Spatial dependencies of wind power and interrelations with spot price dynamics

Energy Technology Data Exchange (ETDEWEB)

Elberg, Christina; Hagspiel, Simeon

2013-06-15

Wind power has seen a strong growth over the last decade. Due to its high intermittency, spot prices have become more volatile and exhibit correlated behavior with wind power fed into the system. In this paper, we develop a stochastic simulation model that incorporates the spatial dependencies of wind power and its interrelations with spot prices: We employ a structural supply and demand based model for the electricity spot price that takes into account stochastic production quantities of wind power. Spatial dependencies are modeled with the help of copulas, thus linking the single turbine wind power to the aggregated wind power in a market. The model is applied to the German electricity market where wind power already today makes up a significant share of total power production. Revenue distributions and the market value of different wind power plants are analyzed. We find that the specific location of the considered wind turbine, i.e. its spatial dependency with respect to the aggregated wind power in the system, is of high relevance for its market value. Many of the analyzed locations show an upper tail dependence that adversely impacts the market value. This effect becomes more important for increasing levels of wind power penetration.

Spatial dependencies of wind power and interrelations with spot price dynamics

International Nuclear Information System (INIS)

Elberg, Christina; Hagspiel, Simeon

2013-01-01

Wind power has seen a strong growth over the last decade. Due to its high intermittency, spot prices have become more volatile and exhibit correlated behavior with wind power fed into the system. In this paper, we develop a stochastic simulation model that incorporates the spatial dependencies of wind power and its interrelations with spot prices: We employ a structural supply and demand based model for the electricity spot price that takes into account stochastic production quantities of wind power. Spatial dependencies are modeled with the help of copulas, thus linking the single turbine wind power to the aggregated wind power in a market. The model is applied to the German electricity market where wind power already today makes up a significant share of total power production. Revenue distributions and the market value of different wind power plants are analyzed. We find that the specific location of the considered wind turbine, i.e. its spatial dependency with respect to the aggregated wind power in the system, is of high relevance for its market value. Many of the analyzed locations show an upper tail dependence that adversely impacts the market value. This effect becomes more important for increasing levels of wind power penetration.

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)

Continuous Spatial Process Models for Spatial Extreme Values

KAUST Repository

Sang, Huiyan

2010-01-28

We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e. g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model. © 2009 International Biometric Society.

Multivariate Receptor Models for Spatially Correlated Multipollutant Data

KAUST Repository

Jun, Mikyoung

2013-08-01

The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.

Effect of Spatial-Dependent Utility on Social Group Domination

Science.gov (United States)

Rodriguez, Nathaniel; Meyertholen, Andrew

2012-02-01

The mathematical modeling of social group competition has garnered much attention. We consider a model originated by Abrams and Strogatz [Nature 424, 900 (2003)] that predicts the extinction of one of two social groups. This model assigns a utility to each social group, which is constant over the entire society. We find by allowing this utility to vary over a society, through the introduction of a network or spatial dependence, this model may result in the coexistence of the two social groups.

SPATIAL MODELLING FOR DESCRIBING SPATIAL VARIABILITY OF SOIL PHYSICAL PROPERTIES IN EASTERN CROATIA

Directory of Open Access Journals (Sweden)

Igor Bogunović

2016-06-01

Full Text Available The objectives of this study were to characterize the field-scale spatial variability and test several interpolation methods to identify the best spatial predictor of penetration resistance (PR, bulk density (BD and gravimetric water content (GWC in the silty loam soil in Eastern Croatia. The measurements were made on a 25 x 25-m grid which created 40 individual grid cells. Soil properties were measured at the center of the grid cell deep 0-10 cm and 10-20 cm. Results demonstrated that PR and GWC displayed strong spatial dependence at 0-10 cm BD, while there was moderate and weak spatial dependence of PR, BD and GWC at depth of 10-20 cm. Semi-variogram analysis suggests that future sampling intervals for investigated parameters can be increased to 35 m in order to reduce research costs. Additionally, interpolation models recorded similar root mean square values with high predictive accuracy. Results suggest that investigated properties do not have uniform interpolation method implying the need for spatial modelling in the evaluation of these soil properties in Eastern Croatia.

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.

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.

Spatial modeling of households' knowledge about arsenic pollution in Bangladesh.

Science.gov (United States)

Sarker, M Mizanur Rahman

2012-04-01

Arsenic in drinking water is an important public health issue in Bangladesh, which is affected by households' knowledge about arsenic threats from their drinking water. In this study, spatial statistical models were used to investigate the determinants and spatial dependence of households' knowledge about arsenic risk. The binary join matrix/binary contiguity matrix and inverse distance spatial weight matrix techniques are used to capture spatial dependence in the data. This analysis extends the spatial model by allowing spatial dependence to vary across divisions and regions. A positive spatial correlation was found in households' knowledge across neighboring districts at district, divisional and regional levels, but the strength of this spatial correlation varies considerably by spatial weight. Literacy rate, daily wage rate of agricultural labor, arsenic status, and percentage of red mark tube well usage in districts were found to contribute positively and significantly to households' knowledge. These findings have policy implications both at regional and national levels in mitigating the present arsenic crisis and to ensure arsenic-free water in Bangladesh. Copyright © 2012 Elsevier Ltd. All rights reserved.

The Effect Of Omitted Spatial Effects And Social Dependence In The Modelling Of Household Expenditure For Fruits And Vegetables

Directory of Open Access Journals (Sweden)

Łaszkiewicz Edyta

2014-12-01

Full Text Available As is well known, ignoring spatial heterogeneity leads to biased parameter estimates, while omitting the spatial lag of a dependent variable results in biasness and inconsistency (Anselin, 1988. However, the common approach to analysing households’ expenditures is to ignore the potential spatial effects and social dependence. In light of this, the aim of this paper is to examine the consequences of omitting the spatial effects as well as social dependence in households’ expenditures.

Bayesian spatial modeling of HIV mortality via zero-inflated Poisson models.

Science.gov (United States)

Musal, Muzaffer; Aktekin, Tevfik

2013-01-30

In this paper, we investigate the effects of poverty and inequality on the number of HIV-related deaths in 62 New York counties via Bayesian zero-inflated Poisson models that exhibit spatial dependence. We quantify inequality via the Theil index and poverty via the ratios of two Census 2000 variables, the number of people under the poverty line and the number of people for whom poverty status is determined, in each Zip Code Tabulation Area. The purpose of this study was to investigate the effects of inequality and poverty in addition to spatial dependence between neighboring regions on HIV mortality rate, which can lead to improved health resource allocation decisions. In modeling county-specific HIV counts, we propose Bayesian zero-inflated Poisson models whose rates are functions of both covariate and spatial/random effects. To show how the proposed models work, we used three different publicly available data sets: TIGER Shapefiles, Census 2000, and mortality index files. In addition, we introduce parameter estimation issues of Bayesian zero-inflated Poisson models and discuss MCMC method implications. Copyright © 2012 John Wiley & Sons, Ltd.

Statistical inference and visualization in scale-space for spatially dependent images

KAUST Repository

Vaughan, Amy

2012-03-01

SiZer (SIgnificant ZERo crossing of the derivatives) is a graphical scale-space visualization tool that allows for statistical inferences. In this paper we develop a spatial SiZer for finding significant features and conducting goodness-of-fit tests for spatially dependent images. The spatial SiZer utilizes a family of kernel estimates of the image and provides not only exploratory data analysis but also statistical inference with spatial correlation taken into account. It is also capable of comparing the observed image with a specific null model being tested by adjusting the statistical inference using an assumed covariance structure. Pixel locations having statistically significant differences between the image and a given null model are highlighted by arrows. The spatial SiZer is compared with the existing independent SiZer via the analysis of simulated data with and without signal on both planar and spherical domains. We apply the spatial SiZer method to the decadal temperature change over some regions of the Earth. © 2011 The Korean Statistical Society.

How to get rid of W: a latent variables approach to modelling spatially lagged variables

NARCIS (Netherlands)

Folmer, H.; Oud, J.

2008-01-01

In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are

How to get rid of W : a latent variables approach to modelling spatially lagged variables

NARCIS (Netherlands)

Folmer, Henk; Oud, Johan

2008-01-01

In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are

Towards a taxonomy of spatial scale-dependence

DEFF Research Database (Denmark)

Sandel, Brody Steven

2015-01-01

Spatial scale-dependence is a ubiquitous feature of ecological systems. This presents a challenge for ecologists who seek to discern general principles. A solution is to search for generalities in patterns of scale-dependence – that is, what kinds of things are scale-dependent, in what ways, and ...

A random spatial network model based on elementary postulates

Science.gov (United States)

Karlinger, Michael R.; Troutman, Brent M.

1989-01-01

A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

Spatial dependencies between large-scale brain networks.

Directory of Open Access Journals (Sweden)

Robert Leech

Full Text Available Functional neuroimaging reveals both increases (task-positive and decreases (task-negative in neural activation with many tasks. Many studies show a temporal relationship between task positive and task negative networks that is important for efficient cognitive functioning. Here we provide evidence for a spatial relationship between task positive and negative networks. There are strong spatial similarities between many reported task negative brain networks, termed the default mode network, which is typically assumed to be a spatially fixed network. However, this is not the case. The spatial structure of the DMN varies depending on what specific task is being performed. We test whether there is a fundamental spatial relationship between task positive and negative networks. Specifically, we hypothesize that the distance between task positive and negative voxels is consistent despite different spatial patterns of activation and deactivation evoked by different cognitive tasks. We show significantly reduced variability in the distance between within-condition task positive and task negative voxels than across-condition distances for four different sensory, motor and cognitive tasks--implying that deactivation patterns are spatially dependent on activation patterns (and vice versa, and that both are modulated by specific task demands. We also show a similar relationship between positively and negatively correlated networks from a third 'rest' dataset, in the absence of a specific task. We propose that this spatial relationship may be the macroscopic analogue of microscopic neuronal organization reported in sensory cortical systems, and that this organization may reflect homeostatic plasticity necessary for efficient brain function.

A simplified spatial model for BWR stability

International Nuclear Information System (INIS)

Berman, Y.; Lederer, Y.; Meron, E.

2012-01-01

A spatial reduced order model for the study of BWR stability, based on the phenomenological model of March-Leuba et al., is presented. As one dimensional spatial dependence of the neutron flux, fuel temperature and void fraction is introduced, it is possible to describe both global and regional oscillations of the reactor power. Both linear stability analysis and numerical analysis were applied in order to describe the parameters which govern the model stability. The results were found qualitatively similar to past results. Doppler reactivity feedback was found essential for the explanation of the different regions of the flow-power stability map. (authors)

Hippocampal-dependent spatial memory in the water maze is preserved in an experimental model of temporal lobe epilepsy in rats.

Directory of Open Access Journals (Sweden)

Marion Inostroza

Full Text Available Cognitive impairment is a major concern in temporal lobe epilepsy (TLE. While different experimental models have been used to characterize TLE-related cognitive deficits, little is known on whether a particular deficit is more associated with the underlying brain injuries than with the epileptic condition per se. Here, we look at the relationship between the pattern of brain damage and spatial memory deficits in two chronic models of TLE (lithium-pilocarpine, LIP and kainic acid, KA from two different rat strains (Wistar and Sprague-Dawley using the Morris water maze and the elevated plus maze in combination with MRI imaging and post-morten neuronal immunostaining. We found fundamental differences between LIP- and KA-treated epileptic rats regarding spatial memory deficits and anxiety. LIP-treated animals from both strains showed significant impairment in the acquisition and retention of spatial memory, and were unable to learn a cued version of the task. In contrast, KA-treated rats were differently affected. Sprague-Dawley KA-treated rats learned less efficiently than Wistar KA-treated animals, which performed similar to control rats in the acquisition and in a probe trial testing for spatial memory. Different anxiety levels and the extension of brain lesions affecting the hippocampus and the amydgala concur with spatial memory deficits observed in epileptic rats. Hence, our results suggest that hippocampal-dependent spatial memory is not necessarily affected in TLE and that comorbidity between spatial deficits and anxiety is more related with the underlying brain lesions than with the epileptic condition per se.

Stability of a spatial model of social interactions

International Nuclear Information System (INIS)

Bragard, Jean; Mossay, Pascal

2016-01-01

We study a spatial model of social interactions. Though the properties of the spatial equilibrium have been largely discussed in the existing literature, the stability of equilibrium remains an unaddressed issue. Our aim is to fill up this gap by introducing dynamics in the model and by determining the stability of equilibrium. First we derive a variational equation useful for the stability analysis. This allows to study the corresponding eigenvalue problem. While odd modes are shown to be always stable, there is a single even mode of which stability depends on the model parameters. Finally various numerical simulations illustrate our theoretical results.

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.

Scale-dependent approaches to modeling spatial epidemiology of chronic wasting disease.

Science.gov (United States)

Conner, Mary M.; Gross, John E.; Cross, Paul C.; Ebinger, Michael R.; Gillies, Robert; Samuel, Michael D.; Miller, Michael W.

2007-01-01

This e-book is the product of a second workshop that was funded and promoted by the United States Geological Survey to enhance cooperation between states for the management of chronic wasting disease (CWD). The first workshop addressed issues surrounding the statistical design and collection of surveillance data for CWD. The second workshop, from which this document arose, followed logically from the first workshop and focused on appropriate methods for analysis, interpretation, and use of CWD surveillance and related epidemiology data. Consequently, the emphasis of this e-book is on modeling approaches to describe and gain insight of the spatial epidemiology of CWD. We designed this e-book for wildlife managers and biologists who are responsible for the surveillance of CWD in their state or agency. We chose spatial methods that are popular or common in the spatial epidemiology literature and evaluated them for their relevance to modeling CWD. Our opinion of the usefulness and relevance of each method was based on the type of field data commonly collected as part of CWD surveillance programs and what we know about CWD biology, ecology, and epidemiology. Specifically, we expected the field data to consist primarily of the infection status of a harvested or culled sample along with its date of collection (not date of infection), location, and demographic status. We evaluated methods in light of the fact that CWD does not appear to spread rapidly through wild populations, relative to more highly contagious viruses, and can be spread directly from animal to animal or indirectly through environmental contamination.

Spatial cluster modelling

CERN Document Server

Lawson, Andrew B

2002-01-01

Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

Spatial-frequency dependent binocular imbalance in amblyopia.

Science.gov (United States)

Kwon, MiYoung; Wiecek, Emily; Dakin, Steven C; Bex, Peter J

2015-11-25

While amblyopia involves both binocular imbalance and deficits in processing high spatial frequency information, little is known about the spatial-frequency dependence of binocular imbalance. Here we examined binocular imbalance as a function of spatial frequency in amblyopia using a novel computer-based method. Binocular imbalance at four spatial frequencies was measured with a novel dichoptic letter chart in individuals with amblyopia, or normal vision. Our dichoptic letter chart was composed of band-pass filtered letters arranged in a layout similar to the ETDRS acuity chart. A different chart was presented to each eye of the observer via stereo-shutter glasses. The relative contrast of the corresponding letter in each eye was adjusted by a computer staircase to determine a binocular Balance Point at which the observer reports the letter presented to either eye with equal probability. Amblyopes showed pronounced binocular imbalance across all spatial frequencies, with greater imbalance at high compared to low spatial frequencies (an average increase of 19%, p imbalance may be useful for diagnosing amblyopia and as an outcome measure for recovery of binocular vision following therapy.

Factor copula models for data with spatio-temporal dependence

KAUST Repository

Krupskii, Pavel

2017-10-13

We propose a new copula model for spatial data that are observed repeatedly in time. The model is based on the assumption that there exists a common factor that affects the measurements of a process in space and in time. Unlike models based on multivariate normality, our model can handle data with tail dependence and asymmetry. The likelihood for the proposed model can be obtained in a simple form and therefore parameter estimation is quite fast. Simulation from this model is straightforward and data can be predicted at any spatial location and time point. We use simulation studies to show different types of dependencies, both in space and in time, that can be generated by this model. We apply the proposed copula model to hourly wind data and compare its performance with some classical models for spatio-temporal data.

Factor copula models for data with spatio-temporal dependence

KAUST Repository

Krupskii, Pavel; Genton, Marc G.

2017-01-01

We propose a new copula model for spatial data that are observed repeatedly in time. The model is based on the assumption that there exists a common factor that affects the measurements of a process in space and in time. Unlike models based on multivariate normality, our model can handle data with tail dependence and asymmetry. The likelihood for the proposed model can be obtained in a simple form and therefore parameter estimation is quite fast. Simulation from this model is straightforward and data can be predicted at any spatial location and time point. We use simulation studies to show different types of dependencies, both in space and in time, that can be generated by this model. We apply the proposed copula model to hourly wind data and compare its performance with some classical models for spatio-temporal data.

Extinction threshold of a population in spatial and stochastic model

OpenAIRE

Soroka, Yevheniia; Rublyov, Bogdan

2016-01-01

In this study, spatial stochastic and logistic model (SSLM) describing dynamics of a population of a certain species was analysed. The behaviour of the extinction threshold as a function of model parameters was studied. More specifically, we studied how the critical values for the model parameters that separate the cases of extinction and persistence depend on the spatial scales of the competition and dispersal kernels. We compared the simulations and analytical results to examine if and how ...

Spatial dependence and correlation of rainfall in the Danube catchment and its role in flood risk assessment.

Science.gov (United States)

Martina, M. L. V.; Vitolo, R.; Todini, E.; Stephenson, D. B.; Cook, I. M.

2009-04-01

The possibility that multiple catastrophic events occur within a given timespan and affect the same portfolio of insured properties may induce enhanced risk. For this reason, in the insurance industry it is of interest to characterise not only the point probability of catastrophic events, but also their spatial structure. As far as floods are concerned it is important to determine the probability of having multiple simultaneous events in different parts of the same basin: in this case, indeed, the loss in a portfolio can be significantly different. Understanding the spatial structure of the precipitation field is a necessary step for the proper modelling of the spatial dependence and correlation of river discharge. Several stochastic models are available in the scientific literature for the multi-site generation of precipitation. Although most models achieve good performance in modelling mean values, temporal variability and inter-site dependence of extremes are still delicate issues. In this work we aim at identifying the main spatial characteristics of the precipitation structure and then at analysing them in a real case. We consider data from a large network of raingauges in the Danube catchment. This catchment is a good example of a large-scale catchment where the spatial correlation of flood events can radically change the effect in term of flood damage.

CaMKII-dependent dendrite ramification and spine generation promote spatial training-induced memory improvement in a rat model of sporadic Alzheimer's disease.

