tested farms, followed by the liver fluke Fasciola hepatica (7.1%, the rumen fluke Calicophoron daubneyi (7.1%, the nematode Strongyloides spp. (3.1%, the lancet liver fluke Dicrocoelium dendriticum (2.4% and the tapeworm Moniezia spp. (2.4%. In order to display the spatial distribution of the various helminths detected on the water buffalo farms (used as epidemiological unit in our study, point maps were drawn within the GIS. In addition, for each helminth, clustering of test-positive farms were investigated based on location determined by exact coordinates. Using spatial scan statistic, spatial clusters were found for the flukes F. hepatica and C. daubneyi and the cestode Moniezia spp.; these findings are consistent with the life cycle of these parasites, which have strong environmental determinants. In conclusion, the present study demonstrated that, with the appropriate survey-based data at hand, GIS is a useful tool to study epidemiological patterns of infections in veterinary health, in particular in water buffaloes.

  13. A spatial and temporal analysis of Japanese encephalitis in mainland China, 1963-1975: a period without Japanese encephalitis vaccination.

    Science.gov (United States)

    Li, Xiaolong; Gao, Xiaoyan; Ren, Zhoupeng; Cao, Yuxi; Wang, Jinfeng; Liang, Guodong

    2014-01-01

    More than a million Japanese encephalitis (JE) cases occurred in mainland China from the 1960s to 1970s without vaccine interventions. The aim of this study is to analyze the spatial and temporal pattern of JE cases reported in mainland China from 1965 to 1973 in the absence of JE vaccination, and to discuss the impacts of climatic and geographical factors on JE during that period. Thus, the data of reported JE cases at provincial level and monthly precipitation and monthly mean temperature from 1963 to 1975 in mainland China were collected. Local Indicators of Spatial Association analysis was performed to identify spatial clusters at the province level. During that period, The epidemic peaked in 1966 and 1971 and the JE incidence reached up to 20.58/100000 and 20.92/100000, respectively. The endemic regions can be divided into three classes including high, medium, and low prevalence regions. Through spatial cluster analysis, JE epidemic hot spots were identified; most were located in the Yangtze River Plain which lies in the southeast of China. In addition, JE incidence was shown to vary among eight geomorphic units in China. Also, the JE incidence in the Loess Plateau and the North China Plain was showed to increase with the rise of temperature. Likewise, JE incidence in the Loess Plateau and the Yangtze River Plain was observed a same trend with the increase of rainfall. In conclusion, the JE cases clustered geographically during the epidemic period. Besides, the JE incidence was markedly higher on the plains than plateaus. These results may provide an insight into the epidemiological characteristics of JE in the absence of vaccine interventions and assist health authorities, both in China and potentially in Europe and Americas, in JE prevention and control strategies.

  14. Characterizing China's energy consumption with selective economic factors and energy-resource endowment: a spatial econometric approach

    Science.gov (United States)

    Jiang, Lei; Ji, Minhe; Bai, Ling

    2015-06-01

    Coupled with intricate regional interactions, the provincial disparity of energy-resource endowment and other economic conditions in China have created spatially complex energy consumption patterns that require analyses beyond the traditional ones. To distill the spatial effect out of the resource and economic factors on China's energy consumption, this study recast the traditional econometric model in a spatial context. Several analytic steps were taken to reveal different aspects of the issue. Per capita energy consumption (AVEC) at the provincial level was first mapped to reveal spatial clusters of high energy consumption being located in either well developed or energy resourceful regions. This visual spatial autocorrelation pattern of AVEC was quantitatively tested to confirm its existence among Chinese provinces. A Moran scatterplot was employed to further display a relatively centralized trend occurring in those provinces that had parallel AVEC, revealing a spatial structure with attraction among high-high or low-low regions and repellency among high-low or low-high regions. By a comparison between the ordinary least square (OLS) model and its spatial econometric counterparts, a spatial error model (SEM) was selected to analyze the impact of major economic determinants on AVEC. While the analytic results revealed a significant positive correlation between AVEC and economic development, other determinants showed some intricate influential patterns. The provinces endowed with rich energy reserves were inclined to consume much more energy than those otherwise, whereas changing the economic structure by increasing the proportion of secondary and tertiary industries also tended to consume more energy. Both situations seem to underpin the fact that these provinces were largely trapped in the economies that were supported by technologies of low energy efficiency during the period, while other parts of the country were rapidly modernized by adopting advanced

  15. Temporal and spatial distribution characteristics in the natural plague foci of Chinese Mongolian gerbils based on spatial autocorrelation.

    Science.gov (United States)

    Du, Hai-Wen; Wang, Yong; Zhuang, Da-Fang; Jiang, Xiao-San

    2017-08-07

    The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague, which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus, but also to reveal its cluster rule. This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014, in order to predict plague outbreaks. Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils. Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods. The quantity of M. unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention. The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index. High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high. In terms of time series, the area of the epidemic focus gradually increased from 2005 to 2007, declined rapidly in 2008 and 2009, and then decreased slowly and began trending towards stability from 2009 to 2014. For the spatial change, the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007, and then moved from north to south in 2007 and 2008. The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation. The diversity of temporary and spatial distribution is mainly affected by seasonal variation, the human

  16. Spatial econometrics using microdata

    CERN Document Server

    Dubé, Jean

    2014-01-01

    This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data.Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency.The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares appr

  17. ANALYSIS OF SPATIAL CHANGES IN GROUNDWATER RETENTION FOR THE ODER VALLEY IN THE MALCZYCE REGION

    Directory of Open Access Journals (Sweden)

    Edyta Nowicka

    2015-10-01

    Full Text Available The paper presents the analysis of spatial changes of groundwater retention for a part of the Oder valley situated below the barrage in Brzeg Dolny. For the analysis of selected monthly average elevations of the groundwater table of the selected measuring points (32 piezometers located in the area described, and 7 gauges on the Oder river, Średzka Woda, Jeziorka and Nowy Rów. The change of groundwater retention is presented in spatial terms for vegetation periods of years: 2010, 2011 and 2012. The database was made interpolating the groundwater table elevation for the area in question. On this basis, differences between ordinates the groundwater table were calculated. The next step was to obtain the spatial distribution of groundwater retention states and its analysis. The results show significant changes in the states of groundwater retention on the selected portion of the valley in the individual growing seasons. According to formation of changes in status of groundwater retention relative to the distance from the Odra river was analysed.

  18. Trajectory analysis of land use and land cover maps to improve spatial-temporal patterns, and impact assessment on groundwater recharge

    Science.gov (United States)

    Zomlot, Z.; Verbeiren, B.; Huysmans, M.; Batelaan, O.

    2017-11-01

    Land use/land cover (LULC) change is a consequence of human-induced global environmental change. It is also considered one of the major factors affecting groundwater recharge. Uncertainties and inconsistencies in LULC maps are one of the difficulties that LULC timeseries analysis face and which have a significant effect on hydrological impact analysis. Therefore, an accuracy assessment approach of LULC timeseries is needed for a more reliable hydrological analysis and prediction. The objective of this paper is to assess the impact of land use uncertainty and to improve the accuracy of a timeseries of CORINE (coordination of information on the environment) land cover maps by using a new approach of identifying spatial-temporal LULC change trajectories as a pre-processing tool. This ensures consistency of model input when dealing with land-use dynamics and as such improves the accuracy of land use maps and consequently groundwater recharge estimation. As a case study the impact of consistent land use changes from 1990 until 2013 on groundwater recharge for the Flanders-Brussels region is assessed. The change trajectory analysis successfully assigned a rational trajectory to 99% of all pixels. The methodology is shown to be powerful in correcting interpretation inconsistencies and overestimation errors in CORINE land cover maps. The overall kappa (cell-by-cell map comparison) improved from 0.6 to 0.8 and from 0.2 to 0.7 for forest and pasture land use classes respectively. The study shows that the inconsistencies in the land use maps introduce uncertainty in groundwater recharge estimation in a range of 10-30%. The analysis showed that during the period of 1990-2013 the LULC changes were mainly driven by urban expansion. The results show that the resolution at which the spatial analysis is performed is important; the recharge differences using original and corrected CORINE land cover maps increase considerably with increasing spatial resolution. This study indicates

  19. Groundwater Quality: Analysis of Its Temporal and Spatial Variability in a Karst Aquifer.

    Science.gov (United States)

    Pacheco Castro, Roger; Pacheco Ávila, Julia; Ye, Ming; Cabrera Sansores, Armando

    2018-01-01

    This study develops an approach based on hierarchical cluster analysis for investigating the spatial and temporal variation of water quality governing processes. The water quality data used in this study were collected in the karst aquifer of Yucatan, Mexico, the only source of drinking water for a population of nearly two million people. Hierarchical cluster analysis was applied to the quality data of all the sampling periods lumped together. This was motivated by the observation that, if water quality does not vary significantly in time, two samples from the same sampling site will belong to the same cluster. The resulting distribution maps of clusters and box-plots of the major chemical components reveal the spatial and temporal variability of groundwater quality. Principal component analysis was used to verify the results of cluster analysis and to derive the variables that explained most of the variation of the groundwater quality data. Results of this work increase the knowledge about how precipitation and human contamination impact groundwater quality in Yucatan. Spatial variability of groundwater quality in the study area is caused by: a) seawater intrusion and groundwater rich in sulfates at the west and in the coast, b) water rock interactions and the average annual precipitation at the middle and east zones respectively, and c) human contamination present in two localized zones. Changes in the amount and distribution of precipitation cause temporal variation by diluting groundwater in the aquifer. This approach allows to analyze the variation of groundwater quality controlling processes efficiently and simultaneously. © 2017, National Ground Water Association.

  20. The Composition and Spatial Patterns of Bacterial Virulence Factors and Antibiotic Resistance Genes in 19 Wastewater Treatment Plants.

    Directory of Open Access Journals (Sweden)

    Bing Zhang

    Full Text Available Bacterial pathogenicity and antibiotic resistance are of concern for environmental safety and public health. Accumulating evidence suggests that wastewater treatment plants (WWTPs are as an important sink and source of pathogens and antibiotic resistance genes (ARGs. Virulence genes (encoding virulence factors are good indicators for bacterial pathogenic potentials. To achieve a comprehensive understanding of bacterial pathogenic potentials and antibiotic resistance in WWTPs, bacterial virulence genes and ARGs in 19 WWTPs covering a majority of latitudinal zones of China were surveyed by using GeoChip 4.2. A total of 1610 genes covering 13 virulence factors and 1903 genes belonging to 11 ARG families were detected respectively. The bacterial virulence genes exhibited significant spatial distribution patterns of a latitudinal biodiversity gradient and a distance-decay relationship across China. Moreover, virulence genes tended to coexist with ARGs as shown by their strongly positive associations. In addition, key environmental factors shaping the overall virulence gene structure were identified. This study profiles the occurrence, composition and distribution of virulence genes and ARGs in current WWTPs in China, and uncovers spatial patterns and important environmental variables shaping their structure, which may provide the basis for further studies of bacterial virulence factors and antibiotic resistance in WWTPs.

  1. Spatial aspects of surface water quality in the Jakara Basin, Nigeria using chemometric analysis.

    Science.gov (United States)

    Mustapha, Adamu; Aris, Ahmad Zaharin

    2012-01-01

    Multivariate statistical techniques such as hierarchical Agglomerated cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) were applied to identify the spatial variation and pollution sources of Jakara River, Kano, Nigeria. Thirty surface water samples were collected: 23 along Getsi River and 7 along the main channel of River Jakara. Twenty-three water quality parameters, namely pH, temperature, turbidity, electrical conductivity (EC), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), Faecal coliform, total solids (TS), nitrates (NO(3)(-)), phosphates (PO(4)(3-)), cobalt (Co), iron (Fe), nickel (Ni), manganese (Mn), copper (Cu), sodium (Na), potassium (K), mercury (Hg), chromium (Cr), cadmium (Cd), lead (Pb), magnesium (Mg), and calcium(Ca) were analysed. HACA grouped the sampling points into three clusters based on the similarities of river water quality characteristics: industrial, domestic, and agricultural water pollution sources. Forward and backward DA effectively discriminated 5 and 15 water quality variables, respectively, each assigned with 100% correctness from the original 23 variables. PCA and FA were used to investigate the origin of each water quality parameter due to various land use activities, 7 principal components were obtained with 77.5% total variance, and in addition PCA identified 3 latent pollution sources to support HACA. From this study, one can conclude that the application of multivariate techniques derives meaningful information from water quality data.