Science.gov (United States)

Jiang, Xia; Chai, Gao-Shang; Wang, Zhi-Hao; Hu, Yu; Li, Xiao-Guang; Ma, Zhi-Wei; Wang, Qun; Wang, Jian-Zhi; Liu, Gong-Ping

2015-02-01

Participation in cognitively stimulating activities can preserve memory capacities in patients with Alzheimer's disease (AD), but the mechanism is not fully understood. Here, we used a rat model with hyperhomocysteinemia, an independent risk factor of AD, to study whether spatial training could remodel the synaptic and/or dendritic plasticity and the key molecular target(s) involved. We found that spatial training in water maze remarkably improved the subsequent short-term and long-term memory performance in contextual fear conditioning and Barnes maze. The trained rats showed an enhanced dendrite ramification, spine generation and plasticity in dentate gyrus (DG) neurons, and stimulation of long-term potentiation between perforant path and DG circuit. Spatial training also increased the levels of postsynaptic GluA1, GluN2A, GluN2B, and PSD93 with selective activation of calcium/calmodulin-dependent protein kinase II (CaMKII), although inhibition of CaMKII by stereotaxic injection of KN93 into hippocampal DG, abolished the training-induced cognitive improvement, dendrite ramification, and spine generation. We conclude that spatial training can preserve the cognitive function by CaMKII-dependent remodeling of dendritic plasticity in hyperhomocysteinemia-induced sporadic AD-like rats. Copyright © 2015 Elsevier Inc. All rights reserved.

Tapered composite likelihood for spatial max-stable models

KAUST Repository

Sang, Huiyan

2014-05-01

Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.

Tapered composite likelihood for spatial max-stable models

KAUST Repository

Sang, Huiyan; Genton, Marc G.

2014-01-01

Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.

Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models

Directory of Open Access Journals (Sweden)

Kostas Alexandridis

2013-06-01

Full Text Available Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of 1963–1990. The artificial neural network-based Land Transformation Model (LTM predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process.

Neutrino flavor instabilities in a time-dependent supernova model

Energy Technology Data Exchange (ETDEWEB)

Abbar, Sajad; Duan, Huaiyu, E-mail: duan@unm.edu

2015-12-17

A dense neutrino medium such as that inside a core-collapse supernova can experience collective flavor conversion or oscillations because of the neutral-current weak interaction among the neutrinos. This phenomenon has been studied in a restricted, stationary supernova model which possesses the (spatial) spherical symmetry about the center of the supernova and the (directional) axial symmetry around the radial direction. Recently it has been shown that these spatial and directional symmetries can be broken spontaneously by collective neutrino oscillations. In this letter we analyze the neutrino flavor instabilities in a time-dependent supernova model. Our results show that collective neutrino oscillations start at approximately the same radius in both the stationary and time-dependent supernova models unless there exist very rapid variations in local physical conditions on timescales of a few microseconds or shorter. Our results also suggest that collective neutrino oscillations can vary rapidly with time in the regimes where they do occur which need to be studied in time-dependent supernova models.

Spatial modeling on the upperstream of the Citarum watershed: An application of geoinformatics

Science.gov (United States)

Ningrum, Windy Setia; Widyaningsih, Yekti; Indra, Tito Latif

2017-03-01

The Citarum watershed is the longest and the largest watershed in West Java, Indonesia, located at 106°51'36''-107°51' E and 7°19'-6°24'S across 10 districts, and serves as the water supply for over 15 million people. In this area, the water criticality index is concerned to reach the balance between water supply and water demand, so that in the dry season, the watershed is still able to meet the water needs of the society along the Citarum river. The objective of this research is to evaluate the water criticality index of Citarum watershed area using spatial model to overcome the spatial dependencies in the data. The result of Lagrange multiplier diagnostics for spatial dependence results are LM-err = 34.6 (p-value = 4.1e-09) and LM-lag = 8.05 (p-value = 0.005), then modeling using Spatial Lag Model (SLM) and Spatial Error Model (SEM) were conducted. The likelihood ratio test show that both of SLM dan SEM model is better than OLS model in modeling water criticality index in Citarum watershed. The AIC value of SLM and SEM model are 78.9 and 51.4, then the SEM model is better than SLM model in predicting water criticality index in Citarum watershed.

Spatial Impairment and Memory in Genetic Disorders: Insights from Mouse Models

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Sang Ah Lee

2017-02-01

Full Text Available Research across the cognitive and brain sciences has begun to elucidate some of the processes that guide navigation and spatial memory. Boundary geometry and featural landmarks are two distinct classes of environmental cues that have dissociable neural correlates in spatial representation and follow different patterns of learning. Consequently, spatial navigation depends both on the type of cue available and on the type of learning provided. We investigated this interaction between spatial representation and memory by administering two different tasks (working memory, reference memory using two different environmental cues (rectangular geometry, striped landmark in mouse models of human genetic disorders: Prader-Willi syndrome (PWScrm+/p− mice, n = 12 and Beta-catenin mutation (Thr653Lys-substituted mice, n = 12. This exploratory study provides suggestive evidence that these models exhibit different abilities and impairments in navigating by boundary geometry and featural landmarks, depending on the type of memory task administered. We discuss these data in light of the specific deficits in cognitive and brain function in these human syndromes and their animal model counterparts.

Application of Spatial Regression Models to Income Poverty Ratios in Middle Delta Contiguous Counties in Egypt

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Sohair F Higazi

2013-02-01

Full Text Available Regression analysis depends on several assumptions that have to be satisfied. A major assumption that is never satisfied when variables are from contiguous observations is the independence of error terms. Spatial analysis treated the violation of that assumption by two derived models that put contiguity of observations into consideration. Data used are from Egypt's 2006 latest census, for 93 counties in middle delta seven adjacent Governorates. The dependent variable used is the percent of individuals classified as poor (those who make less than 1$ daily. Predictors are some demographic indicators. Explanatory Spatial Data Analysis (ESDA is performed to examine the existence of spatial clustering and spatial autocorrelation between neighboring counties. The ESDA revealed spatial clusters and spatial correlation between locations. Three statistical models are applied to the data, the Ordinary Least Square regression model (OLS, the Spatial Error Model (SEM and the Spatial Lag Model (SLM.The Likelihood Ratio test and some information criterions are used to compare SLM and SEM to OLS. The SEM model proved to be better than the SLM model. Recommendations are drawn regarding the two spatial models used.

The effects of spatial autoregressive dependencies on inference in ordinary least squares: a geometric approach

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Smith, Tony E.; Lee, Ka Lok

2012-01-01

There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce "spurious correlation" that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem.

Bridging asymptotic independence and dependence in spatial exbtremes using Gaussian scale mixtures

KAUST Repository

Huser, Raphaël

2017-06-23

Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect dependence. In this paper, we study the extremal dependence properties of Gaussian scale mixtures and we unify and extend general results on their joint tail decay rates in both asymptotic dependence and independence cases. Motivated by the analysis of spatial extremes, we propose flexible yet parsimonious parametric copula models that smoothly interpolate from asymptotic dependence to independence and include the Gaussian dependence as a special case. We show how these new models can be fitted to high threshold exceedances using a censored likelihood approach, and we demonstrate that they provide valuable information about tail characteristics. In particular, by borrowing strength across locations, our parametric model-based approach can also be used to provide evidence for or against either asymptotic dependence class, hence complementing information given at an exploratory stage by the widely used nonparametric or parametric estimates of the χ and χ̄ coefficients. We demonstrate the capacity of our methodology by adequately capturing the extremal properties of wind speed data collected in the Pacific Northwest, US.

Bridging asymptotic independence and dependence in spatial exbtremes using Gaussian scale mixtures

KAUST Repository

Huser, Raphaë l; Opitz, Thomas; Thibaud, Emeric

2017-01-01

Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect dependence. In this paper, we study the extremal dependence properties of Gaussian scale mixtures and we unify and extend general results on their joint tail decay rates in both asymptotic dependence and independence cases. Motivated by the analysis of spatial extremes, we propose flexible yet parsimonious parametric copula models that smoothly interpolate from asymptotic dependence to independence and include the Gaussian dependence as a special case. We show how these new models can be fitted to high threshold exceedances using a censored likelihood approach, and we demonstrate that they provide valuable information about tail characteristics. In particular, by borrowing strength across locations, our parametric model-based approach can also be used to provide evidence for or against either asymptotic dependence class, hence complementing information given at an exploratory stage by the widely used nonparametric or parametric estimates of the χ and χ̄ coefficients. We demonstrate the capacity of our methodology by adequately capturing the extremal properties of wind speed data collected in the Pacific Northwest, US.

Determination of spatially dependent diffusion parameters in bovine bone using Kalman filter.

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Shokry, Abdallah; Ståhle, Per; Svensson, Ingrid

2015-11-07

Although many studies have been made for homogenous constant diffusion, bone is an inhomogeneous material. It has been suggested that bone porosity decreases from the inner boundaries to the outer boundaries of the long bones. The diffusivity of substances in the bone matrix is believed to increase as the bone porosity increases. In this study, an experimental set up is used where bovine bone samples, saturated with potassium chloride (KCl), were put into distilled water and the conductivity of the water was followed. Chloride ions in the bone samples escaped out in the water through diffusion and the increase of the conductivity was measured. A one-dimensional, spatially dependent mathematical model describing the diffusion process is used. The diffusion parameters in the model are determined using a Kalman filter technique. The parameters for spatially dependent at endosteal and periosteal surfaces are found to be (12.8 ± 4.7) × 10(-11) and (5 ± 3.5) × 10(-11)m(2)/s respectively. The mathematical model function using the obtained diffusion parameters fits very well with the experimental data with mean square error varies from 0.06 × 10(-6) to 0.183 × 10(-6) (μS/m)(2). Copyright © 2015 Elsevier Ltd. All rights reserved.

A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models.

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Whittington, Jesse; Sawaya, Michael A

2015-01-01

Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071) for females, 0.844 (0.703-0.975) for males, and 0.882 (0.779-0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024) for females, 0.825 (0.700-0.948) for males, and 0.863 (0.771-0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth

A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models.

Directory of Open Access Journals (Sweden)

Jesse Whittington

Full Text Available Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071 for females, 0.844 (0.703-0.975 for males, and 0.882 (0.779-0.981 for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024 for females, 0.825 (0.700-0.948 for males, and 0.863 (0.771-0.957 for both sexes. The combination of low densities, low reproductive rates, and predominantly negative

Flexible hydrological modeling - Disaggregation from lumped catchment scale to higher spatial resolutions

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Tran, Quoc Quan; Willems, Patrick; Pannemans, Bart; Blanckaert, Joris; Pereira, Fernando; Nossent, Jiri; Cauwenberghs, Kris; Vansteenkiste, Thomas

2015-04-01

Based on an international literature review on model structures of existing rainfall-runoff and hydrological models, a generalized model structure is proposed. It consists of different types of meteorological components, storage components, splitting components and routing components. They can be spatially organized in a lumped way, or on a grid, spatially interlinked by source-to-sink or grid-to-grid (cell-to-cell) routing. The grid size of the model can be chosen depending on the application. The user can select/change the spatial resolution depending on the needs and/or the evaluation of the accuracy of the model results, or use different spatial resolutions in parallel for different applications. Major research questions addressed during the study are: How can we assure consistent results of the model at any spatial detail? How can we avoid strong or sudden changes in model parameters and corresponding simulation results, when one moves from one level of spatial detail to another? How can we limit the problem of overparameterization/equifinality when we move from the lumped model to the spatially distributed model? The proposed approach is a step-wise one, where first the lumped conceptual model is calibrated using a systematic, data-based approach, followed by a disaggregation step where the lumped parameters are disaggregated based on spatial catchment characteristics (topography, land use, soil characteristics). In this way, disaggregation can be done down to any spatial scale, and consistently among scales. Only few additional calibration parameters are introduced to scale the absolute spatial differences in model parameters, but keeping the relative differences as obtained from the spatial catchment characteristics. After calibration of the spatial model, the accuracies of the lumped and spatial models were compared for peak, low and cumulative runoff total and sub-flows (at downstream and internal gauging stations). For the distributed models, additional

Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

NARCIS (Netherlands)

Hoffmann, H.; Zhao, G.; Bussel, van L.G.J.

2015-01-01

Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield

Neutrino flavor instabilities in a time-dependent supernova model

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Sajad Abbar

2015-12-01

Full Text Available A dense neutrino medium such as that inside a core-collapse supernova can experience collective flavor conversion or oscillations because of the neutral-current weak interaction among the neutrinos. This phenomenon has been studied in a restricted, stationary supernova model which possesses the (spatial spherical symmetry about the center of the supernova and the (directional axial symmetry around the radial direction. Recently it has been shown that these spatial and directional symmetries can be broken spontaneously by collective neutrino oscillations. In this letter we analyze the neutrino flavor instabilities in a time-dependent supernova model. Our results show that collective neutrino oscillations start at approximately the same radius in both the stationary and time-dependent supernova models unless there exist very rapid variations in local physical conditions on timescales of a few microseconds or shorter. Our results also suggest that collective neutrino oscillations can vary rapidly with time in the regimes where they do occur which need to be studied in time-dependent supernova models.

Modeling structural change in spatial system dynamics: A Daisyworld example.

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Neuwirth, C; Peck, A; Simonović, S P

2015-03-01

System dynamics (SD) is an effective approach for helping reveal the temporal behavior of complex systems. Although there have been recent developments in expanding SD to include systems' spatial dependencies, most applications have been restricted to the simulation of diffusion processes; this is especially true for models on structural change (e.g. LULC modeling). To address this shortcoming, a Python program is proposed to tightly couple SD software to a Geographic Information System (GIS). The approach provides the required capacities for handling bidirectional and synchronized interactions of operations between SD and GIS. In order to illustrate the concept and the techniques proposed for simulating structural changes, a fictitious environment called Daisyworld has been recreated in a spatial system dynamics (SSD) environment. The comparison of spatial and non-spatial simulations emphasizes the importance of considering spatio-temporal feedbacks. Finally, practical applications of structural change models in agriculture and disaster management are proposed.

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

Spatially explicit modeling in ecology: A review

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DeAngelis, Donald L.; Yurek, Simeon

2017-01-01

The use of spatially explicit models (SEMs) in ecology has grown enormously in the past two decades. One major advancement has been that fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology. Many areas of modeling have shifted toward a focus on capturing these fine-scale details, to improve mechanistic understanding of ecosystems. However, spatially implicit models (SIMs) have played a dominant role in ecology, and arguments have been made that SIMs, which account for the effects of space without specifying spatial positions, have an advantage of being simpler and more broadly applicable, perhaps contributing more to understanding. We address this debate by comparing SEMs and SIMs in examples from the past few decades of modeling research. We argue that, although SIMs have been the dominant approach in the incorporation of space in theoretical ecology, SEMs have unique advantages for addressing pragmatic questions concerning species populations or communities in specific places, because local conditions, such as spatial heterogeneities, organism behaviors, and other contingencies, produce dynamics and patterns that usually cannot be incorporated into simpler SIMs. SEMs are also able to describe mechanisms at the local scale that can create amplifying positive feedbacks at that scale, creating emergent patterns at larger scales, and therefore are important to basic ecological theory. We review the use of SEMs at the level of populations, interacting populations, food webs, and ecosystems and argue that SEMs are not only essential in pragmatic issues, but must play a role in the understanding of causal relationships on landscapes.

Impaired spatial processing in a mouse model of fragile X syndrome.

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Ghilan, Mohamed; Bettio, Luis E B; Noonan, Athena; Brocardo, Patricia S; Gil-Mohapel, Joana; Christie, Brian R

2018-05-17

Fragile X syndrome (FXS) is the most common form of inherited intellectual impairment. The Fmr1 -/y mouse model has been previously shown to have deficits in context discrimination tasks but not in the elevated plus-maze. To further characterize this FXS mouse model and determine whether hippocampal-mediated behaviours are affected in these mice, dentate gyrus (DG)-dependent spatial processing and Cornu ammonis 1 (CA1)-dependent temporal order discrimination tasks were evaluated. In agreement with previous findings of long-term potentiation deficits in the DG of this transgenic model of FXS, the results reported here demonstrate that Fmr1 -/y mice perform poorly in the DG-dependent metric change spatial processing task. However, Fmr1 -/y mice did not present deficits in the CA1-dependent temporal order discrimination task, and were able to remember the order in which objects were presented to them to the same extent as their wild-type littermate controls. These data suggest that the previously reported subregional-specific differences in hippocampal synaptic plasticity observed in the Fmr1 -/y mouse model may manifest as selective behavioural deficits in hippocampal-dependent tasks. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

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Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

A Non-Gaussian Spatial Generalized Linear Latent Variable Model

KAUST Repository

Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.

2012-01-01

We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

A Non-Gaussian Spatial Generalized Linear Latent Variable Model

KAUST Repository

Irincheeva, Irina

2012-08-03

We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

Convergence Hypothesis: Evidence from Panel Unit Root Test with Spatial Dependence

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Lezheng Liu

2006-10-01

Full Text Available In this paper we test the convergence hypothesis by using a revised 4- step procedure of panel unit root test suggested by Evans and Karras (1996. We use data on output for 24 OECD countries over 40 years long. Whether the convergence, if any, is conditional or absolute is also examined. According to a proposition by Baltagi, Bresson, and Pirotte (2005, we incorporate spatial autoregressive error into a fixedeffect panel model to account for not only the heterogeneous panel structure, but also spatial dependence, which might induce lower statistical power of conventional panel unit root test. Our empirical results indicate that output is converging among OECD countries. However, convergence is characterized as conditional. The results also report a relatively lower convergent speed compared to conventional panel studies.

Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil

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Werneck Guilherme L.

2002-01-01

Full Text Available Most ecologic studies use geographical areas as units of observation. Because data from areas close to one another tend to be more alike than those from distant areas, estimation of effect size and confidence intervals should consider spatial autocorrelation of measurements. In this report we demonstrate a method for modeling spatial autocorrelation within a mixed model framework, using data on environmental and socioeconomic determinants of the incidence of visceral leishmaniasis (VL in the city of Teresina, Piauí, Brazil. A model with a spherical covariance structure indicated significant spatial autocorrelation in the data and yielded a better fit than one assuming independent observations. While both models showed a positive association between VL incidence and residence in a favela (slum or in areas with green vegetation, values for the fixed effects and standard errors differed substantially between the models. Exploration of the data's spatial correlation structure through the semivariogram should precede the use of these models. Our findings support the hypothesis of spatial dependence of VL rates and indicate that it might be useful to model spatial correlation in order to obtain more accurate point and standard error estimates.

A spatial error model with continuous random effects and an application to growth convergence

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Laurini, Márcio Poletti

2017-10-01

We propose a spatial error model with continuous random effects based on Matérn covariance functions and apply this model for the analysis of income convergence processes (β -convergence). The use of a model with continuous random effects permits a clearer visualization and interpretation of the spatial dependency patterns, avoids the problems of defining neighborhoods in spatial econometrics models, and allows projecting the spatial effects for every possible location in the continuous space, circumventing the existing aggregations in discrete lattice representations. We apply this model approach to analyze the economic growth of Brazilian municipalities between 1991 and 2010 using unconditional and conditional formulations and a spatiotemporal model of convergence. The results indicate that the estimated spatial random effects are consistent with the existence of income convergence clubs for Brazilian municipalities in this period.

Spatial Dependence and Determinants of Dairy Farmers' Adoption of Best Management Practices for Water Protection in New Zealand

Science.gov (United States)

Yang, Wei; Sharp, Basil

2017-04-01

This paper analyses spatial dependence and determinants of the New Zealand dairy farmers' adoption of best management practices to protect water quality. A Bayesian spatial durbin probit model is used to survey data collected from farmers in the Waikato region of New Zealand. The results show that farmers located near each other exhibit similar choice behaviour, indicating the importance of farmer interactions in adoption decisions. The results also address that information acquisition is the most important determinant of farmers' adoption of best management practices. Financial problems are considered a significant barrier to adopting best management practices. Overall, the existence of distance decay effect and spatial dependence in farmers' adoption decisions highlights the importance of accounting for spatial effects in farmers' decision-making, which emerges as crucial to the formulation of sustainable agriculture policy.

Selection of spatial reference frames depends on task's demands

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Greeshma Sharma

2016-12-01

Full Text Available Spatial reference frames (SRF are the means of representing spatial relations or locations either in an egocentric coordinate system (centred on navigator or in an allocentric coordinate system (Centred on object. It is necessary to understand when and how spatial representation switches between allocentric and egocentric reference frames in context to spatial tasks. The objective of this study was to explore if the elementary spatial representation does exist, whether it would remain consistent or change under the influence of a task's demand. Also, we explored how the SRF would assist if the environment is enriched with landmarks, having multiple routes for wayfinding. The results showed that the switching of SRF depends not only on the default representation but also on a task's demand. They also demonstrated that participants who were using allocentric representation performed better in the presence of landmarks.

A study of spatial resolution in pollution exposure modelling

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Gustafsson Susanna

2007-06-01

Full Text Available Abstract Background This study is part of several ongoing projects concerning epidemiological research into the effects on health of exposure to air pollutants in the region of Scania, southern Sweden. The aim is to investigate the optimal spatial resolution, with respect to temporal resolution, for a pollutant database of NOx-values which will be used mainly for epidemiological studies with durations of days, weeks or longer periods. The fact that a pollutant database has a fixed spatial resolution makes the choice critical for the future use of the database. Results The results from the study showed that the accuracy between the modelled concentrations of the reference grid with high spatial resolution (100 m, denoted the fine grid, and the coarser grids (200, 400, 800 and 1600 meters improved with increasing spatial resolution. When the pollutant values were aggregated in time (from hours to days and weeks the disagreement between the fine grid and the coarser grids were significantly reduced. The results also illustrate a considerable difference in optimal spatial resolution depending on the characteristic of the study area (rural or urban areas. To estimate the accuracy of the modelled values comparison were made with measured NOx values. The mean difference between the modelled and the measured value were 0.6 μg/m3 and the standard deviation 5.9 μg/m3 for the daily difference. Conclusion The choice of spatial resolution should not considerably deteriorate the accuracy of the modelled NOx values. Considering the comparison between modelled and measured values we estimate that an error due to coarse resolution greater than 1 μg/m3 is inadvisable if a time resolution of one day is used. Based on the study of different spatial resolutions we conclude that for urban areas a spatial resolution of 200–400 m is suitable; and for rural areas the spatial resolution could be coarser (about 1600 m. This implies that we should develop a pollutant

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

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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.

Models and Inference for Multivariate Spatial Extremes

KAUST Repository

Vettori, Sabrina

2017-12-07

The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical

Spatial pattern of diarrhea based on regional economic and environment by spatial autoregressive model

Science.gov (United States)

Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy

2014-10-01

The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.

Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

KAUST Repository

Sang, Huiyan

2011-12-01

This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.

Thermodynamic Model of Spatial Memory

Science.gov (United States)

Kaufman, Miron; Allen, P.

1998-03-01

We develop and test a thermodynamic model of spatial memory. Our model is an application of statistical thermodynamics to cognitive science. It is related to applications of the statistical mechanics framework in parallel distributed processes research. Our macroscopic model allows us to evaluate an entropy associated with spatial memory tasks. We find that older adults exhibit higher levels of entropy than younger adults. Thurstone's Law of Categorical Judgment, according to which the discriminal processes along the psychological continuum produced by presentations of a single stimulus are normally distributed, is explained by using a Hooke spring model of spatial memory. We have also analyzed a nonlinear modification of the ideal spring model of spatial memory. This work is supported by NIH/NIA grant AG09282-06.

The 3-D global spatial data model foundation of the spatial data infrastructure

CERN Document Server

Burkholder, Earl F

2008-01-01

Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements. Modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Foundation of the Spatial Data Infrastructure offers a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This groundbreaking spatial model incorporates both a functional model and a stochastic model to connect the physical world to the ECEF rectangular system. Combining horizontal and vertical data into a single, three-dimensional database, this authoritative monograph provides a logical development of theoretical concepts and practical tools that can be used to handle spatial data mo...

Spatial modelling of assumption of tourism development with geographic IT using

Directory of Open Access Journals (Sweden)

Jitka Machalová

2010-01-01

Full Text Available The aim of this article is to show the possibilities of spatial modelling and analysing of assumptions of tourism development in the Czech Republic with the objective to make decision-making processes in tourism easier and more efficient (for companies, clients as well as destination managements. The development and placement of tourism depend on the factors (conditions that influence its application in specific areas. These factors are usually divided into three groups: selective, localization and realization. Tourism is inseparably connected with space – countryside. The countryside can be modelled and consecutively analysed by the means of geographical information technologies. With the help of spatial modelling and following analyses the localization and realization conditions in the regions of the Czech Republic have been evaluated. The best localization conditions have been found in the Liberecký region. The capital city of Prague has negligible natural conditions; however, those social ones are on a high level. Next, the spatial analyses have shown that the best realization conditions are provided by the capital city of Prague. Then the Central-Bohemian, South-Moravian, Moravian-Silesian and Karlovarský regions follow. The development of tourism destination is depended not only on the localization and realization factors but it is basically affected by the level of local destination management. Spatial modelling can help destination managers in decision-making processes in order to optimal use of destination potential and efficient targeting their marketing activities.

Modeling multisite streamflow dependence with maximum entropy copula

Science.gov (United States)

Hao, Z.; Singh, V. P.

2013-10-01

Synthetic streamflows at different sites in a river basin are needed for planning, operation, and management of water resources projects. Modeling the temporal and spatial dependence structure of monthly streamflow at different sites is generally required. In this study, the maximum entropy copula method is proposed for multisite monthly streamflow simulation, in which the temporal and spatial dependence structure is imposed as constraints to derive the maximum entropy copula. The monthly streamflows at different sites are then generated by sampling from the conditional distribution. A case study for the generation of monthly streamflow at three sites in the Colorado River basin illustrates the application of the proposed method. Simulated streamflow from the maximum entropy copula is in satisfactory agreement with observed streamflow.

Penultimate modeling of spatial extremes: statistical inference for max-infinitely divisible processes

KAUST Repository

Huser, Raphaël

2018-01-09

Extreme-value theory for stochastic processes has motivated the statistical use of max-stable models for spatial extremes. However, fitting such asymptotic models to maxima observed over finite blocks is problematic when the asymptotic stability of the dependence does not prevail in finite samples. This issue is particularly serious when data are asymptotically independent, such that the dependence strength weakens and eventually vanishes as events become more extreme. We here aim to provide flexible sub-asymptotic models for spatially indexed block maxima, which more realistically account for discrepancies between data and asymptotic theory. We develop models pertaining to the wider class of max-infinitely divisible processes, extending the class of max-stable processes while retaining dependence properties that are natural for maxima: max-id models are positively associated, and they yield a self-consistent family of models for block maxima defined over any time unit. We propose two parametric construction principles for max-id models, emphasizing a point process-based generalized spectral representation, that allows for asymptotic independence while keeping the max-stable extremal-$t$ model as a special case. Parameter estimation is efficiently performed by pairwise likelihood, and we illustrate our new modeling framework with an application to Dutch wind gust maxima calculated over different time units.

A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution

KAUST Repository

Tagle, Felipe

2017-12-06

Large, non-Gaussian spatial datasets pose a considerable modeling challenge as the dependence structure implied by the model needs to be captured at different scales, while retaining feasible inference. Skew-normal and skew-t distributions have only recently begun to appear in the spatial statistics literature, without much consideration, however, for the ability to capture dependence at multiple resolutions, and simultaneously achieve feasible inference for increasingly large data sets. This article presents the first multi-resolution spatial model inspired by the skew-t distribution, where a large-scale effect follows a multivariate normal distribution and the fine-scale effects follow a multivariate skew-normal distributions. The resulting marginal distribution for each region is skew-t, thereby allowing for greater flexibility in capturing skewness and heavy tails characterizing many environmental datasets. Likelihood-based inference is performed using a Monte Carlo EM algorithm. The model is applied as a stochastic generator of daily wind speeds over Saudi Arabia.

Spatial structure arising from neighbour-dependent bias in collective cell movement.

Science.gov (United States)

Binny, Rachelle N; Haridas, Parvathi; James, Alex; Law, Richard; Simpson, Matthew J; Plank, Michael J

2016-01-01

Mathematical models of collective cell movement often neglect the effects of spatial structure, such as clustering, on the population dynamics. Typically, they assume that individuals interact with one another in proportion to their average density (the mean-field assumption) which means that cell-cell interactions occurring over short spatial ranges are not accounted for. However, in vitro cell culture studies have shown that spatial correlations can play an important role in determining collective behaviour. Here, we take a combined experimental and modelling approach to explore how individual-level interactions give rise to spatial structure in a moving cell population. Using imaging data from in vitro experiments, we quantify the extent of spatial structure in a population of 3T3 fibroblast cells. To understand how this spatial structure arises, we develop a lattice-free individual-based model (IBM) and simulate cell movement in two spatial dimensions. Our model allows an individual's direction of movement to be affected by interactions with other cells in its neighbourhood, providing insights into how directional bias generates spatial structure. We consider how this behaviour scales up to the population level by using the IBM to derive a continuum description in terms of the dynamics of spatial moments. In particular, we account for spatial correlations between cells by considering dynamics of the second spatial moment (the average density of pairs of cells). Our numerical results suggest that the moment dynamics description can provide a good approximation to averaged simulation results from the underlying IBM. Using our in vitro data, we estimate parameters for the model and show that it can generate similar spatial structure to that observed in a 3T3 fibroblast cell population.

Cosmological backreaction within the Szekeres model and emergence of spatial curvature

Energy Technology Data Exchange (ETDEWEB)

Bolejko, Krzysztof, E-mail: krzysztof.bolejko@sydney.edu.au [Sydney Institute for Astronomy, School of Physics A28, The University of Sydney, Sydney, NSW, 2006 (Australia)

2017-06-01

This paper discusses the phenomenon of backreaction within the Szekeres model. Cosmological backreaction describes how the mean global evolution of the Universe deviates from the Friedmannian evolution. The analysis is based on models of a single cosmological environment and the global ensemble of the Szekeres models (of the Swiss-Cheese-type and Styrofoam-type). The obtained results show that non-linear growth of cosmic structures is associated with the growth of the spatial curvature Ω{sub R} (in the FLRW limit Ω{sub R} → Ω {sub k} ). If averaged over global scales the result depends on the assumed global model of the Universe. Within the Swiss-Cheese model, which does have a fixed background, the volume average follows the evolution of the background, and the global spatial curvature averages out to zero (the background model is the ΛCDM model, which is spatially flat). In the Styrofoam-type model, which does not have a fixed background, the mean evolution deviates from the spatially flat ΛCDM model, and the mean spatial curvature evolves from Ω{sub R} =0 at the CMB to Ω{sub R} ∼ 0.1 at 0 z =. If the Styrofoam-type model correctly captures evolutionary features of the real Universe then one should expect that in our Universe, the spatial curvature should build up (local growth of cosmic structures) and its mean global average should deviate from zero (backreaction). As a result, this paper predicts that the low-redshift Universe should not be spatially flat (i.e. Ω {sub k} ≠ 0, even if in the early Universe Ω {sub k} = 0) and therefore when analysing low- z cosmological data one should keep Ω {sub k} as a free parameter and independent from the CMB constraints.

Physically-based modeling of topographic effects on spatial evapotranspiration and soil moisture patterns through radiation and wind

Directory of Open Access Journals (Sweden)

M. Liu

2012-02-01

Full Text Available In this paper, simulations with the Soil Water Atmosphere Plant (SWAP model are performed to quantify the spatial variability of both potential and actual evapotranspiration (ET, and soil moisture content (SMC caused by topography-induced spatial wind and radiation differences. To obtain the spatially distributed ET/SMC patterns, the field scale SWAP model is applied in a distributed way for both pointwise and catchment wide simulations. An adapted radiation model from r.sun and the physically-based meso-scale wind model METRAS PC are applied to obtain the spatial radiation and wind patterns respectively, which show significant spatial variation and correlation with aspect and elevation respectively. Such topographic dependences and spatial variations further propagate to ET/SMC. A strong spatial, seasonal-dependent, scale-relevant intra-catchment variability in daily/annual ET and less variability in SMC can be observed from the numerical experiments. The study concludes that topography has a significant effect on ET/SMC in the humid region where ET is a energy limited rather than water availability limited process. It affects the spatial runoff generation through spatial radiation and wind, therefore should be applied to inform hydrological model development. In addition, the methodology used in the study can serve as a general method for physically-based ET estimation for data sparse regions.

Spatial structure arising from neighbour-dependent bias in collective cell movement

Directory of Open Access Journals (Sweden)

Rachelle N. Binny

2016-02-01

Full Text Available Mathematical models of collective cell movement often neglect the effects of spatial structure, such as clustering, on the population dynamics. Typically, they assume that individuals interact with one another in proportion to their average density (the mean-field assumption which means that cell–cell interactions occurring over short spatial ranges are not accounted for. However, in vitro cell culture studies have shown that spatial correlations can play an important role in determining collective behaviour. Here, we take a combined experimental and modelling approach to explore how individual-level interactions give rise to spatial structure in a moving cell population. Using imaging data from in vitro experiments, we quantify the extent of spatial structure in a population of 3T3 fibroblast cells. To understand how this spatial structure arises, we develop a lattice-free individual-based model (IBM and simulate cell movement in two spatial dimensions. Our model allows an individual’s direction of movement to be affected by interactions with other cells in its neighbourhood, providing insights into how directional bias generates spatial structure. We consider how this behaviour scales up to the population level by using the IBM to derive a continuum description in terms of the dynamics of spatial moments. In particular, we account for spatial correlations between cells by considering dynamics of the second spatial moment (the average density of pairs of cells. Our numerical results suggest that the moment dynamics description can provide a good approximation to averaged simulation results from the underlying IBM. Using our in vitro data, we estimate parameters for the model and show that it can generate similar spatial structure to that observed in a 3T3 fibroblast cell population.

Bayesian Spatial Modelling with R-INLA

Directory of Open Access Journals (Sweden)

Finn Lindgren

2015-02-01

Full Text Available The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA approach proposed by Rue, Martino, and Chopin (2009 is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized linear mixed to spatial and spatio-temporal models. Combined with the stochastic partial differential equation approach (SPDE, Lindgren, Rue, and Lindstrm 2011, one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial mod- els, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.

SPATIALLY DEPENDENT HEATING AND IONIZATION IN AN ICME OBSERVED BY BOTH ACE AND ULYSSES

Energy Technology Data Exchange (ETDEWEB)

Lepri, Susan T. [Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI 48109-2143 (United States); Laming, J. Martin; Rakowski, Cara E. [Space Science Division, Naval Research Laboratory, Code 7674L, Washington, DC 20375-5321 (United States); Von Steiger, Rudolf [International Space Science Institute, Bern CH-3012 (Switzerland)

2012-12-01

The 2005 January 21 interplanetary coronal mass ejection (ICME) observed by multiple spacecraft at L1 was also observed from January 21-February 4 at Ulysses (5.3 AU). Previous studies of this ICME have found evidence suggesting that the flanks of a magnetic cloud like structure associated with this ICME were observed at L1 while a more central cut through the associated magnetic cloud was observed at Ulysses. This event allows us to study spatial variation across the ICME and relate it to the eruption at the Sun. In order to examine the spatial dependence of the heating in this ICME, we present an analysis and comparison of the heavy ion composition observed during the passage of the ICME at L1 and at Ulysses. Using SWICS, we compare the heavy ion composition across the two different observation cuts through the ICME and compare it with predictions for heating during the eruption based on models of the time-dependent ionization balance throughout the event.

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.

Modeling and Compensating Temperature-Dependent Non-Uniformity Noise in IR Microbolometer Cameras

Directory of Open Access Journals (Sweden)

Alejandro Wolf

2016-07-01

Full Text Available Images rendered by uncooled microbolometer-based infrared (IR cameras are severely degraded by the spatial non-uniformity (NU noise. The NU noise imposes a fixed-pattern over the true images, and the intensity of the pattern changes with time due to the temperature instability of such cameras. In this paper, we present a novel model and a compensation algorithm for the spatial NU noise and its temperature-dependent variations. The model separates the NU noise into two components: a constant term, which corresponds to a set of NU parameters determining the spatial structure of the noise, and a dynamic term, which scales linearly with the fluctuations of the temperature surrounding the array of microbolometers. We use a black-body radiator and samples of the temperature surrounding the IR array to offline characterize both the constant and the temperature-dependent NU noise parameters. Next, the temperature-dependent variations are estimated online using both a spatially uniform Hammerstein-Wiener estimator and a pixelwise least mean squares (LMS estimator. We compensate for the NU noise in IR images from two long-wave IR cameras. Results show an excellent NU correction performance and a root mean square error of less than 0.25 ∘ C, when the array’s temperature varies by approximately 15 ∘ C.

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

Science.gov (United States)

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

Evidence of political yardstick competition in France using a two-regime spatial Durbin model with fixed effects

NARCIS (Netherlands)

Elhorst, J. Paul; Freret, Sandy

2009-01-01

This research proposes a two-regime spatial Durbin model with spatial and time-period fixed effects to test for political yardstick competition and exclude any other explanation that might produce spatial interaction effects among the dependent variable, the independent variables, or the error term.