  2. Quantifying the impact of environmental factors on arthropod communities in agricultural landscapes across organizational levels and spatial scales

    NARCIS (Netherlands)

    Schweiger, O.; Maelfait, J.P.; Wingerden, van W.K.R.E.; Hendrickx, F.; Billeter, R.; Speelmans, M.; Augenstein, I.; Aukema, B.; Aviron, S.; Bailey, D.; Bukacek, R.; Burel, F.; Diekötter, T.; Dirksen, J.; Frenzel, M.; Herzog, F.; Liira, J.; Roubalova, M.; Bugter, R.J.F.

    2005-01-01

    1. In landscapes influenced by anthropogenic activities, such as intensive agriculture, knowledge of the relative importance and interaction of environmental factors on the composition and function of local communities across a range of spatial scales is important for maintaining biodiversity. 2. We

  3. [Spatial-temporal pattern and obstacle factors of cultivated land ecological security in major grain producing areas of northeast China: a case study in Jilin Province].

    Science.gov (United States)

    Zhao, Hong-Bo; Ma, Yan-Ji

    2014-02-01

    According to the cultivated land ecological security in major grain production areas of Northeast China, this paper selected 48 counties of Jilin Province as the research object. Based on the PSR-EES conceptual framework model, an evaluation index system of cultivated land ecological security was built. By using the improved TOPSIS, Markov chains, GIS spatial analysis and obstacle degree models, the spatial-temporal pattern of cultivated land ecological security and the obstacle factors were analyzed from 1995 to 2011 in Jilin Province. The results indicated that, the composite index of cultivated land ecological security appeared in a rising trend in Jilin Province from 1995 to 2011, and the cultivated land ecological security level changed from being sensitive to being general. There was a pattern of 'Club Convergence' in cultivated land ecological security level in each county and the spatial discrepancy tended to become larger. The 'Polarization' trend of cultivated land ecological security level was obvious. The distributions of sensitive level and critical security level with ribbon patterns tended to be dispersed, the general security level and relative security levels concentrated, and the distributions of security level scattered. The unstable trend of cultivated land ecological security level was more and more obvious. The main obstacle factors that affected the cultivated land ecological security level in Jilin Province were rural net income per capita, economic density, the proportion of environmental protection investment in GDP, degree of machinery cultivation and the comprehensive utilization rate of industrial solid wastes.

  4. The spatial distribution and temporal variation of desert riparian forests and their influencing factors in the downstream Heihe River basin, China

    Science.gov (United States)

    Ding, Jingyi; Zhao, Wenwu; Daryanto, Stefani; Wang, Lixin; Fan, Hao; Feng, Qiang; Wang, Yaping

    2017-05-01

    Desert riparian forests are the main restored vegetation community in Heihe River basin. They provide critical habitats and a variety of ecosystem services in this arid environment. Since desert riparian forests are also sensitive to disturbance, examining the spatial distribution and temporal variation of these forests and their influencing factors is important to determine the limiting factors of vegetation recovery after long-term restoration. In this study, field experiment and remote sensing data were used to determine the spatial distribution and temporal variation of desert riparian forests and their relationship with the environmental factors. We classified five types of vegetation communities at different distances from the river channel. Community coverage and diversity formed a bimodal pattern, peaking at the distances of 1000 and 3000 m from the river channel. In general, the temporal normalized difference vegetation index (NDVI) trend from 2000 to 2014 was positive at different distances from the river channel, except for the region closest to the river bank (i.e. within 500 m from the river channel), which had been undergoing degradation since 2011. The spatial distribution of desert riparian forests was mainly influenced by the spatial heterogeneity of soil properties (e.g. soil moisture, bulk density and soil particle composition). Meanwhile, while the temporal variation of vegetation was affected by both the spatial heterogeneity of soil properties (e.g. soil moisture and soil particle composition) and to a lesser extent, the temporal variation of water availability (e.g. annual average and variability of groundwater, soil moisture and runoff). Since surface (0-30 cm) and deep (100-200 cm) soil moisture, bulk density and the annual average of soil moisture at 100 cm obtained from the remote sensing data were regarded as major determining factors of community distribution and temporal variation, conservation measures that protect the soil structure

  5. Calculation of spatial distribution of optical escape factor and its application to He I collisional-radiative model

    International Nuclear Information System (INIS)

    Iida, Yohei; Kado, Shinichiro; Tanaka, Satoru

    2010-01-01

    An integral analytical formula for a spatial distribution of the optical escape factor (OEF) in an infinite cylindrical plasma is derived as a function of an arbitrary upper state spatial density profile, the temperature ratio of the upper state to the lower state, and the optical depth of the corresponding transition. Test calculations are carried out for three different upper state profiles, i.e., uniform (rectangular), parabolic, and Gaussian upper state profiles. The OEF takes on negative values at the periphery of the parabolic and Gaussian upper state profiles. These characteristics cannot be expressed by the conventional OEF formulas derived for the center of the plasma, even though the optical depth is increased. In addition to the analytical derivation of the formula, two practical formulas are proposed: an empirical formula of the spatial distribution of the OEF for the Gaussian upper state density profile and a linear formula of the OEF distribution for upper state profiles that are expressed as linear combinations. These formulas enable us to calculate the spatial distribution of the OEF for the multiple-Gaussian upper state profile without the need for time-consuming integral calculations.

  6. Integrating cross-scale analysis in the spatial and temporal domains for classification of behavioral movement

    Directory of Open Access Journals (Sweden)

    Ali Soleymani

    2014-06-01

    Full Text Available Since various behavioral movement patterns are likely to be valid within different, unique ranges of spatial and temporal scales (e.g., instantaneous, diurnal, or seasonal with the corresponding spatial extents, a cross-scale approach is needed for accurate classification of behaviors expressed in movement. Here, we introduce a methodology for the characterization and classification of behavioral movement data that relies on computing and analyzing movement features jointly in both the spatial and temporal domains. The proposed methodology consists of three stages. In the first stage, focusing on the spatial domain, the underlying movement space is partitioned into several zonings that correspond to different spatial scales, and features related to movement are computed for each partitioning level. In the second stage, concentrating on the temporal domain, several movement parameters are computed from trajectories across a series of temporal windows of increasing sizes, yielding another set of input features for the classification. For both the spatial and the temporal domains, the ``reliable scale'' is determined by an automated procedure. This is the scale at which the best classification accuracy is achieved, using only spatial or temporal input features, respectively. The third stage takes the measures from the spatial and temporal domains of movement, computed at the corresponding reliable scales, as input features for behavioral classification. With a feature selection procedure, the most relevant features contributing to known behavioral states are extracted and used to learn a classification model. The potential of the proposed approach is demonstrated on a dataset of adult zebrafish (Danio rerio swimming movements in testing tanks, following exposure to different drug treatments. Our results show that behavioral classification accuracy greatly increases when firstly cross-scale analysis is used to determine the best analysis scale, and

  7. Spatial and Temporal Assessment on Drug Addiction Using Multivariate Analysis and GIS

    International Nuclear Information System (INIS)

    Mohd Ekhwan Toriman; Mohd Ekhwan Toriman; Siti Nor Fazillah Abdullah; Izwan Arif Azizan; Mohd Khairul Amri Kamarudin; Roslan Umar; Nasir Mohamad

    2015-01-01

    There is a need for managing and displaying drug addiction phenomena and trend at both spatial and temporal scales. Spatial and temporal assessment on drug addiction in Terengganu was undertaken to understand the geographical area of district in the same cluster, in addition, identify the hot spot area of this problem and analysis the trend of drug addiction. Data used were topography map of Terengganu and number of drug addicted person in Terengganu by district within 10 years (2004-2013). Number of drug addicted person by district were mapped using Geographic Information system and analysed using a combination of multivariate analysis which is cluster analysis were applied to the database in order to validate the correlation between data in the same cluster. Result showed a cluster analysis for number of drug addiction by district generated three clusters which are Besut and Kuala Terengganu in cluster 1 named moderate drug addicted person (MDA), Dungun, Marang, Setiu and Hulu Terengganu in cluster 2 named lower drug addicted person (LDA) and Kemaman in cluster 3 named high drug addicted person(HDA). This analysis indicates that cluster 3 which is Kemaman is a hot spot area. These results were beneficial for stakeholder to monitor and manage this problem especially in the hot spot area which needs to be emphasized. (author)

  8. Components of spatial information management in wildlife ecology: Software for statistical and modeling analysis [Chapter 14

    Science.gov (United States)

    Hawthorne L. Beyer; Jeff Jenness; Samuel A. Cushman

    2010-01-01

    Spatial information systems (SIS) is a term that describes a wide diversity of concepts, techniques, and technologies related to the capture, management, display and analysis of spatial information. It encompasses technologies such as geographic information systems (GIS), global positioning systems (GPS), remote sensing, and relational database management systems (...

  9. Spatial-temporal detection of risk factors for bacillary dysentery in Beijing, Tianjin and Hebei, China

    Directory of Open Access Journals (Sweden)

    Chengdong Xu

    2017-09-01

    Full Text Available Abstract Background Bacillary dysentery is the third leading notifiable disease and remains a major public health concern in China. The Beijing–Tianjin–Hebei urban region is the biggest urban agglomeration in northern China, and it is one of the areas in the country that is most heavily infected with bacillary dysentery. The objective of the study was to analyze the spatial-temporal pattern and to determine any contributory risk factors on the bacillary dysentery. Methods Bacillary dysentery case data from 1 January 2012 to 31 December 2012 in Beijing–Tianjin– Hebei were employed. GeoDetector method was used to determine the impact of potential risk factors, and to identify regions and seasons at high risk of the disease. Results There were 36,472 cases of bacillary dysentery in 2012 in the study region. The incidence of bacillary dysentery varies widely amongst different age groups; the higher incidence of bacillary dysentery mainly occurs in the population under the age of five. Bacillary dysentery presents apparent seasonal variance, with the highest incidence occurring from June to September. In terms of the potential meteorological risk factors, mean temperature, relative humidity, precipitation, mean wind speed and sunshine hours explain the time variant of bacillary dysentery at 83%, 31%, 25%, 17% and 13%, respectively. The interactive effect between temperature and humidity has an explanatory power of 87%, indicating that a hot and humid environment is more likely to lead to the occurrence of bacillary dysentery. Socio-economic factors affect the spatial distribution of bacillary dysentery. The top four factors are age group, per capita GDP, population density and rural population proportion, and their determinant powers are 61%, 27%, 25% and 21%, respectively. The interactive effect between age group and the other factors accounts for more than 60% of bacillary dysentery transmission. Conclusions Bacillary dysentery poses a

  10. Spatial and temporal analysis of natural resources degradation in the Likodra River watershed

    Directory of Open Access Journals (Sweden)

    Polovina Siniša

    2016-01-01

    Full Text Available Soil is an important natural resource whose proper use requires a good knowledge of all endogenous and exogenous factors that cause different types of degradation. Erosion is one of the forms of soil degradation. Erosion processes are characterized by a distinctive complexity and the factors affecting them are dynamics and change in space and time. A complex system degradation requires a multidisciplinary approach to the use of modern methods and techniques. Today, a large number of models are available for the assessment of soil loss through erosion as well as the levels of risk from erosion, today. Most of these are based on the logics of GIS thanks to its ability to sublimate heterogeneous information. In this paper, the analysis of spatial and temporal degradation of natural resources is carried out in the Likodra River watershed. The Likodra River is located in the northwestern part of the Republic of Serbia, and is positioned in the municipality of Krupanj. The main stream in the immediate vicinity of the town of Krupanj formed from four small streams that have expressed torrential character (the Bogoštica with the Kržava and the Čađavica with the Brštica. In May 2014, the urban area and rural parts of the municipality Krupanj were affected by catastrophic flash floods that resulted in the loss of human lives and enormous material damage. Soil degradation in the study area was analyzed using the Erosion Potential Method (EPM. The method is characterized by a high degree of reliability for determining the intensity of erosion, calculation of sediment yield and transport. The advantage of this method compared to other methods its lower complexity in terms of quantity of input parameters, simplicity and the possibility of application in GIS. In addition, the method has the advantage of choice, because it was developed in this area. The method is based on the analytical processing of data on factors affecting erosion. As the erosion

  11. Spatial and temporal analysis of postural control in dyslexic children.

    Science.gov (United States)

    Gouleme, Nathalie; Gerard, Christophe Loic; Bui-Quoc, Emmanuel; Bucci, Maria Pia

    2015-07-01

    The aim of this study is to examine postural control of dyslexic children using both spatial and temporal analysis. Thirty dyslexic (mean age 9.7±0.3years) and thirty non-dyslexic age-matched children participated in the study. Postural stability was evaluated using Multitest Equilibre from Framiral®. Posture was recorded in the following conditions: eyes open fixating a target (EO) and eyes closed (EC) on stable (-S-) and unstable (-U-) platforms. The findings of this study showed poor postural stability in dyslexic children with respect to the non-dyslexic children group, as demonstrated by both spatial and temporal analysis. In both groups of children postural control depends on the condition, and improves when the eyes are open on a stable platform. Dyslexic children have spectral power indices that are higher than in non-dyslexic children and they showed a shorter cancelling time. Poor postural control in dyslexic children could be due to a deficit in using sensory information most likely caused by impairment in cerebellar activity. The reliability of brain activation patterns, namely in using sensory input and cerebellar activity may explain the deficit in postural control in dyslexic children. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  12. Effects on ground motion related to spatial variability

    International Nuclear Information System (INIS)

    Vanmarcke, E.H.