STARS: An ArcGIS Toolset Used to Calculate the Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data

Directory of Open Access Journals (Sweden)

Erin Peterson

2014-01-01

Full Text Available This paper describes the STARS ArcGIS geoprocessing toolset, which is used to calcu- late the spatial information needed to fit spatial statistical models to stream network data using the SSN package. The STARS toolset is designed for use with a landscape network (LSN, which is a topological data model produced by the FLoWS ArcGIS geoprocessing toolset. An overview of the FLoWS LSN structure and a few particularly useful tools is also provided so that users will have a clear understanding of the underlying data struc- ture that the STARS toolset depends on. This document may be used as an introduction to new users. The methods used to calculate the spatial information and format the final .ssn object are also explicitly described so that users may create their own .ssn object using other data models and software.

Long-range spatial dependence in fractured rock. Empirical evidence and implications for tracer transport

International Nuclear Information System (INIS)

Painter, S.

1999-02-01

Nonclassical stochastic continuum models incorporating long-range spatial dependence are evaluated as models for fractured crystalline rock. Open fractures and fracture zones are not modeled explicitly in this approach. The fracture zones and intact rock are modeled as a single stochastic continuum. The large contrasts between the fracture zones and unfractured rock are accounted for by making use of random field models specifically designed for highly variable systems. Hydraulic conductivity data derived from packer tests in the vicinity of the Aespoe Hard Rock Laboratory form the basis for the evaluation. The Aespoe log K data were found to be consistent with a fractal scaling model based on bounded fractional Levy motion (bfLm), a model that has been used previously to model highly variable sedimentary formations. However, the data are not sufficient to choose between this model, a fractional Brownian motion model for the normal-score transform of log K, and a conventional geostatistical model. Stochastic simulations conditioned by the Aespoe data coupled with flow and tracer transport calculations demonstrate that the models with long-range dependence predict earlier arrival times for contaminants. This demonstrates the need to evaluate this class of models when assessing the performance of proposed waste repositories. The relationship between intermediate-scale and large-scale transport properties in media with long-range dependence is also addressed. A new Monte Carlo method for stochastic upscaling of intermediate-scale field data is proposed

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.

Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

KAUST Repository

Sang, Huiyan; Jun, Mikyoung; Huang, Jianhua Z.

2011-01-01

This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models

Intelligent spatial ecosystem modeling using parallel processors

International Nuclear Information System (INIS)

Maxwell, T.; Costanza, R.

1993-01-01

Spatial modeling of ecosystems is essential if one's modeling goals include developing a relatively realistic description of past behavior and predictions of the impacts of alternative management policies on future ecosystem behavior. Development of these models has been limited in the past by the large amount of input data required and the difficulty of even large mainframe serial computers in dealing with large spatial arrays. These two limitations have begun to erode with the increasing availability of remote sensing data and GIS systems to manipulate it, and the development of parallel computer systems which allow computation of large, complex, spatial arrays. Although many forms of dynamic spatial modeling are highly amenable to parallel processing, the primary focus in this project is on process-based landscape models. These models simulate spatial structure by first compartmentalizing the landscape into some geometric design and then describing flows within compartments and spatial processes between compartments according to location-specific algorithms. The authors are currently building and running parallel spatial models at the regional scale for the Patuxent River region in Maryland, the Everglades in Florida, and Barataria Basin in Louisiana. The authors are also planning a project to construct a series of spatially explicit linked ecological and economic simulation models aimed at assessing the long-term potential impacts of global climate change

Input-dependent frequency modulation of cortical gamma oscillations shapes spatial synchronization and enables phase coding.

Science.gov (United States)

Lowet, Eric; Roberts, Mark; Hadjipapas, Avgis; Peter, Alina; van der Eerden, Jan; De Weerd, Peter

2015-02-01

Fine-scale temporal organization of cortical activity in the gamma range (∼25-80Hz) may play a significant role in information processing, for example by neural grouping ('binding') and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes

Spatial-Temporal Correlation Properties of the 3GPP Spatial Channel Model and the Kronecker MIMO Channel Model

Directory of Open Access Journals (Sweden)

Cheng-Xiang Wang

2007-02-01

Full Text Available The performance of multiple-input multiple-output (MIMO systems is greatly influenced by the spatial-temporal correlation properties of the underlying MIMO channels. This paper investigates the spatial-temporal correlation characteristics of the spatial channel model (SCM in the Third Generation Partnership Project (3GPP and the Kronecker-based stochastic model (KBSM at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-temporal separability at all the three levels. The spatial-temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA and angle of departure (AoD. The KBSM with the Gaussian-shaped power azimuth spectrum (PAS is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.

Lattice Three-Species Models of the Spatial Spread of Rabies among FOXES

Science.gov (United States)

Benyoussef, A.; Boccara, N.; Chakib, H.; Ez-Zahraouy, H.

Lattice models describing the spatial spread of rabies among foxes are studied. In these models, the fox population is divided into three-species: susceptible (S), infected or incubating (I), and infectious or rabid (R). They are based on the fact that susceptible and incubating foxes are territorial while rabid foxes have lost their sense of direction and move erratically. Two different models are investigated: a one-dimensional coupled-map lattice model, and a two-dimensional automata network model. Both models take into account the short-range character of the infection process and the diffusive motion of rabid foxes. Numerical simulations show how the spatial distribution of rabies, and the speed of propagation of the epizootic front depend upon the carrying capacity of the environment and diffusion of rabid foxes out of their territory.

Neuroprotective mechanism of Lycium barbarum polysaccharides against hippocampal-dependent spatial memory deficits in a rat model of obstructive sleep apnea.

Directory of Open Access Journals (Sweden)

Chun-Sing Lam

Full Text Available Chronic intermittent hypoxia (CIH is a hallmark of obstructive sleep apnea (OSA, which induces hippocampal injuries mediated by oxidative stress. This study aims to examine the neuroprotective mechanism of Lycium barbarum polysaccharides (LBP against CIH-induced spatial memory deficits. Adult Sprague-Dawley rats were exposed to hypoxic treatment resembling a severe OSA condition for a week. The animals were orally fed with LBP solution (1 mg/kg daily 2 hours prior to hypoxia or in air for the control. The effect of LBP on the spatial memory and levels of oxidative stress, inflammation, endoplasmic reticulum (ER stress, apoptosis and neurogenesis in the hippocampus was examined. There was a significant deficit in the spatial memory and an elevated level of malondialdehyde with a decreased expression of antioxidant enzymes (SOD, GPx-1 in the hypoxic group when compared with the normoxic control. In addition, redox-sensitive nuclear factor kappa B (NFКB canonical pathway was activated with a translocation of NFКB members (p65, p50 and increased expression levels of NFКB-dependent inflammatory cytokines and mediator (TNFα, IL-1β, COX-2; also, a significantly elevated level of ER stress (GRP78/Bip, PERK, CHOP and autophagic flux in the hypoxic group, leading to neuronal apoptosis in hippocampal subfields (DG, CA1, CA3. Remarkably, LBP administration normalized the elevated level of oxidative stress, neuroinflammation, ER stress, autophagic flux and apoptosis induced by hypoxia. Moreover, LBP significantly mitigated both the caspase-dependent intrinsic (Bax, Bcl2, cytochrome C, cleaved caspase-3 and extrinsic (FADD, cleaved caspase-8, Bid signaling apoptotic cascades. Furthermore, LBP administration prevented the spatial memory deficit and enhanced the hippocampal neurogenesis induced by hypoxia. Our results suggest that LBP is neuroprotective against CIH-induced hippocampal-dependent spatial memory deficits by promoting hippocampal neurogenesis

Novel scatter compensation with energy and spatial dependent corrections in positron emission tomography

International Nuclear Information System (INIS)

Guerin, Bastien

2010-01-01

We developed and validated a fast Monte Carlo simulation of PET acquisitions based on the SimSET program modeling accurately the propagation of gamma photons in the patient as well as the block-based PET detector. Comparison of our simulation with another well validated code, GATE, and measurements on two GE Discovery ST PET scanners showed that it models accurately energy spectra (errors smaller than 4.6%), the spatial resolution of block-based PET scanners (6.1%), scatter fraction (3.5%), sensitivity (2.3%) and count rates (12.7%). Next, we developed a novel scatter correction incorporating the energy and position of photons detected in list-mode. Our approach is based on the reformulation of the list-mode likelihood function containing the energy distribution of detected coincidences in addition to their spatial distribution, yielding an EM reconstruction algorithm containing spatial and energy dependent correction terms. We also proposed using the energy in addition to the position of gamma photons in the normalization of the scatter sinogram. Finally, we developed a method for estimating primary and scatter photons energy spectra from total spectra detected in different sectors of the PET scanner. We evaluated the accuracy and precision of our new spatio-spectral scatter correction and that of the standard spatial correction using realistic Monte Carlo simulations. These results showed that incorporating the energy in the scatter correction reduces bias in the estimation of the absolute activity level by ∼ 60% in the cold regions of the largest patients and yields quantification errors less than 13% in all regions. (author)

A selfsimilar behavior of the urban structure in the spatially inhomogeneous model

Science.gov (United States)

Echkina, E. Y.; Inovenkov, O. I.; Kostomarov, D. P.

2006-03-01

At present there is a strong tendency to use new methods for the description of the regional and spatial economy. In increasing frequency we consider that any economic activity is spatially dependent. The problem of the evolution of internal urban formation can be described with the exact supposition. So that is why we use partial derivative equations set with the appropriate boundary and initial conditions for the solving the problem of the urban evolution. Here we describe the model of urban population's density modification taking into account a modification of the housing quality. A program has been created which realizes difference method of mixed problem solution for population's density. For the wide class of coefficients it has been shown that the problem's solution “quickly forgets” the parts of the initial conditions and comes out to the intermediate asymptotic form, which nature depends only on the problem's operator. Actually it means that the urban structure does not depend on external circumstances and is formed by the internal structure of the model.

Potential and flux field landscape theory. I. Global stability and dynamics of spatially dependent non-equilibrium systems.

Science.gov (United States)

Wu, Wei; Wang, Jin

2013-09-28

We established a potential and flux field landscape theory to quantify the global stability and dynamics of general spatially dependent non-equilibrium deterministic and stochastic systems. We extended our potential and flux landscape theory for spatially independent non-equilibrium stochastic systems described by Fokker-Planck equations to spatially dependent stochastic systems governed by general functional Fokker-Planck equations as well as functional Kramers-Moyal equations derived from master equations. Our general theory is applied to reaction-diffusion systems. For equilibrium spatially dependent systems with detailed balance, the potential field landscape alone, defined in terms of the steady state probability distribution functional, determines the global stability and dynamics of the system. The global stability of the system is closely related to the topography of the potential field landscape in terms of the basins of attraction and barrier heights in the field configuration state space. The effective driving force of the system is generated by the functional gradient of the potential field alone. For non-equilibrium spatially dependent systems, the curl probability flux field is indispensable in breaking detailed balance and creating non-equilibrium condition for the system. A complete characterization of the non-equilibrium dynamics of the spatially dependent system requires both the potential field and the curl probability flux field. While the non-equilibrium potential field landscape attracts the system down along the functional gradient similar to an electron moving in an electric field, the non-equilibrium flux field drives the system in a curly way similar to an electron moving in a magnetic field. In the small fluctuation limit, the intrinsic potential field as the small fluctuation limit of the potential field for spatially dependent non-equilibrium systems, which is closely related to the steady state probability distribution functional, is

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)

The role of spatial topology in a toy model of classical electrodynamics in (1+1) dimensions

International Nuclear Information System (INIS)

Boozer, A.D.

2010-01-01

We discuss the role of spatial topology in a toy model of classical electrodynamics in (1+1) dimensions. The model describes a collection of Newtonian point particles coupled to a pair of scalar fields E(t,x) and B(t,x), which mediate forces between the particles and support freely propagating radiation. We formulate the model on both a line and a circle, and show that the behavior of the model strongly depends on the choice of spatial topology.

An API for Integrating Spatial Context Models with Spatial Reasoning Algorithms

DEFF Research Database (Denmark)

Kjærgaard, Mikkel Baun

2006-01-01

The integration of context-aware applications with spatial context models is often done using a common query language. However, algorithms that estimate and reason about spatial context information can benefit from a tighter integration. An object-oriented API makes such integration possible...... and can help reduce the complexity of algorithms making them easier to maintain and develop. This paper propose an object-oriented API for context models of the physical environment and extensions to a location modeling approach called geometric space trees for it to provide adequate support for location...... modeling. The utility of the API is evaluated in several real-world cases from an indoor location system, and spans several types of spatial reasoning algorithms....

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

Spatial-dependence recurrence sample entropy

Science.gov (United States)

Pham, Tuan D.; Yan, Hong

2018-03-01

Measuring complexity in terms of the predictability of time series is a major area of research in science and engineering, and its applications are spreading throughout many scientific disciplines, where the analysis of physiological signals is perhaps the most widely reported in literature. Sample entropy is a popular measure for quantifying signal irregularity. However, the sample entropy does not take sequential information, which is inherently useful, into its calculation of sample similarity. Here, we develop a method that is based on the mathematical principle of the sample entropy and enables the capture of sequential information of a time series in the context of spatial dependence provided by the binary-level co-occurrence matrix of a recurrence plot. Experimental results on time-series data of the Lorenz system, physiological signals of gait maturation in healthy children, and gait dynamics in Huntington's disease show the potential of the proposed method.

Spatially varying coefficient models in real estate: Eigenvector spatial filtering and alternative approaches

NARCIS (Netherlands)

Helbich, M; Griffith, D

2016-01-01

Real estate policies in urban areas require the recognition of spatial heterogeneity in housing prices to account for local settings. In response to the growing number of spatially varying coefficient models in housing applications, this study evaluated four models in terms of their spatial patterns

Linking spatial and dynamic models for traffic maneuvers

DEFF Research Database (Denmark)

Olderog, Ernst-Rüdiger; Ravn, Anders Peter; Wisniewski, Rafal

2015-01-01

For traffic maneuvers of multiple vehicles on highways we build an abstract spatial and a concrete dynamic model. In the spatial model we show the safety (collision freedom) of lane-change maneuvers. By linking the spatial and dynamic model via suitable refinements of the spatial atoms to distance...

Integrating spatial and biomass planning for the United States

International Nuclear Information System (INIS)

Wang, Sicong; Wang, Shifeng

2016-01-01

Biomass is low-carbon energy and has tremendous potential as an alternative to fossil fuels. However, the significant role of biomass in future low-carbon energy portfolio depends heavily on its consumption. The paper presents a first attempt to examine the spatial-temporal patterns of biomass consumption in the United States (US), using a novel method-spatial Seemingly Unrelated Regression (SUR) model, in order to strengthen the link between energy planning and spatial planning. In order to obtain the robust parameters of spatial SUR models and estimate the parameters efficiently, an iterative maximum likelihood method, which takes full advantage of the stationary characteristic of maximum likelihood estimation, has been developed. The robust parameters of models can help draw a proper inference for biomass consumption. Then the spatial-temporal patterns of biomass consumption in the US at the state level are investigated using the spatial SUR models with the estimation method developed and data covering the period of 2000–2012. Results show that there are spatial dependences among biomass consumption. The presence of spatial dependence in biomass consumption has informative implications for making sustainable biomass polices. It suggests new efforts to adding a cross-state dimension to state-level energy policy and coordinating some elements of energy policy across states are still needed. In addition, results consistent with classic economic theory further proves the correctness of applying the spatial SUR models to investigate the spatial-temporal patterns of biomass consumption. - Highlights: • A spatial model is suggested as framework to investigate biomass consumption. • A new estimation method is developed to obtain the robust parameters of model. • There are spatial dependences among biomass consumption. • The spatial dependence can contribute to making sustainable biomass policies. • Efforts to adding cross-state dimension to state

Angular dependent anisotropic terahertz response of vertically aligned multi-walled carbon nanotube arrays with spatial dispersion

Science.gov (United States)

Zhou, Yixuan; Yiwen, E.; Xu, Xinlong; Li, Weilong; Wang, Huan; Zhu, Lipeng; Bai, Jintao; Ren, Zhaoyu; Wang, Li

2016-12-01

Spatial dispersion effect of aligned carbon nanotubes (CNTs) in the terahertz (THz) region has significance for both theoretical and applied consideration due to the unique intrinsically anisotropic physical properties of CNTs. Herein, we report the angular dependent reflection of p-polarized THz wave from vertically aligned multi-walled CNT arrays in both experiment and theory. The spectra indicate that the reflection depends on the film thickness of vertically aligned CNTs, the incident angle, and the frequency. The calculation model is based on the spatial dispersion effect of aligned CNTs and performed with effective impedance method and the Maxwell-Garnett approximation. The results fit well with the experiment when the thickness of CNT film is thin, which reveals a coherent superposition mechanism of the CNT surface reflection and CNTs/Si interface reflection. For thick CNT films, the CNTs/Si interface response determines the reflection at small incident angles, while the CNTs surface effect dominates at large incident angles. This work investigates the spatial dispersion effect of vertically aligned CNT arrays in the THz region, and paves a way for potential anisotropic THz applications based on CNTs with oblique incidence requirements.

Spatial occupancy models for large data sets

Science.gov (United States)

Johnson, Devin S.; Conn, Paul B.; Hooten, Mevin B.; Ray, Justina C.; Pond, Bruce A.

2013-01-01

Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km2) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.

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.

Spatial short-term memory is impaired in dependent betel quid chewers.

Science.gov (United States)

Chiu, Meng-Chun; Shen, Bin; Li, Shuo-Heng; Ho, Ming-Chou

2016-08-01

Betel quid is regarded as a human carcinogen by the World Health Organization. It remains unknown whether chewing betel quid has a chronic effect on healthy betel quid chewers' memory. The present study aims to investigate whether chewing betel quid can affect short-term memory (STM). Three groups of participants (24 dependent chewers, 24 non-dependent chewers, and 24 non-chewers) were invited to carry out the matrix span task, the object span task, and the digit span task. All span tasks' results were adopted to assess spatial STM, visual STM, and verbal STM, respectively. Besides, there are three set sizes (small, medium, and large) in each span task. For the matrix span task, results showed that the dependent chewers had worse performances than the non-dependent chewers and the non-chewers at medium and large set sizes. For the object span task and digit span task, there were no differences in between groups. In each group, recognition performances were worse with the increasing set size and showing successful manipulation of memory load. The current study provided the first evidence that dependent betel quid chewing can selectively impair spatial STM rather than visual STM and verbal STM. Theoretical and practical implications of this result are discussed.

Modeling spatial effects of PM{sub 2.5} on term low birth weight in Los Angeles County

Energy Technology Data Exchange (ETDEWEB)

Coker, Eric, E-mail: cokerer@onid.orst.edu [College of Public Health and Human Sciences, Oregon State University, Corvallis, OR (United States); Ghosh, Jokay [School of Public Health, University of California, Los Angeles, Los Angeles, CA (United States); Jerrett, Michael [School of Public Health, University of California, Berkeley, Berkeley, CA (United States); Gomez-Rubio, Virgilio [Department of Mathematics, Universidad De Castilla-La Mancha, Albacete (Spain); Beckerman, Bernardo [School of Public Health, University of California, Berkeley, Berkeley, CA (United States); Cockburn, Myles [Preventive Medicine and Spatial Sciences, University of Southern California, Los Angeles, CA (United States); Liverani, Silvia [Department of Mathematics, Brunel University, London (United Kingdom); Su, Jason [School of Public Health, University of California, Berkeley, Berkeley, CA (United States); Li, Arthur [Department of Information Science, City of Hope National Cancer Center, Duarte, CA (United States); Kile, Molly L [College of Public Health and Human Sciences, Oregon State University, Corvallis, OR (United States); Ritz, Beate [School of Public Health, University of California, Los Angeles, Los Angeles, CA (United States); Molitor, John [College of Public Health and Human Sciences, Oregon State University, Corvallis, OR (United States)

2015-10-15

effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective. - Highlights: • We model the spatial dependency of PM{sub 2.5} effects on term low birth weight (TLBW). • PM{sub 2.5} effects on TLBW are shown to vary spatially across urban LA County. • Modeling spatial dependency of PM{sub 2.5} health effects may identify effect 'hotspots'. • Birth outcomes studies should consider the spatial dependency of PM{sub 2.5} effects.

Discretization-dependent model for weakly connected excitable media

Science.gov (United States)

Arroyo, Pedro André; Alonso, Sergio; Weber dos Santos, Rodrigo

2018-03-01

Pattern formation has been widely observed in extended chemical and biological processes. Although the biochemical systems are highly heterogeneous, homogenized continuum approaches formed by partial differential equations have been employed frequently. Such approaches are usually justified by the difference of scales between the heterogeneities and the characteristic spatial size of the patterns. Under different conditions, for example, under weak coupling, discrete models are more adequate. However, discrete models may be less manageable, for instance, in terms of numerical implementation and mesh generation, than the associated continuum models. Here we study a model to approach discreteness which permits the computer implementation on general unstructured meshes. The model is cast as a partial differential equation but with a parameter that depends not only on heterogeneities sizes, as in the case of quasicontinuum models, but also on the discretization mesh. Therefore, we refer to it as a discretization-dependent model. We validate the approach in a generic excitable media that simulates three different phenomena: the propagation of action membrane potential in cardiac tissue, in myelinated axons of neurons, and concentration waves in chemical microemulsions.

The Employment of spatial autoregressive models in predicting demand for natural gas

International Nuclear Information System (INIS)

Castro, Jorge Henrique de; Silva, Alexandre Pinto Alves da

2010-01-01

Develop the natural gas network is critical success factor for the distribution company. It is a decision that employs the demand given location 'x' and a future time 't' so that the net allows the best conditions for the return of the capital. In this segment, typical network industry, the spatial infra-structure vision associated to the market allows better evaluation of the business because to mitigate costs and risks. In fact, economic models little developed in order to assess the question of the location, due to its little employment by economists. The objective of this article is to analyze the application of spatial perspective in natural gas demand forecasting and to identify the models that can be employed observing issues of dependency and spatial heterogeneity; as well as the capacity of mapping of variables associated with the problem. (author)

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.

Spatial modelling with R-INLA: A review

KAUST Repository

Bakka, Haakon; Rue, Haavard; Fuglstad, Geir-Arne; Riebler, Andrea; Bolin, David; Krainski, Elias; Simpson, Daniel; Lindgren, Finn

2018-01-01

Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R-INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Mat\\'ern Gaussian random fields. In this review, we discuss the large success of spatial modelling with R-INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight-forward approaches for non-stationary spatial models and non-separable space-time models.

Spatial modelling with R-INLA: A review

KAUST Repository

Bakka, Haakon

2018-02-18

Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R-INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Mat\\\\\\'ern Gaussian random fields. In this review, we discuss the large success of spatial modelling with R-INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight-forward approaches for non-stationary spatial models and non-separable space-time models.

A gender- and sexual orientation-dependent spatial attentional effect of invisible images

OpenAIRE

Jiang, Yi; Costello, Patricia; Fang, Fang; Huang, Miner; He, Sheng

2006-01-01

Human observers are constantly bombarded with a vast amount of information. Selective attention helps us to quickly process what is important while ignoring the irrelevant. In this study, we demonstrate that information that has not entered observers' consciousness, such as interocularly suppressed (invisible) erotic pictures, can direct the distribution of spatial attention. Furthermore, invisible erotic information can either attract or repel observers' spatial attention depending on their ...

Focal adhesion kinase regulates neuronal growth, synaptic plasticity and hippocampus-dependent spatial learning and memory.

Science.gov (United States)

Monje, Francisco J; Kim, Eun-Jung; Pollak, Daniela D; Cabatic, Maureen; Li, Lin; Baston, Arthur; Lubec, Gert

2012-01-01

The focal adhesion kinase (FAK) is a non-receptor tyrosine kinase abundantly expressed in the mammalian brain and highly enriched in neuronal growth cones. Inhibitory and facilitatory activities of FAK on neuronal growth have been reported and its role in neuritic outgrowth remains controversial. Unlike other tyrosine kinases, such as the neurotrophin receptors regulating neuronal growth and plasticity, the relevance of FAK for learning and memory in vivo has not been clearly defined yet. A comprehensive study aimed at determining the role of FAK in neuronal growth, neurotransmitter release and synaptic plasticity in hippocampal neurons and in hippocampus-dependent learning and memory was therefore undertaken using the mouse model. Gain- and loss-of-function experiments indicated that FAK is a critical regulator of hippocampal cell morphology. FAK mediated neurotrophin-induced neuritic outgrowth and FAK inhibition affected both miniature excitatory postsynaptic potentials and activity-dependent hippocampal long-term potentiation prompting us to explore the possible role of FAK in spatial learning and memory in vivo. Our data indicate that FAK has a growth-promoting effect, is importantly involved in the regulation of the synaptic function and mediates in vivo hippocampus-dependent spatial learning and memory. Copyright © 2011 S. Karger AG, Basel.

Modelling spatial patterns and temporal trends of wildfires in Galicia (NW Spain

Directory of Open Access Journals (Sweden)

Jesús Barreal

2015-08-01

Full Text Available Aim of study: The goal of this paper is to analyse the importance of the main contributing factors to the occurrence of wildfires. Area of study: We employ data from the region of Galicia during 2001-2010; although the similarities shared between this area and other rural areas may allow extrapolation of the present results. Material and Methods: The spatial dependence is analysed by using the Moran’s I and LISA statistics. We also conduct an econometric analysis modelling both, the number of fires and the relative size of afflicted woodland area as dependent variables, which depend on the climatic, land cover variables, and socio-economic characteristics of the affected areas. Fixed effects and random effect models are estimated in order to control for the heterogeneity between the Forest Districts in Galicia. Main results: Moran’s I and LISA statistics show that there is spatial dependence in the occurrence of Galician wildfires. Econometrics models show that climatology, socioeconomic variables, and temporal trends are also important to study both, the number of wildfires and the burned-forest ratio. Research highlights: We conclude that in addition to direct forest actions, other agricultural or social public plans, can help to reduce wildfires in rural areas or wildland-urban areas. Based on these conclusions, a number of guidelines are provided that may foster the development of better forest management policies in order to reduce the occurrence of wildfires.

Spatial data quality and coastal spill modelling

International Nuclear Information System (INIS)

Li, Y.; Brimicombe, A.J.; Ralphs, M.P.

1998-01-01

Issues of spatial data quality are central to the whole oil spill modelling process. Both model and data quality performance issues should be considered as indispensable parts of a complete oil spill model specification and testing procedure. This paper presents initial results of research that will emphasise to modeler and manager alike the practical issues of spatial data quality for coastal oil spill modelling. It is centred around a case study of Jiao Zhou Bay in the People's Republic of China. The implications for coastal oil spill modelling are discussed and some strategies for managing the effects of spatial data quality in the outputs of oil spill modelling are explored. (author)

Control of spatial discretisation in coastal oil spill modelling

OpenAIRE

Li, Yang

2007-01-01

Spatial discretisation plays an important role in many numerical environmental models. This paper studies the control of spatial discretisation in coastal oil spill modelling with a view to assure the quality of modelling outputs for given spatial data inputs. Spatial data analysis techniques are effective for investigating and improving the spatial discretisation in different phases of the modelling. Proposed methods are implemented and tested with experimental models. A new “Automatic Searc...

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

Science.gov (United States)

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models

Directory of Open Access Journals (Sweden)

Chuanglin Fang

2015-11-01

Full Text Available Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI values at the city level, and employed Ordinary Least Squares (OLS, Spatial Lag Model (SAR, and Geographically Weighted Regression (GWR to quantitatively estimate the comprehensive impact and spatial variations of China’s urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality.

Spatial Interpretation of Tower, Chamber and Modelled Terrestrial Fluxes in a Tropical Forest Plantation

Science.gov (United States)

Whidden, E.; Roulet, N.

2003-04-01

Interpretation of a site average terrestrial flux may be complicated in the presence of inhomogeneities. Inhomogeneity may invalidate the basic assumptions of aerodynamic flux measurement. Chamber measurement may miss or misinterpret important temporal or spatial anomalies. Models may smooth over important nonlinearities depending on the scale of application. Although inhomogeneity is usually seen as a design problem, many sites have spatial variance that may have a large impact on net flux, and in many cases a large homogeneous surface is unrealistic. The sensitivity and validity of a site average flux are investigated in the presence of an inhomogeneous site. Directional differences are used to evaluate the validity of aerodynamic methods and the computation of a site average tower flux. Empirical and modelling methods are used to interpret the spatial controls on flux. An ecosystem model, Ecosys, is used to assess spatial length scales appropriate to the ecophysiologic controls. A diffusion model is used to compare tower, chamber, and model data, by spatially weighting contributions within the tower footprint. Diffusion model weighting is also used to improve tower flux estimates by producing footprint averaged ecological parameters (soil moisture, soil temperature, etc.). Although uncertainty remains in the validity of measurement methods and the accuracy of diffusion models, a detailed spatial interpretation is required at an inhomogeneous site. Flux estimation between methods improves with spatial interpretation, showing the importance to an estimation of a site average flux. Small-scale temporal and spatial anomalies may be relatively unimportant to overall flux, but accounting for medium-scale differences in ecophysiological controls is necessary. A combination of measurements and modelling can be used to define the appropriate time and length scales of significant non-linearity due to inhomogeneity.

Landform classification using a sub-pixel spatial attraction model to increase spatial resolution of digital elevation model (DEM

Directory of Open Access Journals (Sweden)

Marzieh Mokarrama

2018-04-01

Full Text Available The purpose of the present study is preparing a landform classification by using digital elevation model (DEM which has a high spatial resolution. To reach the mentioned aim, a sub-pixel spatial attraction model was used as a novel method for preparing DEM with a high spatial resolution in the north of Darab, Fars province, Iran. The sub-pixel attraction models convert the pixel into sub-pixels based on the neighboring pixels fraction values, which can only be attracted by a central pixel. Based on this approach, a mere maximum of eight neighboring pixels can be selected for calculating of the attraction value. In the mentioned model, other pixels are supposed to be far from the central pixel to receive any attraction. In the present study by using a sub-pixel attraction model, the spatial resolution of a DEM was increased. The design of the algorithm is accomplished by using a DEM with a spatial resolution of 30 m (the Advanced Space borne Thermal Emission and Reflection Radiometer; (ASTER and a 90 m (the Shuttle Radar Topography Mission; (SRTM. In the attraction model, scale factors of (S = 2, S = 3, and S = 4 with two neighboring methods of touching (T = 1 and quadrant (T = 2 are applied to the DEMs by using MATLAB software. The algorithm is evaluated by taking the best advantages of 487 sample points, which are measured by surveyors. The spatial attraction model with scale factor of (S = 2 gives better results compared to those scale factors which are greater than 2. Besides, the touching neighborhood method is turned to be more accurate than the quadrant method. In fact, dividing each pixel into more than two sub-pixels decreases the accuracy of the resulted DEM. On the other hand, in these cases DEM, is itself in charge of increasing the value of root-mean-square error (RMSE and shows that attraction models could not be used for S which is greater than 2. Thus considering results, the proposed model is highly capable of

The spatial limitations of current neutral models of biodiversity.

Directory of Open Access Journals (Sweden)

Rampal S Etienne

Full Text Available The unified neutral theory of biodiversity and biogeography is increasingly accepted as an informative null model of community composition and dynamics. It has successfully produced macro-ecological patterns such as species-area relationships and species abundance distributions. However, the models employed make many unrealistic auxiliary assumptions. For example, the popular spatially implicit version assumes a local plot exchanging migrants with a large panmictic regional source pool. This simple structure allows rigorous testing of its fit to data. In contrast, spatially explicit models assume that offspring disperse only limited distances from their parents, but one cannot as yet test the significance of their fit to data. Here we compare the spatially explicit and the spatially implicit model, fitting the most-used implicit model (with two levels, local and regional to data simulated by the most-used spatially explicit model (where offspring are distributed about their parent on a grid according to either a radially symmetric Gaussian or a 'fat-tailed' distribution. Based on these fits, we express spatially implicit parameters in terms of spatially explicit parameters. This suggests how we may obtain estimates of spatially explicit parameters from spatially implicit ones. The relationship between these parameters, however, makes no intuitive sense. Furthermore, the spatially implicit model usually fits observed species-abundance distributions better than those calculated from the spatially explicit model's simulated data. Current spatially explicit neutral models therefore have limited descriptive power. However, our results suggest that a fatter tail of the dispersal kernel seems to improve the fit, suggesting that dispersal kernels with even fatter tails should be studied in future. We conclude that more advanced spatially explicit models and tools to analyze them need to be developed.

Evaluating spatial patterns in hydrological modelling

DEFF Research Database (Denmark)

Koch, Julian

the contiguous United Sates (10^6 km2). To this end, the thesis at hand applies a set of spatial performance metrics on various hydrological variables, namely land-surface-temperature (LST), evapotranspiration (ET) and soil moisture. The inspiration for the applied metrics is found in related fields...... is not fully exploited by current modelling frameworks due to the lack of suitable spatial performance metrics. Furthermore, the traditional model evaluation using discharge is found unsuitable to lay confidence on the predicted catchment inherent spatial variability of hydrological processes in a fully...

The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models

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Koch, Julian; Cüneyd Demirel, Mehmet; Stisen, Simon

2018-05-01

The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.

Spatially explicit models for inference about density in unmarked or partially marked populations

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Chandler, Richard B.; Royle, J. Andrew

2013-01-01

Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5–10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19–1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating

Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation

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Chonggang Xu; Hong S. He; Yuanman Hu; Yu Chang; Xiuzhen Li; Rencang Bu

2005-01-01

Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great...

Redox-dependent spatially resolved electrochemistry at graphene and graphite step edges.

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Güell, Aleix G; Cuharuc, Anatolii S; Kim, Yang-Rae; Zhang, Guohui; Tan, Sze-yin; Ebejer, Neil; Unwin, Patrick R

2015-04-28

The electrochemical (EC) behavior of mechanically exfoliated graphene and highly oriented pyrolytic graphite (HOPG) is studied at high spatial resolution in aqueous solutions using Ru(NH3)6(3+/2+) as a redox probe whose standard potential sits close to the intrinsic Fermi level of graphene and graphite. When scanning electrochemical cell microscopy (SECCM) data are coupled with that from complementary techniques (AFM, micro-Raman) applied to the same sample area, different time-dependent EC activity between the basal planes and step edges is revealed. In contrast, other redox couples (ferrocene derivatives) whose potential is further removed from the intrinsic Fermi level of graphene and graphite show uniform and high activity (close to diffusion-control). Macroscopic voltammetric measurements in different environments reveal that the time-dependent behavior after HOPG cleavage, peculiar to Ru(NH3)6(3+/2+), is not associated particularly with any surface contaminants but is reasonably attributed to the spontaneous delamination of the HOPG with time to create partially coupled graphene layers, further supported by conductive AFM measurements. This process has a major impact on the density of states of graphene and graphite edges, particularly at the intrinsic Fermi level to which Ru(NH3)6(3+/2+) is most sensitive. Through the use of an improved voltammetric mode of SECCM, we produce movies of potential-resolved and spatially resolved HOPG activity, revealing how enhanced activity at step edges is a subtle effect for Ru(NH3)6(3+/2+). These latter studies allow us to propose a microscopic model to interpret the EC response of graphene (basal plane and edges) and aged HOPG considering the nontrivial electronic band structure.

Measuring the value of air quality: application of the spatial hedonic model.

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Kim, Seung Gyu; Cho, Seong-Hoon; Lambert, Dayton M; Roberts, Roland K

2010-03-01

This study applies a hedonic model to assess the economic benefits of air quality improvement following the 1990 Clean Air Act Amendment at the county level in the lower 48 United States. An instrumental variable approach that combines geographically weighted regression and spatial autoregression methods (GWR-SEM) is adopted to simultaneously account for spatial heterogeneity and spatial autocorrelation. SEM mitigates spatial dependency while GWR addresses spatial heterogeneity by allowing response coefficients to vary across observations. Positive amenity values of improved air quality are found in four major clusters: (1) in East Kentucky and most of Georgia around the Southern Appalachian area; (2) in a few counties in Illinois; (3) on the border of Oklahoma and Kansas, on the border of Kansas and Nebraska, and in east Texas; and (4) in a few counties in Montana. Clusters of significant positive amenity values may exist because of a combination of intense air pollution and consumer awareness of diminishing air quality.

Using IBMs to Investigate Spatially-dependent Processes in Landscape Genetics Theory

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Much of landscape and conservation genetics theory has been derived using non-spatialmathematical models. Here, we use a mechanistic, spatially-explicit, eco-evolutionary IBM to examine the utility of this theoretical framework in landscapes with spatial structure. Our analysis...

Hierarchical modeling and analysis for spatial data

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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

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

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Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

2010-12-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies

Objective Tuning of Model Parameters in CAM5 Across Different Spatial Resolutions

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Bulaevskaya, V.; Lucas, D. D.

2014-12-01

Parameterizations of physical processes in climate models are highly dependent on the spatial and temporal resolution and must be tuned for each resolution under consideration. At high spatial resolutions, objective methods for parameter tuning are computationally prohibitive. Our work has focused on calibrating parameters in the Community Atmosphere Model 5 (CAM5) for three spatial resolutions: 1, 2, and 4 degrees. Using perturbed-parameter ensembles and uncertainty quantification methodology, we have identified input parameters that minimize discrepancies of energy fluxes simulated by CAM5 across the three resolutions and with respect to satellite observations. We are also beginning to exploit the parameter-resolution relationships to objectively tune parameters in a high-resolution version of CAM5 by leveraging cheaper, low-resolution simulations and statistical models. We will present our approach to multi-resolution climate model parameter tuning, as well as the key findings. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 and was supported from the DOE Office of Science through the Scientific Discovery Through Advanced Computing (SciDAC) project on Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System.