    1987-01-01

    Models of the spectral content and the space-time correlation structure of strong earthquake ground motion are combined with transient random vibration analysis to yield site-specific response spectra that can account for the effect of local spatial averaging of the ground motion across a rigid foundation of prescribed size. The methodology is presented with reference to sites in eastern North America, although the basic approach is applicable to other seismic regions provided the source and attenuation parameters are regionally adjusted. Parameters in the spatial correlation model are based on data from the SMART-I accelerograph array, and the sensitivity of response spectra reduction factors with respect to these parameters is examined. The starting point of the analysis is the Fourier amplitude spectrum of site displacement expresses as a function of earthquake source parameters and source-to-site distance. The bedrock acceleration spectral density function at a point, derived from the displacement spectrum, is modified to account for anelastic attenuation, and where appropriate, for local soil effects and/or local spatial averaging across a foundation. Transient random vibration analysis yields approximate analytical expressions for median ground motion amplitudes and median response spectra of an earthquake defined in terms of its spectral density function and strong motion duration. The methodology is illustrated for three events characterized by their m b magnitude and epicentral distance. The focus in this paper is on the stochastic response prediction methodology enabling explicit accounting for strong motion duration and the effect of local spatial averaging on response spectra. The numerical examples enable a preliminary assessment of the reduction of response spectral amplitudes attributable to local spatial averaging across rigid foundations of different sizes. 36 refs

  13. Exploratory Bi-Factor Analysis: The Oblique Case

    Science.gov (United States)

    Jennrich, Robert I.; Bentler, Peter M.

    2012-01-01

    Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…

  14. Spatial Durbin model analysis macroeconomic loss due to natural disasters

    Science.gov (United States)

    Kusrini, D. E.; Mukhtasor

    2015-03-01

    Magnitude of the damage and losses caused by natural disasters is huge for Indonesia, therefore this study aimed to analyze the effects of natural disasters for macroeconomic losses that occurred in 115 cities/districts across Java during 2012. Based on the results of previous studies it is suspected that it contains effects of spatial dependencies in this case, so that the completion of this case is performed using a regression approach to the area, namely Analysis of Spatial Durbin Model (SDM). The obtained significant predictor variable is population, and predictor variable with a significant weighting is the number of occurrences of disasters, i.e., disasters in the region which have an impact on other neighboring regions. Moran's I index value using the weighted Queen Contiguity also showed significant results, meaning that the incidence of disasters in the region will decrease the value of GDP in other.

  15. Spectral theory and nonlinear analysis with applications to spatial ecology

    CERN Document Server

    Cano-Casanova, S; Mora-Corral , C

    2005-01-01

    This volume details some of the latest advances in spectral theory and nonlinear analysis through various cutting-edge theories on algebraic multiplicities, global bifurcation theory, non-linear Schrödinger equations, non-linear boundary value problems, large solutions, metasolutions, dynamical systems, and applications to spatial ecology. The main scope of the book is bringing together a series of topics that have evolved separately during the last decades around the common denominator of spectral theory and nonlinear analysis - from the most abstract developments up to the most concrete applications to population dynamics and socio-biology - in an effort to fill the existing gaps between these fields.

  16. Concept of spatial channel theory applied to reactor shielding analysis

    International Nuclear Information System (INIS)

    Williams, M.L.; Engle, W.W. Jr.

    1977-01-01

    The concept of channel theory is used to locate spatial regions that are important in contributing to a shielding response. The method is analogous to the channel-theory method developed for ascertaining important energy channels in cross-section analysis. The mathematical basis for the theory is shown to be the generalized reciprocity relation, and sample problems are given to exhibit and verify properties predicted by the mathematical equations. A practical example is cited from the shielding analysis of the Fast Flux Test Facility performed at Oak Ridge National Laboratory, in which a perspective plot of channel-theory results was found useful in locating streaming paths around the reactor cavity shield

  17. Spatial Distribution of Bacterial Communities Driven by Multiple Environmental Factors in a Beach Wetland of the Largest Freshwater Lake in China

    Directory of Open Access Journals (Sweden)

    Xia eDing

    2015-02-01

    Full Text Available The spatial distributions of bacterial communities may be driven by multiple environmental factors. Thus, understanding the relationships between bacterial distribution and environmental factors is critical for understanding wetland stability and the functioning of freshwater lakes. However, little research on the bacterial communities in deep sediment layers exists. In this study, thirty clone libraries of 16S rRNA were constructed from a beach wetland of the Poyang Lake along both horizontal (distance to the water-land junction and vertical (sediment depth gradients to assess the effects of sediment properties on bacterial community structure and diversity. Our results showed that bacterial diversity increased along the horizontal gradient and decreased along the vertical gradient. The heterogeneous sediment properties along gradients substantially affected the dominant bacterial groups at the phylum and species levels. For example, the NH4+ concentration decreased with increasing depth, which was positively correlated with the relative abundance of Alphaproteobacteria. The changes in bacterial diversity and dominant bacterial groups showed that the top layer had a different bacterial community structure than the deeper layers. Principal component analysis revealed that both gradients, not each gradient independently, contributed to the shift in the bacterial community structure. A multiple linear regression model explained the changes in bacterial diversity and richness along the depth and distance gradients. Overall, our results suggest that spatial gradients associated with sediment properties shaped the bacterial communities in the Poyang Lake beach wetland.

  18. WILD FIRE RISK MAP IN THE EASTERN STEPPE OF MONGOLIA USING SPATIAL MULTI-CRITERIA ANALYSIS

    Directory of Open Access Journals (Sweden)

    E. Nasanbat

    2016-06-01

    Full Text Available Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.

  19. REMOTE SENSING-BASED DETECTION AND SPATIAL PATTERN ANALYSIS FOR GEO-ECOLOGICAL NICHE MODELING OF TILLANDSIA SPP. IN THE ATACAMA, CHILE

    Directory of Open Access Journals (Sweden)

    N. Wolf

    2016-06-01

    Full Text Available In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called ‘fog oases’ dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

  20. Remote Sensing-Based Detection and Spatial Pattern Analysis for Geo-Ecological Niche Modeling of Tillandsia SPP. In the Atacama, Chile

    Science.gov (United States)

    Wolf, N.; Siegmund, A.; del Río, C.; Osses, P.; García, J. L.

    2016-06-01

    In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called `fog oases' dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

  1. Temporal and spatial variation in recent vehicular emission inventories in China based on dynamic emission factors.

    Science.gov (United States)

    Cai, Hao; Xie, Shaodong

    2013-03-01

    The vehicular emission trend in China was tracked for the recent period 2006-2009 based on a database of dynamic emission factors of CO, nonmethane volatile organic compounds (NMVOC), NOx, PM10, CO2, CH4, and N2O for all categories of on-road motor vehicles in China, which was developed at the provincial level using the COPERT 4 model, to account for the effects of rapid advances in engine technologies, implementation of improved emission standards, emission deterioration due to mileage, and fuel quality improvement. Results show that growth rates of CO and NMVOC emissions slowed down, but NOx and PM10 emissions continued rising rapidly for the period 2006-2009. Moreover CO2, CH4, and N2O emissions in 2009 almost doubled compared to those in 2005. Characteristics of recent spatial distribution of emissions and emission contributions by vehicle category revealed that priority of vehicular emission control should be put on the eastern and southeastern coastal provinces and northern regions, and passenger cars and motorcycles require stricter control for the reduction of CO and NMVOC emissions, while effective reduction of NOx and PM10 emissions can be achieved by better control of heavy-duty vehicles, buses and coaches, and passenger cars. Explicit provincial-level Monte Carlo uncertainty analysis, which quantified for the first time the Chinese vehicular emission uncertainties associated with both COPERT-derived and domestically measured emission factors by vehicle technology, showed that CO, NMVOC, and NOx emissions for the period 2006-2009 were calculated with the least uncertainty, followed by PM10 and CO2, despite relatively larger uncertainties in N2O and CH4 emissions. The quantified low uncertainties of emissions revealed a necessity of applying vehicle technology- and vehicle age-specific dynamic emission factors for vehicular emission estimation, and these improved methodologies are applicable for routine update and forecast of China's on-road motor vehicle

  2. A spatial epidemiological analysis of nontuberculous mycobacterial infections in Queensland, Australia.

    Science.gov (United States)

    Chou, Michael P; Clements, Archie C A; Thomson, Rachel M

    2014-05-21

    The epidemiology of infections with nontuberculous mycobacteria (NTM) has been changing and the incidence has been increasing in some settings. The main route of transmission to humans is considered to be from the environment. We aimed to describe spatial clusters of cases of NTM infections and to identify associated climatic, environmental and socio-economic variables. NTM data were obtained from the Queensland Mycobacterial Reference Laboratory for the period 2001-2011. A Bayesian spatial conditional autoregressive model was constructed at the postcode level, with covariates including soil variables, maximum, mean and minimum rainfall and temperature, income (proportion of population earning Queensland region overlying the Surat sub-division of the Great Artesian Basin, as well as in the lower North Queensland Local Government Area known as the Whitsunday region. Our models estimated an expected increase of 21% per percentage increase of population earning Queensland, and a number of socio-ecological, economic and environmental factors were found to be associated with NTM infection risk.

  3. The Simultaneous Effects of Spatial and Social Networks on Cholera Transmission

    Directory of Open Access Journals (Sweden)

    Sophia Giebultowicz

    2011-01-01

    Full Text Available This study uses social network and spatial analytical methods simultaneously to understand cholera transmission in rural Bangladesh. Both have been used separately to incorporate context into health studies, but using them together is a new and recent approach. Data include a spatially referenced longitudinal demographic database consisting of approximately 200,000 people and a database of all laboratory-confirmed cholera cases from 1983 to 2003. A complete kinship-based network linking households is created, and distance matrices are also constructed to model spatial relationships. A spatial error-social effects model tested for cholera clustering in socially linked households while accounting for spatial factors. Results show that there was social clustering in five out of twenty-one years while accounting for both known and unknown environmental variables. This suggests that environmental cholera transmission is significant and social networks also influence transmission, but not as consistently. Simultaneous spatial and social network analysis may improve understanding of disease transmission.

  4. Spatial Operations

    Directory of Open Access Journals (Sweden)

    Anda VELICANU

    2010-09-01

    Full Text Available This paper contains a brief description of the most important operations that can be performed on spatial data such as spatial queries, create, update, insert, delete operations, conversions, operations on the map or analysis on grid cells. Each operation has a graphical example and some of them have code examples in Oracle and PostgreSQL.

  5. A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

    Science.gov (United States)

    Wang, Jun; Wang, Yang; Zeng, Hui

    2016-01-01

    A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

  6. Monthly and spatially resolved black carbon emission inventory of India: uncertainty analysis

    Directory of Open Access Journals (Sweden)

    U. Paliwal

    2016-10-01

    Full Text Available Black carbon (BC emissions from India for the year 2011 are estimated to be 901.11 ± 151.56 Gg yr−1 based on a new ground-up, GIS-based inventory. The grid-based, spatially resolved emission inventory includes, in addition to conventional sources, emissions from kerosene lamps, forest fires, diesel-powered irrigation pumps and electricity generators at mobile towers. The emissions have been estimated at district level and were spatially distributed onto grids at a resolution of 40 × 40 km2. The uncertainty in emissions has been estimated using a Monte Carlo simulation by considering the variability in activity data and emission factors. Monthly variation of BC emissions has also been estimated to account for the seasonal variability. To the total BC emissions, domestic fuels contributed most significantly (47 %, followed by industry (22 %, transport (17 %, open burning (12 % and others (2 %. The spatial and seasonal resolution of the inventory will be useful for modeling BC transport in the atmosphere for air quality, global warming and other process-level studies that require greater temporal resolution than traditional inventories.

  7. Spatial patterns of leaf δ13C and its relationship with plant functional groups and environmental factors in China

    Science.gov (United States)

    Li, Mingxu; Peng, Changhui; Wang, Meng; Yang, Yanzheng; Zhang, Kerou; Li, Peng; Yang, Yan; Ni, Jian; Zhu, Qiuan

    2017-07-01

    The leaf carbon isotope ratio (δ13C) is a useful parameter for predicting a plant's water use efficiency, as an indicator for plant classification, and even in the reconstruction of paleoclimatic environments. In this study, we investigated the spatial pattern of leaf δ13C values and its relationship with plant functional groups and environmental factors throughout China. The high leaf δ13C in the database appeared in central and western China, and the averaged leaf δ13C was -27.15‰, with a range from -21.05‰ to -31.5‰. The order of the averaged δ13C for plant life forms from most positive to most negative was subshrubs > herbs = shrubs > trees > subtrees. Leaf δ13C is also influenced by some environmental factors, such as mean annual precipitation, relative humidity, mean annual temperature, solar hours, and altitude, although the overall influences are still relatively weak, in particular the influence of MAT and altitude. And we further found that plant functional types are dominant factors that regulate the magnitude of leaf δ13C for an individual site, whereas environmental conditions are key to understanding spatial patterns of leaf δ13C when we consider China as a whole. Ultimately, we conducted a multiple regression model of leaf δ13C with environmental factors and mapped the spatial distribution of leaf δ13C in China by using this model. However, this partial least squares model overestimated leaf δ13C for most life forms, especially for deciduous trees, evergreen shrubs, and subtrees, and thus need more improvement in the future.

  8. Factors Impacting Spatial Patterns of Snow Distribution in a Small Catchment near Nome, AK

    Science.gov (United States)

    Chen, M.; Wilson, C. J.; Charsley-Groffman, L.; Busey, R.; Bolton, W. R.

    2017-12-01

    Snow cover plays an important role in the climate, hydrology and ecological systems of the Arctic due to its influence on the water balance, thermal regimes, vegetation and carbon flux. Thus, snow depth and coverage have been key components in all the earth system models but are often poorly represented for arctic regions, where fine scale snow distribution data is sparse. The snow data currently used in the models is at coarse resolution, which in turn leads to high uncertainty in model predictions. Through the DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic, high resolution snow distribution data is being developed and applied in catchment scale models to ultimately improve representation of snow and its interactions with other model components in the earth system models . To improve these models, it is important to identify key factors that control snow distribution and quantify the impacts of those factors on snow distribution. In this study, two intensive snow depth surveys (1 to 10 meters scale) were conducted for a 2.3 km2 catchment on the Teller road, near Nome, AK in the winter of 2016 and 2017. We used a statistical model to quantify the impacts of vegetation types, macro-topography, micro-topography, and meteorological parameters on measured snow depth. The results show that snow spatial distribution was similar between 2016 and 2017, snow depth was spatially auto correlated over small distance (2-5 meters), but not spatially auto correlated over larger distance (more than 2-5 meters). The coefficients of variation of snow depth was above 0.3 for all the snow survey transects (500-800 meters long). Variation of snow depth is governed by vegetation height, aspect, slope, surface curvature, elevation and wind speed and direction. We expect that this empirical statistical model can be used to estimate end of winter snow depth for the whole watershed and will further develop the model using data from other arctic regions to estimate

  9. Photography activities for developing students’ spatial orientation and spatial visualization

    Science.gov (United States)

    Hendroanto, Aan; van Galen, Frans; van Eerde, D.; Prahmana, R. C. I.; Setyawan, F.; Istiandaru, A.

    2017-12-01

    Spatial orientation and spatial visualization are the foundation of students’ spatial ability. They assist students’ performance in learning mathematics, especially geometry. Considering its importance, the present study aims to design activities to help young learners developing their spatial orientation and spatial visualization ability. Photography activity was chosen as the context of the activity to guide and support the students. This is a design research study consisting of three phases: 1) preparation and designing 2) teaching experiment, and 3) retrospective analysis. The data is collected by tests and interview and qualitatively analyzed. We developed two photography activities to be tested. In the teaching experiments, 30 students of SD Laboratorium UNESA, Surabaya were involved. The results showed that the activities supported the development of students’ spatial orientation and spatial visualization indicated by students’ learning progresses, answers, and strategies when they solved the problems in the activities.

  10. Spatial-structural analysis of leafless woody riparian vegetation for hydraulic considerations

    Science.gov (United States)

    Weissteiner, Clemens; Jalonen, Johanna; Järvelä, Juha; Rauch, Hans Peter

    2013-04-01

    Woody riparian vegetation is a vital element of riverine environments. On one hand woody riparian vegetation has to be taken into account from a civil engineering point of view due to boundary shear stress and vegetation drag. On the other hand it has to be considered from a river ecological point of view due to shadowing effects and as a source of organic material for aquatic habitats. In hydrodynamic and hydro-ecological studies the effects of woody riparian vegetation on flow patterns are usually investigated on a very detailed level. On the contrary vegetation elements and their spatial patterns are generally analysed and discussed on the basis of an integral approach measuring for example basal diameters, heights and projected plant areas. For a better understanding of the influence of woody riparian vegetation on turbulent flow and on river ecology, it is essential to record and analyse plant data sets on the same level of quality as for hydrodynamic or hydro-ecologic purposes. As a result of the same scale of the analysis it is possible to incorporate riparian vegetation as a sub-model in the hydraulic analysis. For plant structural components, such as branches on different topological levels it is crucial to record plant geometrical parameters describing the habitus of the plant on branch level. An exact 3D geometrical model of real plants allows for an extraction of various spatial-structural plant parameters. In addition, allometric relationships help to summarize and describe plant traits of riparian vegetation. This paper focuses on the spatial-structural composition of leafless riparia woddy vegetation. Structural and spatial analyses determine detailed geometric properties of the structural components of the plants. Geometrical and topological parameters were recorded with an electro-magnetic scanning device. In total, 23 plants (willows, alders and birches) were analysed in the study. Data were recorded on branch level, which allowed for the

  11. OpenMSI Arrayed Analysis Toolkit: Analyzing Spatially Defined Samples Using Mass Spectrometry Imaging

    Energy Technology Data Exchange (ETDEWEB)

    de Raad, Markus [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); de Rond, Tristan [Univ. of California, Berkeley, CA (United States); Rübel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Keasling, Jay D. [Univ. of California, Berkeley, CA (United States); Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Technical Univ. of Denmark, Lyngby (Denmark); Northen, Trent R. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Bowen, Benjamin P. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States)

    2017-05-03

    Mass spectrometry imaging (MSI) has primarily been applied in localizing biomolecules within biological matrices. Although well-suited, the application of MSI for comparing thousands of spatially defined spotted samples has been limited. One reason for this is a lack of suitable and accessible data processing tools for the analysis of large arrayed MSI sample sets. In this paper, the OpenMSI Arrayed Analysis Toolkit (OMAAT) is a software package that addresses the challenges of analyzing spatially defined samples in MSI data sets. OMAAT is written in Python and is integrated with OpenMSI (http://openmsi.nersc.gov), a platform for storing, sharing, and analyzing MSI data. By using a web-based python notebook (Jupyter), OMAAT is accessible to anyone without programming experience yet allows experienced users to leverage all features. OMAAT was evaluated by analyzing an MSI data set of a high-throughput glycoside hydrolase activity screen comprising 384 samples arrayed onto a NIMS surface at a 450 μm spacing, decreasing analysis time >100-fold while maintaining robust spot-finding. The utility of OMAAT was demonstrated for screening metabolic activities of different sized soil particles, including hydrolysis of sugars, revealing a pattern of size dependent activities. Finally, these results introduce OMAAT as an effective toolkit for analyzing spatially defined samples in MSI. OMAAT runs on all major operating systems, and the source code can be obtained from the following GitHub repository: https://github.com/biorack/omaat.

  12. Spatial Distribution Analysis of Scrub Typhus in Korea

    OpenAIRE

    Jin, Hong Sung; Chu, Chaeshin; Han, Dong Yeob

    2013-01-01

    Objective: This study analyzes the spatial distribution of scrub typhus in Korea. Methods: A spatial distribution of Orientia tsutsugamushi occurrence using a geographic information system (GIS) is presented, and analyzed by means of spatial clustering and correlations. Results: The provinces of Gangwon-do and Gyeongsangbuk-do show a low incidence throughout the year. Some districts have almost identical environmental conditions of scrub typhus incidence. The land use change of districts does...

  13. Spatial epidemiology of eastern equine encephalitis in Florida

    Directory of Open Access Journals (Sweden)

    Vander Kelen Patrick T

    2012-11-01

    Full Text Available Abstract Background Eastern Equine Encephalitis virus (EEEV is an alphavirus with high pathogenicity in both humans and horses. Florida continues to have the highest occurrence of human cases in the USA, with four fatalities recorded in 2010. Unlike other states, Florida supports year-round EEEV transmission. This research uses GIS to examine spatial patterns of documented horse cases during 2005–2010 in order to understand the relationships between habitat and transmission intensity of EEEV in Florida. Methods Cumulative incidence rates of EEE in horses were calculated for each county. Two cluster analyses were performed using density-based spatial clustering of applications with noise (DBSCAN. The first analysis was based on regional clustering while the second focused on local clustering. Ecological associations of EEEV were examined using compositional analysis and Euclidean distance analysis to determine if the proportion or proximity of certain habitats played a role in transmission. Results The DBSCAN algorithm identified five distinct regional spatial clusters that contained 360 of the 438 horse cases. The local clustering resulted in 18 separate clusters containing 105 of the 438 cases. Both the compositional analysis and Euclidean distance analysis indicated that the top five habitats positively associated with horse cases were rural residential areas, crop and pastureland, upland hardwood forests, vegetated non-forested wetlands, and tree plantations. Conclusions This study demonstrates that in Florida tree plantations are a focus for epizootic transmission of EEEV. It appears both the abundance and proximity of tree plantations are factors associated with increased risk of EEE in horses and therefore humans. This association helps to explain why there is are spatially distinct differences in the amount of EEE horse cases across Florida.

  14. Analysis the temporal and spatial impact of water harvesting on Aforestation processes, at the Northen Negev region, Israel

    Science.gov (United States)

    Argaman, E.; Egozi, R.; Goldshlager, N.

    2012-04-01

    Water availability in arid regions is a major limiting factor, which affect plant development. Therefore, knowledge about preliminary and ongoing spatial & temporal conditions (e.g. land surface properties, hydrological regime and vegetation dynamics) can improve greatly afforestation practice. The Ambassadors forest is one of the Jewish National Fund (JNF) new afforestation projects (initiated on 2005), which rely on water harvesting irrigation systems, located at the northern Negev region, Israel. Temporal and spatial processes are studied utilizing ground, air-borne and space-borne techniques for assessment of surface processes, that take place due to significant land-use change. Since 2005 the area shows significant variation of surface energy balance components which impact the spatial and temporal forest generation. Both human and climate affect these parameters, hence their influence is essential for future study of the region. Parameters of surface Albedo & Temperature and Vegetation dynamics are gathered by space-borne sensors (e.g. MODIS, Landsat & ALI) and verified at field scale in conjunction with ground-truth measurements of climate and soil properties. In addition, the project study various scenarios that might result from diverse climate trajectories that impact soil formation factors and therefore forest development. Preliminary results show that surface physical & ecoligical properties had changed significantly since the aforestation project began, comparing previous years. Sharp increase of surface albedo detected since 2005 that raised from 0.25 to 0.32, while vegetation density, estimated from NDVI, had dropped from annaul average of 0.21 down to 0.13 during 10-year time period. These changes are related to human interferance. The current paper presents the first phase of the long-term study of the Remote Sensing analysis and the current surface monitoring phase.

  15. Statistical Analysis of the Spatial Distribution of Multi-Elements in an Island Arc Region: Complicating Factors and Transfer by Water Currents

    Directory of Open Access Journals (Sweden)

    Atsuyuki Ohta

    2017-01-01

    Full Text Available The compositions and transfer processes affecting coastal sea sediments from the Seto Inland Sea and the Pacific Ocean are examined through the construction of comprehensive terrestrial and marine geochemical maps for western Japan. Two-way analysis of variance (ANOVA suggests that the elemental concentrations of marine sediments vary with particle size, and that this has a greater effect than the regional provenance of the terrestrial material. Cluster analysis is employed to reveal similarities and differences in the geochemistry of coastal sea and stream sediments. This analysis suggests that the geochemical features of fine sands and silts in the marine environment reflect those of stream sediments in the adjacent terrestrial areas. However, gravels and coarse sands do not show this direct relationship, which is likely a result of mineral segregation by strong tidal currents and the denudation of old basement rocks. Finally, the transport processes for the fine-grained sediments are discussed, using the spatial distribution patterns of outliers for those elements enriched in silt and clay. Silty and clayey sediments are found to be transported and dispersed widely by a periodic current in the inner sea, and are selectively deposited at the boundary of different water masses in the outer sea.