A multi-scale spatial model of hepatitis-B viral dynamics.

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Quentin Cangelosi

Full Text Available Chronic hepatitis B viral infection (HBV afflicts around 250 million individuals globally and few options for treatment exist. Once infected, the virus entrenches itself in the liver with a notoriously resilient colonisation of viral DNA (covalently-closed circular DNA, cccDNA. The majority of infections are cleared, yet we do not understand why 5% of adult immune responses fail leading to the chronic state with its collateral morbid effects such as cirrhosis and eventual hepatic carcinoma. The liver environment exhibits particularly complex spatial structures for metabolic processing and corresponding distributions of nutrients and transporters that may influence successful HBV entrenchment. We assembled a multi-scaled mathematical model of the fundamental hepatic processing unit, the sinusoid, into a whole-liver representation to investigate the impact of this intrinsic spatial heterogeneity on the HBV dynamic. Our results suggest HBV may be exploiting spatial aspects of the liver environment. We distributed increased HBV replication rates coincident with elevated levels of nutrients in the sinusoid entry point (the periportal region in tandem with similar distributions of hepatocyte transporters key to HBV invasion (e.g., the sodium-taurocholate cotransporting polypeptide or NTCP, or immune system activity. According to our results, such co-alignment of spatial distributions may contribute to persistence of HBV infections, depending on spatial distributions and intensity of immune response as well. Moreover, inspired by previous HBV models and experimentalist suggestions of extra-hepatic HBV replication, we tested in our model influence of HBV blood replication and observe an overall nominal effect on persistent liver infection. Regardless, we confirm prior results showing a solo cccDNA is sufficient to re-infect an entire liver, with corresponding concerns for transplantation and treatment.

The mARM3D spatially distributed soil evolution model: Three-dimensional model framework and analysis of hillslope and landform responses

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Cohen, Sagy; Willgoose, Garry; Hancock, Greg

2010-10-01

We present a three-dimensional landscape-pedogenesis model, mARM3D (matrices ARMOUR 3D), which simulates soil evolution as a function of erosion and pedogenic processes. The model simulates the discretized soil profile for points on a spatial grid. The approach, using transition matrices, is computationally efficient and allows the simulation of large-scale spatial coupling and long-term soil evolution. We study the effect of the depth-dependent soil-weathering rate (i.e., the rate of soil particle breakdown) and bedrock-lowering rate (i.e., the rate of conversion of bedrock to soil). The difference in depth-dependent weathering functions has a significant effect on the in-profile soil properties through depth, specifically particle size grading. However, the depth dependency has a relatively minor effect on the surface properties of the soil profile, with all weathering functions generating very similar surface properties. The surface properties were a function of the cumulative amount of weathering (i.e., the integral of the weathering function over exhumation) with finer surface grading for higher weathering rates. Soil thickness could be estimated without explicitly modeling soil thickness. Thickness was negatively correlated with surface median grain size. As thickness decreases, the surface grading coarsens. This was driven by surface erosion, where as surface grading coarsens, erosion decreases and the soil deepens. Weathering and erosion interact to spatially organize the surface soil grading with a log-log relationship between surface grading, contributing area, and local slope. This relationship was independent of the weathering function. This relationship might be useful for the spatial description of soil properties in digital soil mapping.

Latent spatial models and sampling design for landscape genetics

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Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

2016-01-01

We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

Empirical spatial econometric modelling of small scale neighbourhood

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Gerkman, Linda

2012-07-01

The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.

The spatial impact of neighbouring on the exports activities of COMESA countries by using spatial panel models

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Hamzalouh, L.; Ismail, M. T.; Rahman, R. A.

2017-09-01

In this paper, spatial panel models were used and the method for selecting the best model amongst the spatial fixed effects model and the spatial random effects model to estimate the fitting model by using the robust Hausman test for analysis of the exports pattern of the Common Market for Eastern and Southern African (COMESA) countries. And examine the effects of the interactions of the economic statistic of explanatory variables on the exports of the COMESA. Results indicated that the spatial Durbin model with fixed effects specification should be tested and considered in most cases of this study. After that, the direct and indirect effects among COMESA regions were assessed, and the role of indirect spatial effects in estimating exports was empirically demonstrated. Regarding originality and research value, and to the best of the authors’ knowledge, this is the first attempt to examine exports between COMESA and its member countries through spatial panel models using XSMLE, which is a new command for spatial analysis using STATA.

Multidimensional extended spatial evolutionary games.

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Krześlak, Michał; Świerniak, Andrzej

2016-02-01

The goal of this paper is to study the classical hawk-dove model using mixed spatial evolutionary games (MSEG). In these games, played on a lattice, an additional spatial layer is introduced for dependence on more complex parameters and simulation of changes in the environment. Furthermore, diverse polymorphic equilibrium points dependent on cell reproduction, model parameters, and their simulation are discussed. Our analysis demonstrates the sensitivity properties of MSEGs and possibilities for further development. We discuss applications of MSEGs, particularly algorithms for modelling cell interactions during the development of tumours. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effect of grain morphology on gas bubble swelling in UMo fuels – A 3D microstructure dependent Booth model

Energy Technology Data Exchange (ETDEWEB)

Hu, Shenyang, E-mail: shenyang.hu@pnnl.gov; Burkes, Douglas; Lavender, Curt A.; Joshi, Vineet

2016-11-15

A three dimensional microstructure dependent swelling model is developed for studying the fission gas swelling kinetics in irradiated nuclear fuels. The model is extended from the Booth model [1] in order to investigate the effect of heterogeneous microstructures on gas bubble swelling kinetics. As an application of the model, the effect of grain morphology, fission gas diffusivity, and spatially dependent fission rate on swelling kinetics are simulated in UMo fuels. It is found that the decrease of grain size, the increase of grain aspect ratio for the grain having the same volume, and the increase of fission gas diffusivity (fission rate) cause the increase of swelling kinetics. Other heterogeneities such as second phases and spatially dependent thermodynamic properties including diffusivity of fission gas, sink and source strength of defects could be naturally integrated into the model to enhance the model capability.

Three-dimensional solutions in media with spatial dependence of nonlinear refractive index

International Nuclear Information System (INIS)

Kovachev, L.M.; Kaymakanova, N.I.; Dakova, D.Y.; Pavlov, L.I.; Donev, S.G.; Pavlov, R.L.

2004-01-01

We investigate a nonparaxial vector generalization of the scalar 3D+1 Nonlinear Schrodinger Equation (NSE). Exact analytical 3D+1 soliton solutions are obtained for the first time in media of spatial dependence of the nonlinear refractive index

Location Aggregation of Spatial Population CTMC Models

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Luca Bortolussi

2016-10-01

Full Text Available In this paper we focus on spatial Markov population models, describing the stochastic evolution of populations of agents, explicitly modelling their spatial distribution, representing space as a discrete, finite graph. More specifically, we present a heuristic approach to aggregating spatial locations, which is designed to preserve the dynamical behaviour of the model whilst reducing the computational cost of analysis. Our approach combines stochastic approximation ideas (moment closure, linear noise, with computational statistics (spectral clustering to obtain an efficient aggregation, which is experimentally shown to be reasonably accurate on two case studies: an instance of epidemic spreading and a London bike sharing scenario.

Spatial extrapolation of light use efficiency model parameters to predict gross primary production

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Karsten Schulz

2011-12-01

Full Text Available To capture the spatial and temporal variability of the gross primary production as a key component of the global carbon cycle, the light use efficiency modeling approach in combination with remote sensing data has shown to be well suited. Typically, the model parameters, such as the maximum light use efficiency, are either set to a universal constant or to land class dependent values stored in look-up tables. In this study, we employ the machine learning technique support vector regression to explicitly relate the model parameters of a light use efficiency model calibrated at several FLUXNET sites to site-specific characteristics obtained by meteorological measurements, ecological estimations and remote sensing data. A feature selection algorithm extracts the relevant site characteristics in a cross-validation, and leads to an individual set of characteristic attributes for each parameter. With this set of attributes, the model parameters can be estimated at sites where a parameter calibration is not possible due to the absence of eddy covariance flux measurement data. This will finally allow a spatially continuous model application. The performance of the spatial extrapolation scheme is evaluated with a cross-validation approach, which shows the methodology to be well suited to recapture the variability of gross primary production across the study sites.

How does spatial study design influence density estimates from spatial capture-recapture models?

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Rahel Sollmann

Full Text Available When estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (SCR models present an advance over non-spatial models by accounting for individual movement. While these models should be more robust to changes in trapping designs, they have not been well tested. Here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for SCR models. We analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions. To see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the South of the array. Additionally, we simulated and analysed data under a suite of trap designs and home range sizes. In the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. Black bear density was approximately 10 individuals per 100 km(2. Our simulation study showed that SCR models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. SCR models performed well across a range of spatial trap setups and animal movements. Contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. This renders SCR models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species.

Stochastic Spatial Models in Ecology: A Statistical Physics Approach

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Pigolotti, Simone; Cencini, Massimo; Molina, Daniel; Muñoz, Miguel A.

2017-11-01

Ecosystems display a complex spatial organization. Ecologists have long tried to characterize them by looking at how different measures of biodiversity change across spatial scales. Ecological neutral theory has provided simple predictions accounting for general empirical patterns in communities of competing species. However, while neutral theory in well-mixed ecosystems is mathematically well understood, spatial models still present several open problems, limiting the quantitative understanding of spatial biodiversity. In this review, we discuss the state of the art in spatial neutral theory. We emphasize the connection between spatial ecological models and the physics of non-equilibrium phase transitions and how concepts developed in statistical physics translate in population dynamics, and vice versa. We focus on non-trivial scaling laws arising at the critical dimension D = 2 of spatial neutral models, and their relevance for biological populations inhabiting two-dimensional environments. We conclude by discussing models incorporating non-neutral effects in the form of spatial and temporal disorder, and analyze how their predictions deviate from those of purely neutral theories.

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

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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.

Multilevel Modelling with Spatial Interaction Effects with Application to an Emerging Land Market in Beijing, China.

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Guanpeng Dong

Full Text Available This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership structure--for example, census zones into regions but the actual locations of the units and the distances between them are not directly considered: what matters is the groupings but not how close together any two units are within those groupings. As a consequence, spatial interaction effects are neither modelled nor measured, confounding group effects (understood as some sort of contextual effect that acts 'top down' upon members of a group with proximity effects (some sort of joint dependency that emerges between neighbours. To deal with this, we incorporate spatial simultaneous autoregressive processes into both the outcome variable and the higher level residuals. To assess the performance of the proposed method and the classic multilevel model, a series of Monte Carlo simulations are conducted. The results show that the proposed method performs well in retrieving the true model parameters whereas the classic multilevel model provides biased and inefficient parameter estimation in the presence of spatial interactions. An important implication of the study is to be cautious of an apparent neighbourhood effect in terms of both its magnitude and statistical significance if spatial interaction effects at a lower level are suspected. Applying the new approach to a two-level land price data set for Beijing, China, we find significant spatial interactions at both the land parcel and district levels.

Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography

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Muller, Leah; Hamilton, Liberty S.; Edwards, Erik; Bouchard, Kristofer E.; Chang, Edward F.

2016-10-01

Objective. Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram. Approach. Five subjects with high-density (4 mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient. Main results. The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150 Hz) are largely uncorrelated (<90%) at 4 mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4 mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies. Significance. Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and

A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model

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S. Kolberg

2006-01-01

Full Text Available A method for assimilating remotely sensed snow covered area (SCA into the snow subroutine of a grid distributed precipitation-runoff model (PRM is presented. The PRM is assumed to simulate the snow state in each grid cell by a snow depletion curve (SDC, which relates that cell's SCA to its snow cover mass balance. The assimilation is based on Bayes' theorem, which requires a joint prior distribution of the SDC variables in all the grid cells. In this paper we propose a spatial model for this prior distribution, and include similarities and dependencies among the grid cells. Used to represent the PRM simulated snow cover state, our joint prior model regards two elevation gradients and a degree-day factor as global variables, rather than describing their effect separately for each cell. This transformation results in smooth normalised surfaces for the two related mass balance variables, supporting a strong inter-cell dependency in their joint prior model. The global features and spatial interdependency in the prior model cause each SCA observation to provide information for many grid cells. The spatial approach similarly facilitates the utilisation of observed discharge. Assimilation of SCA data using the proposed spatial model is evaluated in a 2400 km2 mountainous region in central Norway (61° N, 9° E, based on two Landsat 7 ETM+ images generalized to 1 km2 resolution. An image acquired on 11 May, a week before the peak flood, removes 78% of the variance in the remaining snow storage. Even an image from 4 May, less than a week after the melt onset, reduces this variance by 53%. These results are largely improved compared to a cell-by-cell independent assimilation routine previously reported. Including observed discharge in the updating information improves the 4 May results, but has weak effect on 11 May. Estimated elevation gradients are shown to be sensitive to informational deficits occurring at high altitude, where snowmelt has not started

The Effect of Reversible Abolition of Basolateral Amygdala on Hippocampal Dependent Spatial Memory Processes in Mice

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A Rashidy-Pour

2004-04-01

Full Text Available Introduction: Many evidences have suggested that the Basolateral Amygdala (BLA are probably involved in emotional learning and modulation of spatial memory processes. The aim of this present study was assessment of the effect of reversible abolition of BLA on spatial memory processes in a place avoidance learning model in a stable environment. Methods and Materials: Long-Evans strain rats (280-320 gr. were selected and cannulae aimed at the BLA were surgically implanted bilaterally. The mice were trained to avoid a 60° segment of the arena by punishing with a mild foot shock upon entering the area. The punished sector was defined by room cues during the place avoidance training, which occurred in a single 30-min session and the avoidance memory was assessed during a 30-min extinction trial after 24 hours. The time of the first entry and the number of entrances into the punished sector during extinction were used to measure the place avoidance memory. Bilateral injections of Tetrodotoxin (5ng/0.6ml per side were used to inactivate the BLA 60 min before acquisition, immediately, 60 and 120 min after training, or 60 min before the retrieval test. Control mice were injected saline at the same time. Results : The results indicated that acquisition, consolidation (immediately, 60 min after training and retrieval of spatial memory in stable arena were impaired (p0.05. Conclusion: We conclude that the Basolateral Amygdala (BLA modulate spatial memory processes in place avoidance learning model in stable arena and this effect in regard to consolidation is time dependent.

A composite likelihood approach for spatially correlated survival data

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Paik, Jane; Ying, Zhiliang

2013-01-01

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory. PMID:24223450

A composite likelihood approach for spatially correlated survival data.

Science.gov (United States)

Paik, Jane; Ying, Zhiliang

2013-01-01

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.

Time-dependent density functional theory (TD-DFT) coupled with reference interaction site model self-consistent field explicitly including spatial electron density distribution (RISM-SCF-SEDD)

Energy Technology Data Exchange (ETDEWEB)

Yokogawa, D., E-mail: d.yokogawa@chem.nagoya-u.ac.jp [Department of Chemistry, Graduate School of Science, Nagoya University, Chikusa, Nagoya 464-8602 (Japan); Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Chikusa, Nagoya 464-8602 (Japan)

2016-09-07

Theoretical approach to design bright bio-imaging molecules is one of the most progressing ones. However, because of the system size and computational accuracy, the number of theoretical studies is limited to our knowledge. To overcome the difficulties, we developed a new method based on reference interaction site model self-consistent field explicitly including spatial electron density distribution and time-dependent density functional theory. We applied it to the calculation of indole and 5-cyanoindole at ground and excited states in gas and solution phases. The changes in the optimized geometries were clearly explained with resonance structures and the Stokes shift was correctly reproduced.

A Spatial Model for the Instantaneous Estimation of Wind Power at a Large Number of Unobserved Sites

DEFF Research Database (Denmark)

Lenzi, Amanda; Guillot, Gilles; Pinson, Pierre

2015-01-01

We propose a hierarchical Bayesian spatial model to obtain predictive densities of wind power at a set of un-monitored locations. The model consists of a mixture of Gamma density for the non-zero values and degenerated distributions at zero. The spatial dependence is described through a common...... Gaussian random field with a Matérn covariance. For inference and prediction, we use the GMRF-SPDE approximation implemented in the R-INLA package. We showcase the method outlined here on data for 336 wind farms located in Denmark. We test the predictions derived from our method with model-diagnostic tools...

Spatial generalised linear mixed models based on distances.

Science.gov (United States)

Melo, Oscar O; Mateu, Jorge; Melo, Carlos E

2016-10-01

Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.

Spatially Distributed, Coupled Modeling of Plant Growth, Nitrogen and Water Fluxes in an Alpine Catchment

Science.gov (United States)

Schneider, K.

2001-12-01

Carbon, water and nitrogen fluxes are closely coupled. They interact and have many feedbacks. Human interference, in particular through land use management and global change strongly modifies these fluxes. Increasing demands and conflicting interests result in an increasing need for regulation targeting different aspects of the system. Without being their main target, many of these measures directly affect water quantity, quality and availability. Improved management and planning of our water resources requires the development of integrated tools, in particular since interactions of the involved environmental and social systems often lead to unexpected or adverse results. To investigate the effect of plant growth, land use management and global change on water fluxes and quality, the PROcess oriented Modular EnvironmenT and Vegetation Model (PROMET-V) was developed. PROMET-V models the spatial patterns and temporal course of water, carbon and nitrogen fluxes using process oriented and mechanistic model components. The hydrological model is based on the Penman-Monteith approach, it uses a plant-physiological model to calculate the canopy conductance, and a multi-layer soil water model. Plant growth for different vegetation is modelled by calculating canopy photosynthesis, respiration, phenology and allocation. Plant growth and water fluxes are coupled directly through photosynthesis and transpiration. Many indirect feedbacks and interactions occur due to their mutual dependency upon leaf area, root distribution, water and nutrient availability for instance. PROMET-V calculates nitrogen fluxes and transformations. The time step used depends upon the modelled process and varies from 1 hour to 1 day. The kernel model is integrated in a raster GIS system for spatially distributed modelling. PROMET-V was tested in a pre-alpine landscape (Ammer river, 709 km**2, located in Southern Germany) which is characterized by small scale spatial heterogeneities of climate, soil and

Spatially heterogeneous dynamics investigated via a time-dependent four-point density correlation function

DEFF Research Database (Denmark)

Lacevic, N.; Starr, F. W.; Schrøder, Thomas

2003-01-01

correlation function g4(r,t) and corresponding "structure factor" S4(q,t) which measure the spatial correlations between the local liquid density at two points in space, each at two different times, and so are sensitive to dynamical heterogeneity. We study g4(r,t) and S4(q,t) via molecular dynamics......Relaxation in supercooled liquids above their glass transition and below the onset temperature of "slow" dynamics involves the correlated motion of neighboring particles. This correlated motion results in the appearance of spatially heterogeneous dynamics or "dynamical heterogeneity." Traditional...... two-point time-dependent density correlation functions, while providing information about the transient "caging" of particles on cooling, are unable to provide sufficiently detailed information about correlated motion and dynamical heterogeneity. Here, we study a four-point, time-dependent density...