  16. Laser speckle imaging of rat retinal blood flow with hybrid temporal and spatial analysis method

    Science.gov (United States)

    Cheng, Haiying; Yan, Yumei; Duong, Timothy Q.

    2009-02-01

    Noninvasive monitoring of blood flow in retinal circulation will reveal the progression and treatment of ocular disorders, such as diabetic retinopathy, age-related macular degeneration and glaucoma. A non-invasive and direct BF measurement technique with high spatial-temporal resolution is needed for retinal imaging. Laser speckle imaging (LSI) is such a method. Currently, there are two analysis methods for LSI: spatial statistics LSI (SS-LSI) and temporal statistical LSI (TS-LSI). Comparing these two analysis methods, SS-LSI has higher signal to noise ratio (SNR) and TSLSI is less susceptible to artifacts from stationary speckle. We proposed a hybrid temporal and spatial analysis method (HTS-LSI) to measure the retinal blood flow. Gas challenge experiment was performed and images were analyzed by HTS-LSI. Results showed that HTS-LSI can not only remove the stationary speckle but also increase the SNR. Under 100% O2, retinal BF decreased by 20-30%. This was consistent with the results observed with laser Doppler technique. As retinal blood flow is a critical physiological parameter and its perturbation has been implicated in the early stages of many retinal diseases, HTS-LSI will be an efficient method in early detection of retina diseases.

  17. Variability of apparently homogeneous soilscapes in São Paulo state, Brazil: I. spatial analysis

    Directory of Open Access Journals (Sweden)

    M. van Den Berg

    2000-06-01

    Full Text Available The spatial variability of strongly weathered soils under sugarcane and soybean/wheat rotation was quantitatively assessed on 33 fields in two regions in São Paulo State, Brazil: Araras (15 fields with sugarcane and Assis (11 fields with sugarcane and seven fields with soybean/wheat rotation. Statistical methods used were: nested analysis of variance (for 11 fields, semivariance analysis and analysis of variance within and between fields. Spatial levels from 50 m to several km were analyzed. Results are discussed with reference to a previously published study carried out in the surroundings of Passo Fundo (RS. Similar variability patterns were found for clay content, organic C content and cation exchange capacity. The fields studied are quite homogeneous with respect to these relatively stable soil characteristics. Spatial variability of other characteristics (resin extractable P, pH, base- and Al-saturation and also soil colour, varies with region and, or land use management. Soil management for sugarcane seems to have induced modifications to greater depths than for soybean/wheat rotation. Surface layers of soils under soybean/wheat present relatively little variation, apparently as a result of very intensive soil management. The major part of within-field variation occurs at short distances (< 50 m in all study areas. Hence, little extra information would be gained by increasing sampling density from, say, 1/km² to 1/50 m². For many purposes, the soils in the study regions can be mapped with the same observation density, but residual variance will not be the same in all areas. Bulk sampling may help to reveal spatial patterns between 50 and 1.000 m.

  18. Defensible Spaces in Philadelphia: Exploring Neighborhood Boundaries Through Spatial Analysis

    Directory of Open Access Journals (Sweden)

    Rory Kramer

    2017-02-01

    Full Text Available Few spatial scales are as important to individual outcomes as the neighborhood. However, it is nearly impossible to define neighborhoods in a generalizable way. This article proposes that by shifting the focus to measuring neighborhood boundaries rather than neighborhoods, scholars can avoid the problem of the indefinable neighborhood and better approach questions of what predicts racial segregation across areas. By quantifying an externality space theory of neighborhood boundaries, this article introduces a novel form of spatial analysis to test where potential physical markers of neighborhood boundaries (major roads, rivers, railroads, and the like are associated with persistent racial boundaries between 1990 and 2010. Using Philadelphia as a case study, the paper identifies neighborhoods with persistent racial boundaries. It theorizes that local histories of white reactions to black in-migration explain which boundaries persistently resisted racial turnover, unlike the majority of Philadelphia’s neighborhoods, and that those racial boundaries shape the location, progress, and reaction to new residential development in those neighborhoods.

  19. Accounting for biotic spatial variability in fields: Case of resistance screening against sunflower Verticillium wilt.

    Science.gov (United States)

    Missonnier, Hélène; Jacques, Alban; Bang, JiSu; Daydé, Jean; Mirleau-Thebaud, Virginie

    2017-01-01

    In breeding for disease resistance, the magnitude of the genetic response is difficult to appreciate because of environmental stresses that interact with the plant genotype. We discuss herein the fundamental problems in breeding for disease resistance with the aim being to better understand the interactions between plant, pathogen, and spatial patterns. The goal of this study is to fine tune breeding decisions by incorporating spatial patterns of such biotic factors into the definition of disease-occurrence probability. We use a preexisting statistics method based on geostatistics for a descriptive analysis of biotic factors for trial quality control. The plant-population structure used for spatial-pattern analysis consists of two F1-hybrid cultivars, defined as symptomatic and asymptomatic controls with respect to the studied pathogen. The controls are inserted at specific locations to establish a grid arrangement over the field that include the F1-hybrid cultivars under evaluation. We characterize the spatial structure of the pathogen population and of the general plant environment-with undetermined but present abiotic constraints-not by using direct notation such as flower time or rainfall but by using plant behavior (i.e., leaf symptom severity, indirect notation). The analysis indicates areas with higher or lower risk of disease and reveals a correlation between the symptomatic control and the effective level of disease for sunflowers. This result suggests that the pathogen and/or abiotic components are major factors in determining the probability that a plant develops the disease, which could lead to a misinterpretation of plant resistance.

  20. Spatially heterogeneous land cover/land use and climatic risk factors of tick-borne feline cytauxzoonosis.

    Science.gov (United States)

    Raghavan, Ram K; Almes, Kelli; Goodin, Doug G; Harrington, John A; Stackhouse, Paul W

    2014-07-01

    Feline cytauxzoonosis is a highly fatal tick-borne disease caused by a hemoparasitic protozoan, Cytauxzoon felis. This disease is a leading cause of mortality for cats in the Midwestern United States, and no vaccine or effective treatment options exist. Prevention based on knowledge of risk factors is therefore vital. Associations of different environmental factors, including recent climate were evaluated as potential risk factors for cytauxzoonosis using Geographic Information Systems (GIS). There were 69 cases determined to be positive for cytauxzoonosis based upon positive identification of C. felis within blood film examinations, tissue impression smears, or histopathologic examination of tissues. Negative controls totaling 123 were selected from feline cases that had a history of fever, malaise, icterus, and anorexia but lack of C. felis within blood films, impression smears, or histopathologic examination of tissues. Additional criteria to rule out C. felis among controls were the presence of regenerative anemia, cytologic examination of blood marrow or lymph node aspirate, other causative agent diagnosed, or survival of 25 days or greater after testing. Potential environmental determinants were derived from publicly available sources, viz., US Department of Agriculture (soil attributes), US Geological Survey (land-cover/landscape, landscape metrics), and NASA (climate). Candidate variables were screened using univariate logistic models with a liberal p value (0.2), and associations with cytauxzoonosis were modeled using a global multivariate logistic model (p<0.05). Spatial heterogeneity among significant variables in the study region was modeled using a geographically weighted regression (GWR) approach. Total Edge Contrast Index (TECI), grassland-coverage, humidity conditions recorded during the 9(th) week prior to case arrival, and an interaction variable, "diurnal temperature range × percent mixed forest area" were significant risk factors for

  1. Participative Spatial Scenario Analysis for Alpine Ecosystems

    Science.gov (United States)

    Kohler, Marina; Stotten, Rike; Steinbacher, Melanie; Leitinger, Georg; Tasser, Erich; Schirpke, Uta; Tappeiner, Ulrike; Schermer, Markus

    2017-10-01

    Land use and land cover patterns are shaped by the interplay of human and ecological processes. Thus, heterogeneous cultural landscapes have developed, delivering multiple ecosystem services. To guarantee human well-being, the development of land use types has to be evaluated. Scenario development and land use and land cover change models are well-known tools for assessing future landscape changes. However, as social and ecological systems are inextricably linked, land use-related management decisions are difficult to identify. The concept of social-ecological resilience can thereby provide a framework for understanding complex interlinkages on multiple scales and from different disciplines. In our study site (Stubai Valley, Tyrol/Austria), we applied a sequence of steps including the characterization of the social-ecological system and identification of key drivers that influence farmers' management decisions. We then developed three scenarios, i.e., "trend", "positive" and "negative" future development of farming conditions and assessed respective future land use changes. Results indicate that within the "trend" and "positive" scenarios pluri-activity (various sources of income) prevents considerable changes in land use and land cover and promotes the resilience of farming systems. Contrarily, reductions in subsidies and changes in consumer behavior are the most important key drivers in the negative scenario and lead to distinct abandonment of grassland, predominantly in the sub-alpine zone of our study site. Our conceptual approach, i.e., the combination of social and ecological methods and the integration of local stakeholders' knowledge into spatial scenario analysis, resulted in highly detailed and spatially explicit results that can provide a basis for further community development recommendations.

  2. Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis

    Science.gov (United States)

    Afzal, Peyman; Mirzaei, Misagh; Yousefi, Mahyar; Adib, Ahmad; Khalajmasoumi, Masoumeh; Zarifi, Afshar Zia; Foster, Patrick; Yasrebi, Amir Bijan

    2016-07-01

    Recognition of significant geochemical signatures and separation of geochemical anomalies from background are critical issues in interpretation of stream sediment data to define exploration targets. In this paper, we used staged factor analysis in conjunction with the concentration-number (C-N) fractal model to generate exploration targets for prospecting Cr and Fe mineralization in Balvard area, SE Iran. The results show coexistence of derived multi-element geochemical signatures of the deposit-type sought and ultramafic-mafic rocks in the NE and northern parts of the study area indicating significant chromite and iron ore prospects. In this regard, application of staged factor analysis and fractal modeling resulted in recognition of significant multi-element signatures that have a high spatial association with host lithological units of the deposit-type sought, and therefore, the generated targets are reliable for further prospecting of the deposit in the study area.

  3. Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2008-09

    Science.gov (United States)

    ,

    2009-01-01

    Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is useful for analyzing a wide variety of spatial data. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This fact sheet presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup during 2008 and 2009. After a summary of GIS Workgroup capabilities, brief descriptions of activities by project at the local and national levels are presented. Projects are grouped by the fiscal year (October-September 2008 or 2009) the project ends and include overviews, project images, and Internet links to additional project information and related publications or articles.

  4. Spatial analysis of factors associated with household subscription to the National Health Insurance Scheme in rural Ghana

    Directory of Open Access Journals (Sweden)

    Stephen Manortey

    2014-02-01

    Full Text Available The use of health insurance schemes in financing healthcare delivery and to minimize the poverty gap is gaining considerable recognition among the least developed and resource challenged countries around the world. With the implementation of the socialized health insurance scheme, Ghana has taken the lead in Sub-Saharan Africa and now working out further strategies to gain universal coverage among her citizenry. The primary goal of this study is to explore the spatial relationship between the residential homes and demographic features of the people in the Barekese subdistrict in Ghana on the probability to enroll the entire household unit in the National Health Insurance Scheme (NHIS. Household level data were gathered from 20 communities on the enrollment status into the NHIS alongside demographic and socioeconomic indicators and the spatial location of every household that participated in the study. Kulldorff’s purely spatial scan statistic was used to detect geographic clusters of areas with participatory households that have either higher or lower enrollment patterns in the insurance program. Logistic regression models on selected demographic and socioeconomic indicators were built to predict the effect on the odds of enrolling an entire household membership in the NHIS. Three clusters significantly stood out to have either high or low enrollment patterns in the health insurance program taking into accounts the number of households in those sub-zones of the study region. Households in the Cluster 1 insurance group have very high travel expenses compared to their counterparts in the other idenfied clusters. Travel cost and time to the NHIS registration center to enroll in the program were both significant predictors to participation in the program when controlling for cluster effect. Residents in the High socioeconomic group have about 1.66 [95% CI: 1.27-2.17] times the odds to enroll complete households in the insurance program compared to

  5. Nitrogen Oxide Emission, Economic Growth and Urbanization in China: a Spatial Econometric Analysis

    Science.gov (United States)

    Zhou, Zhimin; Zhou, Yanli; Ge, Xiangyu

    2018-01-01

    This research studies the nexus of nitrogen oxide emissions and economic development/urbanization. Under the environmental Kuznets curve (EKC) hypothesis, we apply the analysis technique of spatial panel data in the STIRPAT framework, and thus obtain the estimated impacts of income/urbanization on nitrogen oxide emission systematically. The empirical findings suggest that spatial dependence on nitrogen oxide emission distribution exist at provincial level, and the inverse N-shape EKC describes both income-nitrogen oxide and urbanization-nitrogen oxide nexuses. In addition, some well-directed policy advices are made to reduce the nitrogen oxide emission in future.

  6. The Determinants of VAT Introduction : A Spatial Duration Analysis

    NARCIS (Netherlands)

    Cizek, P.; Lei, J.; Ligthart, J.E.

    2012-01-01

    Abstract: The spatial survival models typically impose frailties, which characterize unobserved heterogeneity, to be spatially correlated. This specification relies highly on a pre-determinate covariance structure of the errors. However, the spatial effect may not only exist in the unobserved

  7. Spatial analysis of hemorrhagic fever with renal syndrome in Zibo City, China, 2009-2012.

    Directory of Open Access Journals (Sweden)

    Feng Cui

    Full Text Available BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS is highly endemic in mainland China, where human cases account for 90% of the total global cases. Zibo City is one of the most serious affected areas in Shandong Province China with the HFRS incidence increasing sharply from 2009 to 2012. However, the hotspots of HFRS in Zibo remained unclear. Thus, a spatial analysis was conducted with the aim to explore the spatial, spatial-temporal and seasonal patterns of HFRS in Zibo from 2009 to 2012, and to provide guidance for formulating regional prevention and control strategies. METHODS: The study was based on the reported cases of HFRS from the National Notifiable Disease Surveillance System. Annualized incidence maps and seasonal incidence maps were produced to analyze the spatial and seasonal distribution of HFRS in Zibo City. Then spatial scan statistics and space-time scan statistics were conducted to identify clusters of HFRS. RESULTS: There were 200 cases reported in Zibo City during the 4-year study period. One most likely cluster and one secondary cluster for high incidence of HFRS were identified by the space-time analysis. And the most likely cluster was found to exist at Yiyuan County in October to December 2012. The human infections in the fall and winter reflected a seasonal characteristic pattern of Hantaan virus (HTNV transmission. The secondary cluster was detected at the center of Zibo in May to June 2009, presenting a seasonal characteristic of Seoul virus (SEOV transmission. CONCLUSION: To control and prevent HFRS in Zibo city, the comprehensive preventive strategy should be implemented in the southern areas of Zibo in autumn and in the northern areas of Zibo in spring.

  8. Analysis of spatial distribution and marketing area of boutique hotels in Shanghai

    Directory of Open Access Journals (Sweden)

    Chu Xueqin

    2017-08-01

    Full Text Available Basing on data collected from Google Earth and Baidu Map,we inputed the coordinates of the address of boutique hotels,five-star hotels in Shanghai,scenic spots above 3A in the urban area of Shanghai and historical relics protection units in Shanghai et al into the Arc GIS,which locates these units in accurate position.We measured some distance data by the tool of spatial analysis.The distribution map and marketing area analysis of boutique hotel were made.

  9. Malaria prevalence, spatial clustering and risk factors in a low endemic area of Eastern Rwanda: a cross sectional study

    NARCIS (Netherlands)

    Rulisa, Stephen; Kateera, Fredrick; Bizimana, Jean Pierre; Agaba, Steven; Dukuzumuremyi, Javier; Baas, Lisette; de Dieu Harelimana, Jean; Mens, Petra F.; Boer, Kimberly R.; de Vries, Peter J.

    2013-01-01

    Rwanda reported significant reductions in malaria burden following scale up of control intervention from 2005 to 2010. This study sought to; measure malaria prevalence, describe spatial malaria clustering and investigate for malaria risk factors among health-centre-presumed malaria cases and their

  10. A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data

    NARCIS (Netherlands)

    DeSarbo, WS; Kim, Y; Fong, D

    1999-01-01

    We present a new Bayesian formulation of a vector multidimensional scaling procedure for the spatial analysis of binary choice data. The Gibbs sampler is gainfully employed to estimate the posterior distribution of the specified scalar products, bilinear model parameters. The computational procedure

  11. The visualization and analysis of urban facility pois using network kernel density estimation constrained by multi-factors

    Directory of Open Access Journals (Sweden)

    Wenhao Yu

    Full Text Available The urban facility, one of the most important service providers is usually represented by sets of points in GIS applications using POI (Point of Interest model associated with certain human social activities. The knowledge about distribution intensity and pattern of facility POIs is of great significance in spatial analysis, including urban planning, business location choosing and social recommendations. Kernel Density Estimation (KDE, an efficient spatial statistics tool for facilitating the processes above, plays an important role in spatial density evaluation, because KDE method considers the decay impact of services and allows the enrichment of the information from a very simple input scatter plot to a smooth output density surface. However, the traditional KDE is mainly based on the Euclidean distance, ignoring the fact that in urban street network the service function of POI is carried out over a network-constrained structure, rather than in a Euclidean continuous space. Aiming at this question, this study proposes a computational method of KDE on a network and adopts a new visualization method by using 3-D "wall" surface. Some real conditional factors are also taken into account in this study, such as traffic capacity, road direction and facility difference. In practical works the proposed method is implemented in real POI data in Shenzhen city, China to depict the distribution characteristic of services under impacts of multi-factors.

  12. Reconstruction and analysis of temperature and density spatial profiles inertial confinement fusion implosion cores

    International Nuclear Information System (INIS)

    Mancini, R. C.

    2007-01-01

    We discuss several methods for the extraction of temperature and density spatial profiles in inertial confinement fusion implosion cores based on the analysis of the x-ray emission from spectroscopic tracers added to the deuterium fuel. The ideas rely on (1) detailed spectral models that take into account collisional-radiative atomic kinetics, Stark broadened line shapes, and radiation transport calculations, (2) the availability of narrow-band, gated pinhole and slit x-ray images, and space-resolved line spectra of the core, and (3) several data analysis and reconstruction methods that include a multi-objective search and optimization technique based on a novel application of Pareto genetic algorithms to plasma spectroscopy. The spectroscopic analysis yields the spatial profiles of temperature and density in the core at the collapse of the implosion, and also the extent of shell material mixing into the core. Results are illustrated with data recorded in implosion experiments driven by the OMEGA and Z facilities

  13. Time-Series Analysis of Continuously Monitored Blood Glucose: The Impacts of Geographic and Daily Lifestyle Factors

    Directory of Open Access Journals (Sweden)

    Sean T. Doherty

    2015-01-01

    Full Text Available Type 2 diabetes is known to be associated with environmental, behavioral, and lifestyle factors. However, the actual impacts of these factors on blood glucose (BG variation throughout the day have remained relatively unexplored. Continuous blood glucose monitors combined with human activity tracking technologies afford new opportunities for exploration in a naturalistic setting. Data from a study of 40 patients with diabetes is utilized in this paper, including continuously monitored BG, food/medicine intake, and patient activity/location tracked using global positioning systems over a 4-day period. Standard linear regression and more disaggregated time-series analysis using autoregressive integrated moving average (ARIMA are used to explore patient BG variation throughout the day and over space. The ARIMA models revealed a wide variety of BG correlating factors related to specific activity types, locations (especially those far from home, and travel modes, although the impacts were highly personal. Traditional variables related to food intake and medications were less often significant. Overall, the time-series analysis revealed considerable patient-by-patient variation in the effects of geographic and daily lifestyle factors. We would suggest that maps of BG spatial variation or an interactive messaging system could provide new tools to engage patients and highlight potential risk factors.

  14. Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Jianhua Ni

    2016-08-01

    Full Text Available The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

  15. Spatial elements of mortality risk in old-growth forests

    Science.gov (United States)

    Das, Adrian; Battles, John; van Mantgem, Phillip J.; Stephenson, Nathan L.

    2008-01-01

    For many species of long-lived organisms, such as trees, survival appears to be the most critical vital rate affecting population persistence. However, methods commonly used to quantify tree death, such as relating tree mortality risk solely to diameter growth, almost certainly do not account for important spatial processes. Our goal in this study was to detect and, if present, to quantify the relevance of such processes. For this purpose, we examined purely spatial aspects of mortality for four species, Abies concolor, Abies magnifica, Calocedrus decurrens, and Pinus lambertiana, in an old-growth conifer forest in the Sierra Nevada of California, USA. The analysis was performed using data from nine fully mapped long-term monitoring plots.In three cases, the results unequivocally supported the inclusion of spatial information in models used to predict mortality. For Abies concolor, our results suggested that growth rate may not always adequately capture increased mortality risk due to competition. We also found evidence of a facilitative effect for this species, with mortality risk decreasing with proximity to conspecific neighbors. For Pinus lambertiana, mortality risk increased with density of conspecific neighbors, in keeping with a mechanism of increased pathogen or insect pressure (i.e., a Janzen-Connell type effect). Finally, we found that models estimating risk of being crushed were strongly improved by the inclusion of a simple index of spatial proximity.Not only did spatial indices improve models, those improvements were relevant for mortality prediction. For P. lambertiana, spatial factors were important for estimation of mortality risk regardless of growth rate. For A. concolor, although most of the population fell within spatial conditions in which mortality risk was well described by growth, trees that died occurred outside those conditions in a disproportionate fashion. Furthermore, as stands of A. concolor become increasingly dense, such spatial

  16. Spatial ability in computer-aided design courses

    OpenAIRE

    Torner Ribé, Jordi; Alpiste Penalba, Francesc; Brigos Hermida, Miguel Ángel

    2014-01-01

    Many studies have demonstrated that spatial ability is an important factor in the study of Industrial Engineering. Spatial ability is fundamentally important to the work of an engineer, as it is vital for project design. Among other elements, spatial ability correlates with factors such as good academic results and a natural ability to learn how to use I.T systems and computer programs. Furthermore, the new framework drawn up by the European Higher Education Area (EHEA) guides us as to the...

  17. An update to the analysis of the Canadian Spatial Reference System

    Science.gov (United States)

    Ferland, R.; Piraszewski, M.; Craymer, M.

    2015-12-01

    The primary objective of the Canadian Spatial Reference System (CSRS) is to provide users access to a consistent geo-referencing infrastructure over the Canadian landmass. Global Navigation Satellite System (GNSS) positioning accuracy requirements ranges from meter level to mm level (e.g.: crustal deformation). The highest level of the Canadian infrastructure consist of a network of continually operating GPS and GNSS receivers, referred to as active control stations. The network includes all Canadian public active control stations, some bordering US CORS and Alaska stations, Greenland active control stations, as well as a selection of IGS reference frame stations. The Bernese analysis software is used for the daily processing and the combination into weekly solutions which form the basis for this analysis. IGS weekly final orbit, Earth Rotation parameters (ERP's) and coordinates products are used in the processing. For the more demanding users, the time dependant changes of station coordinates is often more important.All station coordinate estimates and related covariance information is used in this analysis. For each input solution, variance factor, translation, rotation and scale (and if needed their rates) or subsets of these are estimated. In the combination of these weekly solutions, station positions and velocities are estimated. Since the time series from the stations in these networks often experience changes in behavior, new (or reuse of) parameters are generally used in these situations. As is often the case with real data, unrealistic coordinates may occur. Automatic detection and removal of outliers is used in these cases. For the transformation, position and velocity parameters loose apriori estimates and uncertainties are provided. Alignment using the usual Helmert transformation to the latest IGb08 realization of ITRF is also performed during the adjustment.

  18. The Evolution of Spatial Representation During Complex Visual Data Analysis: Knowing When and How to be Exact

    National Research Council Canada - National Science Library

    Schunn, Christian D; Saner, Lelyn D; Trafton, J. G; Trickett, Susan B; Kirschenbaum, Susan K; Knepp, Michael; Shoup, Melanie

    2005-01-01

    ... (weather forecasting, submarine target motion analysis, and fMRI data analysis). Internal spatial representations are coded from spontaneous gestures made during cued-recall summaries of problem solving activities...

  19. Exploratory Temporal and Spatial Analysis of Myocardial Infarction Hospitalizations in Calgary, Canada

    Directory of Open Access Journals (Sweden)

    Xiaoxiao Liu

    2017-12-01

    Full Text Available Spatial and temporal analyses are critical to understand the pattern of myocardial infarction (MI hospitalizations over space and time, and to identify their underlying determinants. In this paper, we analyze MI hospitalizations in Calgary from 2004 to 2013, stratified by age and gender. First, a seasonal trend decomposition analyzes the seasonality; then a linear regression models the trend component. Moran’s I and hot spot analyses explore the spatial pattern. Though exploratory, results show that most age and gender groups feature a statistically significant decline over the 10 years, consistent with previous studies in Canada. Decline rates vary across ages and genders, with the slowest decline observed for younger males. Each gender exhibits a seasonal pattern with peaks in both winter and summer. Spatially, MI hot spots are identified in older communities, and in socioeconomically and environmentally disadvantaged communities. In the older communities, higher MI rates appear to be more highly associated with demographics. Conversely, worse air quality appears to be locally associated with higher MI incidence in younger age groups. The study helps identify areas of concern, where MI hot spots are identified for younger age groups, suggesting the need for localized public health policies to target local risk factors.