A GIS-based time-dependent seismic source modeling of Northern Iran

Science.gov (United States)

Hashemi, Mahdi; Alesheikh, Ali Asghar; Zolfaghari, Mohammad Reza

2017-01-01

The first step in any seismic hazard study is the definition of seismogenic sources and the estimation of magnitude-frequency relationships for each source. There is as yet no standard methodology for source modeling and many researchers have worked on this topic. This study is an effort to define linear and area seismic sources for Northern Iran. The linear or fault sources are developed based on tectonic features and characteristic earthquakes while the area sources are developed based on spatial distribution of small to moderate earthquakes. Time-dependent recurrence relationships are developed for fault sources using renewal approach while time-independent frequency-magnitude relationships are proposed for area sources based on Poisson process. GIS functionalities are used in this study to introduce and incorporate spatial-temporal and geostatistical indices in delineating area seismic sources. The proposed methodology is used to model seismic sources for an area of about 500 by 400 square kilometers around Tehran. Previous researches and reports are studied to compile an earthquake/fault catalog that is as complete as possible. All events are transformed to uniform magnitude scale; duplicate events and dependent shocks are removed. Completeness and time distribution of the compiled catalog is taken into account. The proposed area and linear seismic sources in conjunction with defined recurrence relationships can be used to develop time-dependent probabilistic seismic hazard analysis of Northern Iran.

A Spatial Model of the Mere Exposure Effect.

Science.gov (United States)

Fink, Edward L.; And Others

1989-01-01

Uses a spatial model to examine the relationship between stimulus exposure, cognition, and affect. Notes that this model accounts for cognitive changes that a stimulus may acquire as a result of exposure. Concludes that the spatial model is useful for evaluating the mere exposure effect and that affective change does not require cognitive change.…

Model for Atmospheric Propagation of Spatially Combined Laser Beams

Science.gov (United States)

2016-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS by Kum Leong Lee September...MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS 5. FUNDING NUMBERS 6. AUTHOR(S) Kum Leong Lee 7. PERFORMING ORGANIZATION NAME(S) AND...BLANK ii Approved for public release. Distribution is unlimited. MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS Kum Leong Lee

Housing price prediction: parametric versus semi-parametric spatial hedonic models

Science.gov (United States)

Montero, José-María; Mínguez, Román; Fernández-Avilés, Gema

2018-01-01

House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric and semi-parametric spatial hedonic model variants that account for spatial autocorrelation, spatial heterogeneity and (smooth and nonparametrically specified) nonlinearities using penalized splines methodology. The models are represented as a mixed model that allow for the estimation of the smoothing parameters along with the other parameters of the model. To assess the out-of-sample performance of the models, the paper uses a database containing the price and characteristics of 10,512 homes in Madrid, Spain (Q1 2010). The results obtained suggest that the nonlinear models accounting for spatial heterogeneity and flexible nonlinear relationships between some of the individual or areal characteristics of the houses and their prices are the best strategies for house price prediction.

Developing a modelling for the spatial data infrastructure

CSIR Research Space (South Africa)

Hjelmager, J

2005-07-01

Full Text Available The Commission on Spatial Data Standards of the International Cartographic Association (ICA) is working on defining spatial models and technical characteristics of a Spatial Data Infrastructure (SDI). To date, this work has been restricted...

Modeling of spatial dependence in wind power forecast uncertainty

DEFF Research Database (Denmark)

Papaefthymiou, George; Pinson, Pierre

2008-01-01

It is recognized today that short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. When considering different areas covering a region, they are produced independently, and thus...... neglect the interdependence structure of prediction errors, induced by movement of meteorological fronts, or more generally by inertia of meteorological systems. This issue is addressed here by describing a method that permits to generate interdependent scenarios of wind generation for spatially...... distributed wind power production for specific look-ahead times. The approach is applied to the case of western Denmark split in 5 zones, for a total capacity of more than 2.1 GW. The interest of the methodology for improving the resolution of probabilistic forecasts, for a range of decision-making problems...

Acute effects of alcohol on intrusive memory development and viewpoint dependence in spatial memory support a dual representation model.

Science.gov (United States)

Bisby, James A; King, John A; Brewin, Chris R; Burgess, Neil; Curran, H Valerie

2010-08-01

A dual representation model of intrusive memory proposes that personally experienced events give rise to two types of representation: an image-based, egocentric representation based on sensory-perceptual features; and a more abstract, allocentric representation that incorporates spatiotemporal context. The model proposes that intrusions reflect involuntary reactivation of egocentric representations in the absence of a corresponding allocentric representation. We tested the model by investigating the effect of alcohol on intrusive memories and, concurrently, on egocentric and allocentric spatial memory. With a double-blind independent group design participants were administered alcohol (.4 or .8 g/kg) or placebo. A virtual environment was used to present objects and test recognition memory from the same viewpoint as presentation (tapping egocentric memory) or a shifted viewpoint (tapping allocentric memory). Participants were also exposed to a trauma video and required to detail intrusive memories for 7 days, after which explicit memory was assessed. There was a selective impairment of shifted-view recognition after the low dose of alcohol, whereas the high dose induced a global impairment in same-view and shifted-view conditions. Alcohol showed a dose-dependent inverted "U"-shaped effect on intrusions, with only the low dose increasing the number of intrusions, replicating previous work. When same-view recognition was intact, decrements in shifted-view recognition were associated with increases in intrusions. The differential effect of alcohol on intrusive memories and on same/shifted-view recognition support a dual representation model in which intrusions might reflect an imbalance between two types of memory representation. These findings highlight important clinical implications, given alcohol's involvement in real-life trauma. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Spatial demographic models to inform conservation planning of golden eagles in renewable energy landscapes

Science.gov (United States)

Wiens, J. David; Schumaker, Nathan H.; Inman, Richard D.; Esque, Todd C.; Longshore, Kathleen M.; Nussear, Kenneth E

2017-01-01

Spatial demographic models can help guide monitoring and management activities targeting at-risk species, even in cases where baseline data are lacking. Here, we provide an example of how site-specific changes in land use and anthropogenic stressors can be incorporated into a spatial demographic model to investigate effects on population dynamics of Golden Eagles (Aquila chrysaetos). Our study focused on a population of Golden Eagles exposed to risks associated with rapid increases in renewable energy development in southern California, U.S.A. We developed a spatially explicit, individual-based simulation model that integrated empirical data on demography of Golden Eagles with spatial data on the arrangement of nesting habitats, prey resources, and planned renewable energy development sites. Our model permitted simulated eagles of different stage-classes to disperse, establish home ranges, acquire prey resources, prospect for breeding sites, and reproduce. The distribution of nesting habitats, prey resources, and threats within each individual's home range influenced movement, reproduction, and survival. We used our model to explore potential effects of alternative disturbance scenarios, and proposed conservation strategies, on the future distribution and abundance of Golden Eagles in the study region. Results from our simulations suggest that probable increases in mortality associated with renewable energy infrastructure (e.g., collisions with wind turbines and vehicles, electrocution on power poles) could have negative consequences for population trajectories, but that site-specific conservation actions could reduce the magnitude of negative effects. Our study demonstrates the use of a flexible and expandable modeling framework to incorporate spatially dependent processes when determining relative effects of proposed management options to Golden Eagles and their habitats.

A spatial model of mosquito host-seeking behavior.

Directory of Open Access Journals (Sweden)

Bree Cummins

Full Text Available Mosquito host-seeking behavior and heterogeneity in host distribution are important factors in predicting the transmission dynamics of mosquito-borne infections such as dengue fever, malaria, chikungunya, and West Nile virus. We develop and analyze a new mathematical model to describe the effect of spatial heterogeneity on the contact rate between mosquito vectors and hosts. The model includes odor plumes generated by spatially distributed hosts, wind velocity, and mosquito behavior based on both the prevailing wind and the odor plume. On a spatial scale of meters and a time scale of minutes, we compare the effectiveness of different plume-finding and plume-tracking strategies that mosquitoes could use to locate a host. The results show that two different models of chemotaxis are capable of producing comparable results given appropriate parameter choices and that host finding is optimized by a strategy of flying across the wind until the odor plume is intercepted. We also assess the impact of changing the level of host aggregation on mosquito host-finding success near the end of the host-seeking flight. When clusters of hosts are more tightly associated on smaller patches, the odor plume is narrower and the biting rate per host is decreased. For two host groups of unequal number but equal spatial density, the biting rate per host is lower in the group with more individuals, indicative of an attack abatement effect of host aggregation. We discuss how this approach could assist parameter choices in compartmental models that do not explicitly model the spatial arrangement of individuals and how the model could address larger spatial scales and other probability models for mosquito behavior, such as Lévy distributions.

Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor

Directory of Open Access Journals (Sweden)

Jae-Han Park

2012-06-01

Full Text Available This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

Spatial uncertainty model for visual features using a Kinect™ sensor.

Science.gov (United States)

Park, Jae-Han; Shin, Yong-Deuk; Bae, Ji-Hun; Baeg, Moon-Hong

2012-01-01

This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

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

Infusing considerations of trophic dependencies into species distribution modelling.

Science.gov (United States)

Trainor, Anne M; Schmitz, Oswald J

2014-12-01

Community ecology involves studying the interdependence of species with each other and their environment to predict their geographical distribution and abundance. Modern species distribution analyses characterise species-environment dependency well, but offer only crude approximations of species interdependency. Typically, the dependency between focal species and other species is characterised using other species' point occurrences as spatial covariates to constrain the focal species' predicted range. This implicitly assumes that the strength of interdependency is homogeneous across space, which is not generally supported by analyses of species interactions. This discrepancy has an important bearing on the accuracy of inferences about habitat suitability for species. We introduce a framework that integrates principles from consumer-resource analyses, resource selection theory and species distribution modelling to enhance quantitative prediction of species geographical distributions. We show how to apply the framework using a case study of lynx and snowshoe hare interactions with each other and their environment. The analysis shows how the framework offers a spatially refined understanding of species distribution that is sensitive to nuances in biophysical attributes of the environment that determine the location and strength of species interactions. © 2014 John Wiley & Sons Ltd/CNRS.

A model relating Eulerian spatial and temporal velocity correlations

Science.gov (United States)

Cholemari, Murali R.; Arakeri, Jaywant H.

2006-03-01

In this paper we propose a model to relate Eulerian spatial and temporal velocity autocorrelations in homogeneous, isotropic and stationary turbulence. We model the decorrelation as the eddies of various scales becoming decorrelated. This enables us to connect the spatial and temporal separations required for a certain decorrelation through the ‘eddy scale’. Given either the spatial or the temporal velocity correlation, we obtain the ‘eddy scale’ and the rate at which the decorrelation proceeds. This leads to a spatial separation from the temporal correlation and a temporal separation from the spatial correlation, at any given value of the correlation relating the two correlations. We test the model using experimental data from a stationary axisymmetric turbulent flow with homogeneity along the axis.

Langevin Dynamics with Spatial Correlations as a Model for Electron-Phonon Coupling

Science.gov (United States)

Tamm, A.; Caro, M.; Caro, A.; Samolyuk, G.; Klintenberg, M.; Correa, A. A.

2018-05-01

Stochastic Langevin dynamics has been traditionally used as a tool to describe nonequilibrium processes. When utilized in systems with collective modes, traditional Langevin dynamics relaxes all modes indiscriminately, regardless of their wavelength. We propose a generalization of Langevin dynamics that can capture a differential coupling between collective modes and the bath, by introducing spatial correlations in the random forces. This allows modeling the electronic subsystem in a metal as a generalized Langevin bath endowed with a concept of locality, greatly improving the capabilities of the two-temperature model. The specific form proposed here for the spatial correlations produces a physical wave-vector and polarization dependency of the relaxation produced by the electron-phonon coupling in a solid. We show that the resulting model can be used for describing the path to equilibration of ions and electrons and also as a thermostat to sample the equilibrium canonical ensemble. By extension, the family of models presented here can be applied in general to any dense system, solids, alloys, and dense plasmas. As an example, we apply the model to study the nonequilibrium dynamics of an electron-ion two-temperature Ni crystal.

Social networks and trade of services: modelling interregional flows with spatial and network autocorrelation effects

Science.gov (United States)

de la Mata, Tamara; Llano, Carlos

2013-07-01

Recent literature on border effect has fostered research on informal barriers to trade and the role played by network dependencies. In relation to social networks, it has been shown that intensity of trade in goods is positively correlated with migration flows between pairs of countries/regions. In this article, we investigate whether such a relation also holds for interregional trade of services. We also consider whether interregional trade flows in services linked with tourism exhibit spatial and/or social network dependence. Conventional empirical gravity models assume the magnitude of bilateral flows between regions is independent of flows to/from regions located nearby in space, or flows to/from regions related through social/cultural/ethic network connections. With this aim, we provide estimates from a set of gravity models showing evidence of statistically significant spatial and network (demographic) dependence in the bilateral flows of the trade of services considered. The analysis has been applied to the Spanish intra- and interregional monetary flows of services from the accommodation, restaurants and travel agencies for the period 2000-2009, using alternative datasets for the migration stocks and definitions of network effects.

Implementation of marine spatial planning in shellfish aquaculture management: modeling studies in a Norwegian fjord.

Science.gov (United States)

Filgueira, Ramon; Grant, Jon; Strand, Øivind

2014-06-01

Shellfish carrying capacity is determined by the interaction of a cultured species with its ecosystem, which is strongly influenced by hydrodynamics. Water circulation controls the exchange of matter between farms and the adjacent areas, which in turn establishes the nutrient supply that supports phytoplankton populations. The complexity of water circulation makes necessary the use of hydrodynamic models with detailed spatial resolution in carrying capacity estimations. This detailed spatial resolution also allows for the study of processes that depend on specific spatial arrangements, e.g., the most suitable location to place farms, which is crucial for marine spatial planning, and consequently for decision support systems. In the present study, a fully spatial physical-biogeochemical model has been combined with scenario building and optimization techniques as a proof of concept of the use of ecosystem modeling as an objective tool to inform marine spatial planning. The object of this exercise was to generate objective knowledge based on an ecosystem approach to establish new mussel aquaculture areas in a Norwegian fjord. Scenario building was used to determine the best location of a pump that can be used to bring nutrient-rich deep waters to the euphotic layer, increasing primary production, and consequently, carrying capacity for mussel cultivation. In addition, an optimization tool, parameter estimation (PEST), was applied to the optimal location and mussel standing stock biomass that maximize production, according to a preestablished carrying capacity criterion. Optimization tools allow us to make rational and transparent decisions to solve a well-defined question, decisions that are essential for policy makers. The outcomes of combining ecosystem models with scenario building and optimization facilitate planning based on an ecosystem approach, highlighting the capabilities of ecosystem modeling as a tool for marine spatial planning.

Estimation of spatial uncertainties of tomographic velocity models

Energy Technology Data Exchange (ETDEWEB)

Jordan, M.; Du, Z.; Querendez, E. [SINTEF Petroleum Research, Trondheim (Norway)

2012-12-15

This research project aims to evaluate the possibility of assessing the spatial uncertainties in tomographic velocity model building in a quantitative way. The project is intended to serve as a test of whether accurate and specific uncertainty estimates (e.g., in meters) can be obtained. The project is based on Monte Carlo-type perturbations of the velocity model as obtained from the tomographic inversion guided by diagonal and off-diagonal elements of the resolution and the covariance matrices. The implementation and testing of this method was based on the SINTEF in-house stereotomography code, using small synthetic 2D data sets. To test the method the calculation and output of the covariance and resolution matrices was implemented, and software to perform the error estimation was created. The work included the creation of 2D synthetic data sets, the implementation and testing of the software to conduct the tests (output of the covariance and resolution matrices which are not implicitly provided by stereotomography), application to synthetic data sets, analysis of the test results, and creating the final report. The results show that this method can be used to estimate the spatial errors in tomographic images quantitatively. The results agree with' the known errors for our synthetic models. However, the method can only be applied to structures in the model, where the change of seismic velocity is larger than the predicted error of the velocity parameter amplitudes. In addition, the analysis is dependent on the tomographic method, e.g., regularization and parameterization. The conducted tests were very successful and we believe that this method could be developed further to be applied to third party tomographic images.

Model for spatial synthesis of automated control system of the GCR type reactor; Model za prostornu sintezu sistema automatskog upravljanja reaktora GCR tipa

Energy Technology Data Exchange (ETDEWEB)

Lazarevic, B; Matausek, M [Institut za nuklearne nauke ' Boris Kidric' , Vinca, Belgrade (Yugoslavia)

1966-07-01

This paper describes the model which was developed for synthesis of spatial distribution of automated control elements in the reactor. It represents a general reliable mathematical model for analyzing transition states and synthesis of the automated control and regulation systems of GCR type reactors. One-dimensional system was defined under assumption that the time dependence of parameters of the neutron diffusion equation are identical in the total volume of the reactor and that spatial distribution of neutrons is time independent. It is shown that this assumption is satisfactory in case of short term variations which are relevant for safety analysis.