  20. An exploratory spatial analysis of social vulnerability and smoke plum dispersion in the U.S

    Science.gov (United States)

    Cassandra Johnson Gaither; Scott Goodrick; Bryn Elise Murphy; Neelam Poudyal

    2015-01-01

    This study explores the spatial association between social vulnerability and smoke plume dispersion at the census block group level for the 13 southern states in the USDA Forest Service’s Region 8. Using environmental justice as a conceptual basis, we use Exploratory Spatial Data Analysis to identify clusters or “hot spots” for the incidence of both higher than average...

  1. Uplink Interference Analysis for Two-tier Cellular Networks with Diverse Users under Random Spatial Patterns

    OpenAIRE

    Bao, Wei; Liang, Ben

    2013-01-01

    Multi-tier architecture improves the spatial reuse of radio spectrum in cellular networks, but it introduces complicated heterogeneity in the spatial distribution of transmitters, which brings new challenges in interference analysis. In this work, we present a stochastic geometric model to evaluate the uplink interference in a two-tier network considering multi-type users and base stations. Each type of tier-1 users and tier-2 base stations are modeled as independent homogeneous Poisson point...

  2. A GIS-based spatial correlation analysis for ambient air pollution and AECOPD hospitalizations in Jinan, China.

    Science.gov (United States)

    Wang, Wenqiao; Ying, Yangyang; Wu, Quanyuan; Zhang, Haiping; Ma, Dedong; Xiao, Wei

    2015-03-01

    Acute exacerbations of COPD (AECOPD) are important events during disease procedure. AECOPD have negative effect on patients' quality of life, symptoms and lung function, and result in high socioeconomic costs. Though previous studies have demonstrated the significant association between outdoor air pollution and AECOPD hospitalizations, little is known about the spatial relationship utilized a spatial analyzing technique- Geographical Information System (GIS). Using GIS to investigate the spatial association between ambient air pollution and AECOPD hospitalizations in Jinan City, 2009. 414 AECOPD hospitalization cases in Jinan, 2009 were enrolled in our analysis. Monthly concentrations of five monitored air pollutants (NO2, SO2, PM10, O3, CO) during January 2009-December 2009 were provided by Environmental Protection Agency of Shandong Province. Each individual was geocoded in ArcGIS10.0 software. The spatial distribution of five pollutants and the temporal-spatial specific air pollutants exposure level for each individual was estimated by ordinary Kriging model. Spatial autocorrelation (Global Moran's I) was employed to explore the spatial association between ambient air pollutants and AECOPD hospitalizations. A generalized linear model (GLM) using a Poisson distribution with log-link function was used to construct a core model. At residence, concentrations of SO2, PM10, NO2, CO, O3 and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of SO2, PM10, CO, O3, NO2 at residence is 15.88, 13.93, 12.60, 4.02, 2.44 respectively, while at workplace, concentrations of PM10, SO2, O3, CO and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of PM10, SO2, O3, CO at workplace is 11.39, 8.07, 6.10, and 5.08 respectively. After adjusting for potential confounders in the model, only the PM10 concentrations at workplace showed statistical significance, with a 10 μg/m(3) increase of PM10 at

  3. Spatial analysis of infection by the human immunodeficiency virus among pregnant women

    Directory of Open Access Journals (Sweden)

    Eliane Rolim de Holanda

    2015-06-01

    Full Text Available OBJECTIVES: to analyze the spatial distribution of reported cases of pregnant women infected by the human immunodeficiency virus and to identify the urban areas with greater social vulnerability to the infection among pregnant women.METHOD: ecological study, developed by means of spatial analysis techniques of area data. Secondary data were used from the Brazilian National Disease Notification System for the city of Recife, Pernambuco. Birth data were obtained from the Brazilian Information System on Live Births and socioeconomic data from the 2010 Demographic Census.RESULTS: the presence of spatial self-correlation was verified. Moran's Index was significant for the distribution. Clusters were identified, considered as high-risk areas, located in grouped neighborhoods, with equally high infection rates among pregnant women. A neighborhood located in the Northwest of the city was distinguished, considered in an epidemiological transition phase.CONCLUSION: precarious living conditions, as evidenced by the indicators illiteracy, absence of prenatal care and poverty, were relevant for the risk of vertical HIV transmission, converging to the grouping of cases among disadvantaged regions.

  4. Spatial and Temporal Variations of PM2.5 and Its Relation to Meteorological Factors in the Urban Area of Nanjing, China

    Directory of Open Access Journals (Sweden)

    Tao Chen

    2016-09-01

    Full Text Available The serious air pollution problem has aroused widespread public concerns in China. Nanjing city, as one of the famous cities of China, is faced with the same situation. This research aims to investigate spatial and temporal distribution characteristics of fine particulate matter (PM2.5 and the influence of weather factors on PM2.5 in Nanjing using Spearman-Rank analysis and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN method. Hourly PM2.5 observation data and daily meteorological data were collected from 1 April 2013 to 31 December 2015. The spatial distribution result shows that the Maigaoqiao site suffered the most serious pollution. Daily PM2.5 concentrations in Nanjing varied from 7.3 μg/m3 to 336.4 μg/m3. The highest concentration was found in winter and the lowest in summer. The diurnal variation of PM2.5 increased greatly from 6 to 10 a.m. and after 6 p.m., while the concentration exhibited few variations in summer. In addition, the concentration was slightly higher on weekends compared to weekdays. PM2.5 was found to exhibit a reversed relation with wind speed, relative humidity, and precipitation. Although temperature had a positive association with PM2.5 in most months, a negative correlation was observed during the whole period. Additionally, a high concentration was mainly brought with the wind with a southwest direction and several relevant factors are discussed to explain the difference of the impacts of diverse wind directions.

  5. SPHARA--a generalized spatial Fourier analysis for multi-sensor systems with non-uniformly arranged sensors: application to EEG.

    Science.gov (United States)

    Graichen, Uwe; Eichardt, Roland; Fiedler, Patrique; Strohmeier, Daniel; Zanow, Frank; Haueisen, Jens

    2015-01-01

    Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction. We propose a new approach for spatial harmonic analysis (SPHARA) that extends the classical spatial Fourier analysis to EEG sensors positioned non-uniformly on the surface of the head. The proposed method is based on the eigenanalysis of the discrete Laplace-Beltrami operator defined on a triangular mesh. We present several ways to discretize the continuous Laplace-Beltrami operator and compare the properties of the resulting basis functions computed using these discretization methods. We apply SPHARA to somatosensory evoked potential data from eleven volunteers and demonstrate the ability of the method for spatial data decomposition, dimensionality reduction and noise suppression. When employing SPHARA for dimensionality reduction, a significantly more compact representation can be achieved using the FEM approach, compared to the other discretization methods. Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary. The capability of SPHARA for noise suppression is shown using artificial data. We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications.

  6. Temporal and spatial analysis of psittacosis in association with poultry farming in the Netherlands, 2000-2015.

    Science.gov (United States)

    Hogerwerf, Lenny; Holstege, Manon M C; Benincà, Elisa; Dijkstra, Frederika; van der Hoek, Wim

    2017-07-26

    Human psittacosis is a highly under diagnosed zoonotic disease, commonly linked to psittacine birds. Psittacosis in birds, also known as avian chlamydiosis, is endemic in poultry, but the risk for people living close to poultry farms is unknown. Therefore, our study aimed to explore the temporal and spatial patterns of human psittacosis infections and identify possible associations with poultry farming in the Netherlands. We analysed data on 700 human cases of psittacosis notified between 01-01-2000 and 01-09-2015. First, we studied the temporal behaviour of psittacosis notifications by applying wavelet analysis. Then, to identify possible spatial patterns, we applied spatial cluster analysis. Finally, we investigated the possible spatial association between psittacosis notifications and data on the Dutch poultry sector at municipality level using a multivariable model. We found a large spatial cluster that covered a highly poultry-dense area but additional clusters were found in areas that had a low poultry density. There were marked geographical differences in the awareness of psittacosis and the amount and the type of laboratory diagnostics used for psittacosis, making it difficult to draw conclusions about the correlation between the large cluster and poultry density. The multivariable model showed that the presence of chicken processing plants and slaughter duck farms in a municipality was associated with a higher rate of human psittacosis notifications. The significance of the associations was influenced by the inclusion or exclusion of farm density in the model. Our temporal and spatial analyses showed weak associations between poultry-related variables and psittacosis notifications. Because of the low number of psittacosis notifications available for analysis, the power of our analysis was relative low. Because of the exploratory nature of this research, the associations found cannot be interpreted as evidence for airborne transmission of psittacosis from

  7. Few-mode fiber, splice and SDM component characterization by spatially-diverse optical vector network analysis.

    Science.gov (United States)

    Rommel, Simon; Mendinueta, José Manuel Delgado; Klaus, Werner; Sakaguchi, Jun; Olmos, Juan José Vegas; Awaji, Yoshinari; Monroy, Idelfonso Tafur; Wada, Naoya

    2017-09-18

    This paper discusses spatially diverse optical vector network analysis for space division multiplexing (SDM) component and system characterization, which is becoming essential as SDM is widely considered to increase the capacity of optical communication systems. Characterization of a 108-channel photonic lantern spatial multiplexer, coupled to a 36-core 3-mode fiber, is experimentally demonstrated, extracting the full impulse response and complex transfer function matrices as well as insertion loss (IL) and mode-dependent loss (MDL) data. Moreover, the mode-mixing behavior of fiber splices in the few-mode multi-core fiber and their impact on system IL and MDL are analyzed, finding splices to cause significant mode-mixing and to be non-negligible in system capacity analysis.

  8. Parents' Spatial Language Mediates a Sex Difference in Preschoolers' Spatial-Language Use.

    Science.gov (United States)

    Pruden, Shannon M; Levine, Susan C

    2017-11-01

    Do boys produce more terms than girls to describe the spatial world-that is, dimensional adjectives (e.g., big, little, tall, short), shape terms (e.g., circle, square), and words describing spatial features and properties (e.g., bent, curvy, edge)? If a sex difference in children's spatial-language use exists, is it related to the spatial language that parents use when interacting with children? We longitudinally tracked the development of spatial-language production in children between the ages of 14 and 46 months in a diverse sample of 58 parent-child dyads interacting in their homes. Boys produced and heard more of these three categories of spatial words, which we call "what" spatial types (i.e., unique "what" spatial words), but not more of all other word types, than girls. Mediation analysis revealed that sex differences in children's spatial talk at 34 to 46 months of age were fully mediated by parents' earlier spatial-language use, when children were 14 to 26 months old, time points at which there was no sex difference in children's spatial-language use.

  9. Spatial epidemiology of Toxoplasma gondii infection in goats in Serbia

    Directory of Open Access Journals (Sweden)

    Vitomir Djokić

    2014-05-01

    Full Text Available A major risk factor for Toxoplasma gondii infection is consumption of undercooked meat. Increasing demand for goat meat is likely to promote the role of this animal for human toxoplasmosis. As there are virtually no data on toxoplasmosis in goats in Serbia, we undertook a cross-sectional serological study, including prediction modelling using geographical information systems (GIS. Sera from 431 goats reared in 143 households/farms throughout Serbia, sampled between January 2010 and September 2011, were examined for T. gondii antibodies by a modified agglutination test. Seroprevalence was 73.3% at the individual level and 84.6% at the farm level. Risk factor analysis showed above two-fold higher risk of infection for goats used for all purposes compared to dairy goats (P = 0.012, almost seven-fold higher risk for goats kept as sole species versus those kept with other animals (P = 0.001 and a two-fold lower risk for goats introduced from outside the farm compared to those raised on the farm (P = 0.027. Moreover, households/farms located in centre-eastern Serbia were found to be less often infected than those in northern Serbia (P = 0.004. The risk factor analysis was fully supported by spatial analysis based on a GIS database containing data on origin, serology, land cover, elevation, meteorology and a spatial prediction map based on kriging analysis, which showed western Serbia as the area most likely for finding goats positive for T. gondii and centre-eastern Serbia as the least likely. In addition, rainfall favoured seropositivity, whereas temperature, humidity and elevation did not.

  10. The spatial metaphor of Utopia in Russian culture and in analysis.

    Science.gov (United States)

    Tsivinsky, Vladimir

    2014-02-01

    The spatial metaphor of Utopia is considered from a Jungian perspective along with its role in Russian culture and in analysis. Such post-Jungian concepts as the cultural complex and the archetypal story pattern of a victim are used in considering the desperate longing for a rescuer in patients' narratives and in Russian society. A clinical vignette is provided to illustrate these ideas. © 2014, The Society of Analytical Psychology.

  11. Research on the optimization of air quality monitoring station layout based on spatial grid statistical analysis method.

    Science.gov (United States)

    Li, Tianxin; Zhou, Xing Chen; Ikhumhen, Harrison Odion; Difei, An

    2018-05-01

    In recent years, with the significant increase in urban development, it has become necessary to optimize the current air monitoring stations to reflect the quality of air in the environment. Highlighting the spatial representation of some air monitoring stations using Beijing's regional air monitoring station data from 2012 to 2014, the monthly mean particulate matter concentration (PM10) in the region was calculated and through the IDW interpolation method and spatial grid statistical method using GIS, the spatial distribution of PM10 concentration in the whole region was deduced. The spatial distribution variation of districts in Beijing using the gridding model was performed, and through the 3-year spatial analysis, PM10 concentration data including the variation and spatial overlay (1.5 km × 1.5 km cell resolution grid), the spatial distribution result obtained showed that the total PM10 concentration frequency variation exceeded the standard. It is very important to optimize the layout of the existing air monitoring stations by combining the concentration distribution of air pollutants with the spatial region using GIS.

  12. Determinants of developing widened spatial QRS-T angle in HIV-infected individuals

    DEFF Research Database (Denmark)

    Dawood, Farah Z; Roediger, Mollie P; Grandits, Greg

    2014-01-01

    BACKGROUND: A widened electrocardiographic spatial QRS-T angle has been shown to be predictive of cardiovascular disease in HIV-infected individuals. However, determinants and risk factors of developing widened QRS-T angle over time in this population remain unknown. METHODS AND RESULTS: Spatial...... QRS-T angle was automatically measured from standard electrocardiogram of 1444 HIV-infected individuals without baseline widened spatial QRS-T angle from the Strategies for Management of Antiretroviral Therapy [SMART], a clinical trial comparing two antiretroviral treatment strategies [Drug...... Conservation (DC) vs. Viral Suppression (VS)]. Conditional logistic regression analysis was used to examine the association between baseline characteristics and incident widened spatial QRS-T angle (a new angle>93° in males and>74° in females). During 2544 person-years of follow-up, 199 participants developed...

  13. [Development of spatial orientation during pilot training].

    Science.gov (United States)

    Ivanov, V V; Vorob'ev, O A; Snipkov, Iu Iu

    1988-01-01

    The problem of spatial orientation of pilots flying high-altitude aircraft is in the focus of present-day aviation medicine because of a growing number of accidents in the air. One of the productive lines of research is to study spatial orientation in terms of active formation and maintenance of its imagery in a complex environment. However investigators usually emphasize the role of visual (instrumental) information in the image construction, almost ignoring the sensorimotor component of spatial orientation. The theoretical analysis of the process of spatial orientation has facilitated the development of the concept assuming that the pattern of space perception changes with growing professional experience. The concept is based on an active approach to the essence, emergence, formation and variation in the pattern of sensory perception of space in man's consciousness. This concept asserts that as pilot's professional expertise increases, the pattern of spatial orientation becomes geocentric because a new system of spatial perception evolves which is a result of the development of a new (instrumental) type of motor activity in space. This finds expression in the fact that perception of spatial position inflight occurs when man has to resolve a new motor task--movement along a complex trajectory in the three-dimensional space onboard a flying vehicle. The meaningful structure of this problem which is to be implemented through controlling movements of the pilot acts as a factor that forms this new system of perception. All this underlies the arrangement of meaningful collection of instrumental data and detection of noninstrumental signals in the comprehensive perception of changes in the spatial position of a flying vehicle.

  14. The relationship between language and spatial ability an analysis of spatial language for reconstructing the solving of spatial tasks

    CERN Document Server

    Mizzi, Angel

    2017-01-01

    This work investigates how different fifth-grade students solve spatial-verbal tasks and the role of language in this process. Based on a synthesis of theoretical foundations and methodological issues for supporting the relationship between spatial ability and language, this present study examines and classifies strategies used by students as well as the obstacles they encounter when solving spatial tasks in the reconstruction method. Contents Theoretical Framework Design and Implementation Results and Discussion from the Inductive Data Analyses Target Groups Scholars and students of mathematics education Teachers of mathematics in primary and secondary schools About the Author Angel Mizzi works as a research assistant and lecturer at the University of Duisburg-Essen, where he has successfully completed his PhD studies in mathematics education.

  15. Spatial resolution and maximum compensation factor of two-dimensional selective excitation pulses for MRI of objects containing conductive implants

    Directory of Open Access Journals (Sweden)

    Taeseong Woo

    2017-05-01

    Full Text Available A quantitative diagnosis using magnetic resonance imaging (MRI can be disturbed by radiofrequency (RF field inhomogeneity induced by the conductive implants. This inhomogeneity causes a local decrease of the signal intensity around the conductor, resulting in a deterioration of the accurate quantification. In a previous study, we developed an MRI imaging method using a two-dimensional selective excitation pulse (2D pulse to mitigate signal inhomogeneity induced by metallic implants. In this paper, the effect of 2D pulse was evaluated quantitatively by numerical simulation and MRI experiments. We introduced two factors for evaluation, spatial resolution and maximum compensation factor. Numerical simulations were performed with two groups. One group was composed of four models with different signal loss width, to evaluate the spatial resolution of the 2D pulse. The other group is also composed of four models with different amounts of signal loss for evaluating maximum compensation factor. In MRI experiments, we prepared phantoms containing conductors, which have different electrical conductivities related with the amounts of signal intensity decrease. The recovery of signal intensity was observed by 2D pulses, in both numerical simulations and experiments.

  16. Signal Adaptive System for Space/Spatial-Frequency Analysis

    Directory of Open Access Journals (Sweden)

    Veselin N. Ivanović

    2009-01-01

    Full Text Available This paper outlines the development of a multiple-clock-cycle implementation (MCI of a signal adaptive two-dimensional (2D system for space/spatial-frequency (S/SF signal analysis. The design is based on a method for improved S/SF representation of the analyzed 2D signals, also proposed here. The proposed MCI design optimizes critical design performances related to hardware complexity, making it a suitable system for real time implementation on an integrated chip. Additionally, the design allows the implemented system to take a variable number of clock cycles (CLKs (the only necessary ones regarding desirable—2D Wigner distribution-presentation of autoterms in different frequency-frequency points during the execution. This ability represents a major advantage of the proposed design which helps to optimize the time required for execution and produce an improved, cross-terms-free S/SF signal representation. The design has been verified by a field-programmable gate array (FPGA circuit design, capable of performing S/SF analysis of 2D signals in real time.

  17. Spatial-temporal cluster analysis of mortality from road traffic injuries using geographic information systems in West of Iran during 2009-2014.

    Science.gov (United States)

    Zangeneh, Alireza; Najafi, Farid; Karimi, Saeed; Saeidi, Shahram; Izadi, Neda

    2018-04-01

    Road traffic injuries (RTIs) are considered as one of the most important health problems endangering people's life. The examination of the geographical distribution of RTIs could help policymakers in better planning to reduce RTIs. This study, therefore, aimed to determine the spatial-temporal clustering of mortality from RTIs in West of Iran. Deaths from RTIs, registered in Forensic Medicine Organization of Kermanshah province over a period of six years (2009-2014), were used. Using negative binomial regression, the mortality trend was investigated. In order to investigate the spatial distribution of RTIs, we used ArcGIS. (Version 10.3). The median age of the 3231 people died in RTIs was 37 (IQR = 31) year, 78.4% were male. The 6-year average mortality rate from RTIs was 27.8/100,000 deaths, and the average rate had a declining trend. The dispersion of RTIs showed that most deaths occurred in Kermanshah, Islamabad, Bisotun, and Harsin road axes, respectively. The mean center of all deaths from RTIs occurred in Kermanshah province, the central area of Kermanshah district. The spatial trend of such deaths has moved to the northeast-southwest, and such deaths were geographically centralized. Results of Moran's I with respect to cluster analysis also indicated positive spatial autocorrelations. The results showed that the mortality rate from RTIs, despite the decline in recent years, is still high when compared with other countries. The clustering of accidents raises the concern that road infrastructure in certain locations may also be a factor. Regarding the results related to the temporal analysis, it is suggested that the enforcement of traffic rules be stricter at rush hours. Copyright © 2018 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  18. Radiative and magnetic properties of solar active regions. II. Spatially resolved analysis of O V 62.97 nm transition region emission

    Science.gov (United States)

    Fludra, A.; Warren, H.

    2010-11-01

    Context. Global relationships between the photospheric magnetic flux and the extreme ultraviolet emission integrated over active region area have been studied in a previous paper by Fludra & Ireland (2008, A&A, 483, 609). Spatially integrated EUV line intensities are tightly correlated with the total unsigned magnetic flux, and yet these global power laws have been shown to be insufficient for accurately determining the coronal heating mechanism owing to the mathematical ill-conditioning of the inverse problem. Aims: Our aim is to establish a relationship between the EUV line intensities and the photospheric magnetic flux density on small spatial scales in active regions and investigate whether it provides a way of identifying the process that heats the coronal loops. Methods: We compare spatially resolved EUV transition region emission and the photospheric magnetic flux density. This analysis is based on the O V 62.97 nm line recorded by the SOHO Coronal Diagnostic Spectrometer (CDS) and SOHO MDI magnetograms for six solar active regions. The magnetic flux density ϕ is converted to a simulated O V intensity using a model relationship I(ϕ, L) = Cϕδ Lλ, where the loop length L is obtained from potential magnetic field extrapolations. This simulated spatial distribution of O V intensities is convolved with the CDS instrument's point spread function and compared pixel by pixel with the observed O V line intensity. Parameters δ and λ are derived to give the best fit for the observed and simulated intensities. Results: Spatially-resolved analysis of the transition region emission reveals the complex nature of the heating processes in active regions. In some active regions, particularly large, local intensity enhancements up to a factor of five are present. When areas with O V intensities above 3000 erg cm-2 s-1 sr-1 are ignored, a power law has been fitted to the relationship between the local O V line intensity and the photospheric magnetic flux density in each

  19. Collective behavior in the spatial spreading of obesity

    Science.gov (United States)

    Gallos, Lazaros K.; Barttfeld, Pablo; Havlin, Shlomo; Sigman, Mariano; Makse, Hernán A.

    2012-06-01

    Obesity prevalence is increasing in many countries at alarming levels. A difficulty in the conception of policies to reverse these trends is the identification of the drivers behind the obesity epidemics. Here, we implement a spatial spreading analysis to investigate whether obesity shows spatial correlations, revealing the effect of collective and global factors acting above individual choices. We find a regularity in the spatial fluctuations of their prevalence revealed by a pattern of scale-free long-range correlations. The fluctuations are anomalous, deviating in a fundamental way from the weaker correlations found in the underlying population distribution indicating the presence of collective behavior, i.e., individual habits may have negligible influence in shaping the patterns of spreading. Interestingly, we find the same scale-free correlations in economic activities associated with food production. These results motivate future interventions to investigate the causality of this relation providing guidance for the implementation of preventive health policies.

  20. Prospects for higher spatial resolution quantitative X-ray analysis using transition element L-lines

    Science.gov (United States)

    Statham, P.; Holland, J.

    2014-03-01

    Lowering electron beam kV reduces electron scattering and improves spatial resolution of X-ray analysis. However, a previous round robin analysis of steels at 5 - 6 kV using Lα-lines for the first row transition elements gave poor accuracies. Our experiments on SS63 steel using Lα-lines show similar biases in Cr and Ni that cannot be corrected with changes to self-absorption coefficients or carbon coating. The inaccuracy may be caused by different probabilities for emission and anomalous self-absorption for the La-line between specimen and pure element standard. Analysis using Ll(L3-M1)-lines gives more accurate results for SS63 plausibly because the M1-shell is not so vulnerable to the atomic environment as the unfilled M4,5-shell. However, Ll-intensities are very weak and WDS analysis may be impractical for some applications. EDS with large area SDD offers orders of magnitude faster analysis and achieves similar results to WDS analysis with Lα-lines but poorer energy resolution precludes the use of Ll-lines in most situations. EDS analysis of K-lines at low overvoltage is an alternative strategy for improving spatial resolution that could give higher accuracy. The trade-off between low kV versus low overvoltage is explored in terms of sensitivity for element detection for different elements.