THE INFLUENCE OF SPATIAL RESOLUTION ON NONLINEAR FORCE-FREE MODELING

Energy Technology Data Exchange (ETDEWEB)

DeRosa, M. L.; Schrijver, C. J. [Lockheed Martin Solar and Astrophysics Laboratory, 3251 Hanover St. B/252, Palo Alto, CA 94304 (United States); Wheatland, M. S.; Gilchrist, S. A. [Sydney Institute for Astronomy, School of Physics, The University of Sydney, Sydney, NSW 2006 (Australia); Leka, K. D.; Barnes, G. [NorthWest Research Associates, 3380 Mitchell Ln., Boulder, CO 80301 (United States); Amari, T.; Canou, A. [CNRS, Centre de Physique Théorique de l’École Polytechnique, F-91128, Palaiseau Cedex (France); Thalmann, J. K. [Institute of Physics/IGAM, University of Graz, Universitätsplatz 5, A-8010 Graz (Austria); Valori, G. [Mullard Space Science Laboratory, University College London, Holmbury St. Mary, Dorking, Surrey, RH5 6NT (United Kingdom); Wiegelmann, T. [Max-Planck-Institut für Sonnensystemforschung, Justus-von-Liebig-Weg 3, D-37077, Göttingen (Germany); Malanushenko, A. [Department of Physics, Montana State University, Bozeman, MT 59717 (United States); Sun, X. [W. W. Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305 (United States); Régnier, S. [Department of Mathematics and Information Sciences, Faculty of Engineering and Environment, Northumbria University, Newcastle-Upon-Tyne, NE1 8ST (United Kingdom)

2015-10-01

The nonlinear force-free field (NLFFF) model is often used to describe the solar coronal magnetic field, however a series of earlier studies revealed difficulties in the numerical solution of the model in application to photospheric boundary data. We investigate the sensitivity of the modeling to the spatial resolution of the boundary data, by applying multiple codes that numerically solve the NLFFF model to a sequence of vector magnetogram data at different resolutions, prepared from a single Hinode/Solar Optical Telescope Spectro-Polarimeter scan of NOAA Active Region 10978 on 2007 December 13. We analyze the resulting energies and relative magnetic helicities, employ a Helmholtz decomposition to characterize divergence errors, and quantify changes made by the codes to the vector magnetogram boundary data in order to be compatible with the force-free model. This study shows that NLFFF modeling results depend quantitatively on the spatial resolution of the input boundary data, and that using more highly resolved boundary data yields more self-consistent results. The free energies of the resulting solutions generally trend higher with increasing resolution, while relative magnetic helicity values vary significantly between resolutions for all methods. All methods require changing the horizontal components, and for some methods also the vertical components, of the vector magnetogram boundary field in excess of nominal uncertainties in the data. The solutions produced by the various methods are significantly different at each resolution level. We continue to recommend verifying agreement between the modeled field lines and corresponding coronal loop images before any NLFFF model is used in a scientific setting.

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.

Spatial models for context-aware indoor navigation systems: A survey

Directory of Open Access Journals (Sweden)

Imad Afyouni

2012-06-01

Full Text Available This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS, and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, considering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation purposes, and by taking into account the context dimensions.

Consequences of spatial autocorrelation for niche-based models

DEFF Research Database (Denmark)

Segurado, P.; Araújo, Miguel B.; Kunin, W. E.

2006-01-01

1.  Spatial autocorrelation is an important source of bias in most spatial analyses. We explored the bias introduced by spatial autocorrelation on the explanatory and predictive power of species' distribution models, and make recommendations for dealing with the problem. 2.  Analyses were based o...

A Spatial Probit Econometric Model of Land Change: The Case of Infrastructure Development in Western Amazonia, Peru

Science.gov (United States)

Arima, E. Y.

2016-01-01

Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD+ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200–300 km2. This translates into aboveground carbon emissions of 1.36 and 1.85 x 106 tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads. PMID:27010739

A Spatial Probit Econometric Model of Land Change: The Case of Infrastructure Development in Western Amazonia, Peru.

Directory of Open Access Journals (Sweden)

E Y Arima

Full Text Available Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD+ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200-300 km2. This translates into aboveground carbon emissions of 1.36 and 1.85 x 106 tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads.

A Spatial Probit Econometric Model of Land Change: The Case of Infrastructure Development in Western Amazonia, Peru.

Science.gov (United States)

Arima, E Y

2016-01-01

Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD+ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200-300 km2. This translates into aboveground carbon emissions of 1.36 and 1.85 x 106 tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads.

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....

Unleashing spatially distributed ecohydrology modeling using Big Data tools

Science.gov (United States)

Miles, B.; Idaszak, R.

2015-12-01

Physically based spatially distributed ecohydrology models are useful for answering science and management questions related to the hydrology and biogeochemistry of prairie, savanna, forested, as well as urbanized ecosystems. However, these models can produce hundreds of gigabytes of spatial output for a single model run over decadal time scales when run at regional spatial scales and moderate spatial resolutions (~100-km2+ at 30-m spatial resolution) or when run for small watersheds at high spatial resolutions (~1-km2 at 3-m spatial resolution). Numerical data formats such as HDF5 can store arbitrarily large datasets. However even in HPC environments, there are practical limits on the size of single files that can be stored and reliably backed up. Even when such large datasets can be stored, querying and analyzing these data can suffer from poor performance due to memory limitations and I/O bottlenecks, for example on single workstations where memory and bandwidth are limited, or in HPC environments where data are stored separately from computational nodes. The difficulty of storing and analyzing spatial data from ecohydrology models limits our ability to harness these powerful tools. Big Data tools such as distributed databases have the potential to surmount the data storage and analysis challenges inherent to large spatial datasets. Distributed databases solve these problems by storing data close to computational nodes while enabling horizontal scalability and fault tolerance. Here we present the architecture of and preliminary results from PatchDB, a distributed datastore for managing spatial output from the Regional Hydro-Ecological Simulation System (RHESSys). The initial version of PatchDB uses message queueing to asynchronously write RHESSys model output to an Apache Cassandra cluster. Once stored in the cluster, these data can be efficiently queried to quickly produce both spatial visualizations for a particular variable (e.g. maps and animations), as well

Spatial scale separation in regional climate modelling

Energy Technology Data Exchange (ETDEWEB)

Feser, F.

2005-07-01

In this thesis the concept of scale separation is introduced as a tool for first improving regional climate model simulations and, secondly, to explicitly detect and describe the added value obtained by regional modelling. The basic idea behind this is that global and regional climate models have their best performance at different spatial scales. Therefore the regional model should not alter the global model's results at large scales. The for this purpose designed concept of nudging of large scales controls the large scales within the regional model domain and keeps them close to the global forcing model whereby the regional scales are left unchanged. For ensemble simulations nudging of large scales strongly reduces the divergence of the different simulations compared to the standard approach ensemble that occasionally shows large differences for the individual realisations. For climate hindcasts this method leads to results which are on average closer to observed states than the standard approach. Also the analysis of the regional climate model simulation can be improved by separating the results into different spatial domains. This was done by developing and applying digital filters that perform the scale separation effectively without great computational effort. The separation of the results into different spatial scales simplifies model validation and process studies. The search for 'added value' can be conducted on the spatial scales the regional climate model was designed for giving clearer results than by analysing unfiltered meteorological fields. To examine the skill of the different simulations pattern correlation coefficients were calculated between the global reanalyses, the regional climate model simulation and, as a reference, of an operational regional weather analysis. The regional climate model simulation driven with large-scale constraints achieved a high increase in similarity to the operational analyses for medium-scale 2 meter

The role of spatial information in the preservation of the shrimp nursery function of mangroves: a spatially explicit bio-economic model for the assessment of land use trade-offs.

Science.gov (United States)

Zavalloni, Matteo; Groeneveld, Rolf A; van Zwieten, Paul A M

2014-10-01

Conversion to aquaculture affects the provision of important ecosystem services provided by mangrove ecosystems, and this effect depends strongly on the location of the conversion. We introduce in a bio-economic mathematical programming model relevant spatial elements that affect the provision of the nursery habitat service of mangroves: (1) direct or indirect connection of mangroves to watercourses; (2) the spatial allocation of aquaculture ponds; and (3) the presence of non-linear relations between mangrove extent and juvenile recruitment to wild shrimp populations. By tracing out the production possibilities frontier of wild and cultivated shrimp, the model assesses the role of spatial information in the trade-off between aquaculture and the nursery habitat function using spatial elements relevant to our model of a mangrove area in Ca Mau Province, Viet Nam. Results show that where mangrove forests have to coexist with shrimp aquaculture ponds, the inclusion of specific spatial information on ecosystem functions in considerations of land allocation can achieve aquaculture benefits while largely preserving the economic benefits generated by the nursery habitat function. However, if spatial criteria are ignored, ill-advised land allocation decisions can easily lead to a collapse of the mangrove's nursery function. Copyright © 2014 Elsevier Ltd. All rights reserved.

Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

Science.gov (United States)

Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

2016-04-01

Networks with nodes embedded in a metric space have gained increasing interest in recent years. The effects of spatial embedding on the networks' structural characteristics, however, are rarely taken into account when studying their macroscopic properties. Here, we propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve certain global and local statistics associated with the nodes' embedding in a metric space. Comparing the original network's and the resulting surrogates' global characteristics allows one to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links. We apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes' spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into different classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling the underlying complex system structure from spatial embedding of nodes in many fields, ranging from social systems over infrastructure and neurophysiology to climatology.

Hybrid Spatial Data Model for Indoor Space: Combined Topology and Grid

Directory of Open Access Journals (Sweden)

Zhiyong Lin

2017-11-01

Full Text Available The construction and application of an indoor spatial data model is an important prerequisite to meet the requirements of diversified indoor spatial location services. The traditional indoor spatial topology model focuses on the construction of topology information. It has high path analysis and query efficiency, but ignores the spatial location information. The grid model retains the plane position information by grid, but increases the data volume and complexity of the model and reduces the efficiency of the model analysis. This paper presents a hybrid model for interior space based on topology and grid. Based on the spatial meshing and spatial division of the interior space, the model retains the position information and topological connectivity information of the interior space by establishing the connection or affiliation between the grid subspace and the topological subspace. The model improves the speed of interior spatial analysis and solves the problem of the topology information and location information updates not being synchronized. In this study, the A* shortest path query efficiency of typical daily indoor activities under the grid model and the hybrid model were compared for the indoor plane of an apartment and a shopping mall. The results obtained show that the hybrid model is 43% higher than the A* algorithm of the grid model as a result of the existence of topology communication information. This paper provides a useful idea for the establishment of a highly efficient and highly available interior spatial data model.

Spatial housing economics: a survey

OpenAIRE

Meen, Geoff

2016-01-01

This introduction to the Virtual Special Issue surveys the development of spatial housing economics from its roots in neo-classical theory, through more recent developments in social interactions modelling, and touching on the role of institutions, path dependence and economic history. The survey also points to some of the more promising future directions for the subject that are beginning to appear in the literature. The survey covers elements hedonic models, spatial econometrics, neighbourh...

Demography-based adaptive network model reproduces the spatial organization of human linguistic groups

Science.gov (United States)

Capitán, José A.; Manrubia, Susanna

2015-12-01

The distribution of human linguistic groups presents a number of interesting and nontrivial patterns. The distributions of the number of speakers per language and the area each group covers follow log-normal distributions, while population and area fulfill an allometric relationship. The topology of networks of spatial contacts between different linguistic groups has been recently characterized, showing atypical properties of the degree distribution and clustering, among others. Human demography, spatial conflicts, and the construction of networks of contacts between linguistic groups are mutually dependent processes. Here we introduce an adaptive network model that takes all of them into account and successfully reproduces, using only four model parameters, not only those features of linguistic groups already described in the literature, but also correlations between demographic and topological properties uncovered in this work. Besides their relevance when modeling and understanding processes related to human biogeography, our adaptive network model admits a number of generalizations that broaden its scope and make it suitable to represent interactions between agents based on population dynamics and competition for space.

Using a spatially structured life cycle model to assess the influence of multiple stressors on an exploited coastal-nursery-dependent population

Science.gov (United States)

Archambault, B.; Rivot, E.; Savina, M.; Le Pape, O.

2018-02-01

Exploited coastal-nursery-dependent fish species are subject to various stressors occurring at specific stages of the life cycle: climate-driven variability in hydrography determines the success of the first eggs/larvae stages; coastal nursery habitat suitability controls juvenile growth and survival; and fisheries target mostly adults. A life cycle approach was used to quantify the relative influence of these stressors on the Eastern English Channel (EEC) population of the common sole (Solea solea), a coastal-nursery-dependent flatfish population which sustains important fisheries. The common sole has a complex life cycle: after eggs hatch, larvae spend several weeks drifting in open water. Survivors go on to metamorphose into benthic fish. Juveniles spend the first two years of their life in coastal and estuarine nurseries. Close to maturation, they migrate to deeper areas, where different subpopulations supplied by different nurseries reproduce and are exploited by fisheries. A spatially structured age-and stage-based hierarchical Bayesian model integrating various aspects of ecological knowledge, data sources and expert knowledge was built to quantitatively describe this complex life cycle. The model included the low connectivity among three subpopulations in the EEC, the influence of hydrographic variability, the availability of suitable juvenile habitat and fisheries. Scenarios were designed to quantify the effects of interacting stressors on population renewal. Results emphasized the importance of coastal nursery habitat availability and quality for the population renewal. Realistic restoration scenarios of the highly degraded Seine estuary produced a two-third increase in catch potential for the adjacent subpopulation. Fisheries, however, remained the main source of population depletion. Setting fishing mortality to the maximum sustainable yield led to substantial increases in biomass (+100%) and catch (+33%) at the EEC scale. The approach also showed how

OECD/NEA benchmark for time-dependent neutron transport calculations without spatial homogenization

Energy Technology Data Exchange (ETDEWEB)

Hou, Jason, E-mail: jason.hou@ncsu.edu [Department of Nuclear Engineering, North Carolina State University, Raleigh, NC 27695 (United States); Ivanov, Kostadin N. [Department of Nuclear Engineering, North Carolina State University, Raleigh, NC 27695 (United States); Boyarinov, Victor F.; Fomichenko, Peter A. [National Research Centre “Kurchatov Institute”, Kurchatov Sq. 1, Moscow (Russian Federation)

2017-06-15

Highlights: • A time-dependent homogenization-free neutron transport benchmark was created. • The first phase, known as the kinetics phase, was described in this work. • Preliminary results for selected 2-D transient exercises were presented. - Abstract: A Nuclear Energy Agency (NEA), Organization for Economic Co-operation and Development (OECD) benchmark for the time-dependent neutron transport calculations without spatial homogenization has been established in order to facilitate the development and assessment of numerical methods for solving the space-time neutron kinetics equations. The benchmark has been named the OECD/NEA C5G7-TD benchmark, and later extended with three consecutive phases each corresponding to one modelling stage of the multi-physics transient analysis of the nuclear reactor core. This paper provides a detailed introduction of the benchmark specification of Phase I, known as the “kinetics phase”, including the geometry description, supporting neutron transport data, transient scenarios in both two-dimensional (2-D) and three-dimensional (3-D) configurations, as well as the expected output parameters from the participants. Also presented are the preliminary results for the initial state 2-D core and selected transient exercises that have been obtained using the Monte Carlo method and the Surface Harmonic Method (SHM), respectively.

Pattern formation through spatial interactions in a modified Daisyworld model

Science.gov (United States)

Alberti, Tommaso; Primavera, Leonardo; Lepreti, Fabio; Vecchio, Antonio; Carbone, Vincenzo

2015-04-01

The Daisyworld model is based on a hypothetical planet, like the Earth, which receives the radiant energy coming from a Sun-like star, and populated by two kinds of identical plants differing by their colour: white daisies reflecting light and black daisies absorbing light. The interactions and feedbacks between the collective biota of the planet and the incoming radiation form a self-regulating system where the conditions for life are maintained. We investigate a modified version of the Daisyworld model where a spatial dependency on latitude is introduced, and both a variable heat diffusivity along latitude and a simple greenhouse model are included. We show that the spatial interactions between the variables of the system can generate some equilibrium patterns which can locally stabilize the coexistence of the two vegetation types. The feedback on albedo is able to generate new equilibrium solutions which can efficiently self-regulate the planet climate, even for values of the solar luminosity relatively far from the current Earth conditions. The extension to spatial Daisyworld gives room to the possibility of inhomogeneous solar forcing in a curved planet, with explicit differences between poles and equator and the direct use of the heat diffusion equation. As a first approach, to describe a spherical planet, we consider the temperature T(θ,t) and the surface coverage as depending only on time and on latitude θ (-90° ≤ θ ≤ 90°). A second step is the introduction of the greenhouse effect in the model, the process by which outgoing infrared radiation is partly screened by greenhouse gases. This effect can be described by relaxing the black-body radiation hypothesis and by introducing a grayness function g(T) in the heat equation. As a third step, we consider a latitude dependence of the Earth's conductivity, χ = χ(θ). Considering these terms, using spherical coordinates and symmetry with respect to θ, the modified Daisyworld equations reduce to the

Tukey max-stable processes for spatial extremes

KAUST Repository

Xu, Ganggang; Genton, Marc G.

2016-01-01

We propose a new type of max-stable process that we call the Tukey max-stable process for spatial extremes. It brings additional flexibility to modeling dependence structures among spatial extremes. The statistical properties of the Tukey max

A gender- and sexual orientation-dependent spatial attentional effect of invisible images.

Science.gov (United States)

Jiang, Yi; Costello, Patricia; Fang, Fang; Huang, Miner; He, Sheng

2006-11-07

Human observers are constantly bombarded with a vast amount of information. Selective attention helps us to quickly process what is important while ignoring the irrelevant. In this study, we demonstrate that information that has not entered observers' consciousness, such as interocularly suppressed (invisible) erotic pictures, can direct the distribution of spatial attention. Furthermore, invisible erotic information can either attract or repel observers' spatial attention depending on their gender and sexual orientation. While unaware of the suppressed pictures, heterosexual males' attention was attracted to invisible female nudes, heterosexual females' attention was attracted to invisible male nudes, gay males behaved similarly to heterosexual females, and gay/bisexual females performed in-between heterosexual males and females.

The role of spatial information in the preservation of the shrimp nursery function of mangroves: A spatially explicit bio-economic model for the assessment of land use trade-offs

NARCIS (Netherlands)

Zavalloni, M.; Groeneveld, R.A.; Zwieten, van P.A.M.

2014-01-01

Conversion to aquaculture affects the provision of important ecosystem services provided by mangrove ecosystems, and this effect depends strongly on the location of the conversion. We introduce in a bioeconomic mathematical programming model relevant spatial elements that affect the provision of the

Improved control for distributed parameter systems with time-dependent spatial domains utilizing mobile sensor–actuator networks

International Nuclear Information System (INIS)

Zhang Jian-Zhong; Cui Bao-Tong; Zhuang Bo

2017-01-01

A guidance policy for controller performance enhancement utilizing mobile sensor–actuator networks (MSANs) is proposed for a class of distributed parameter systems (DPSs), which are governed by diffusion partial differential equations (PDEs) with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy. (paper)

Economic agglomerations and spatio-temporal cycles in a spatial growth model with capital transport cost

Science.gov (United States)

Juchem Neto, J. P.; Claeyssen, J. C. R.; Pôrto Júnior, S. S.

2018-03-01

In this paper we introduce capital transport cost in a unidimensional spatial Solow-Swan model of economic growth with capital-induced labor migration, considered in an unbounded domain. Proceeding with a stability analysis, we show that there is a critical value for the capital transport cost where the dynamic behavior of the economy changes, provided that the intensity of capital-induced labor migration is strong enough. On the one hand, if the capital transport cost is higher than this critical value, the spatially homogeneous equilibrium of coexistence of the model is stable, and the economy converges to this spatially homogeneous state in the long run; on the other hand, if transport cost is lower than this critical value, the equilibrium is unstable, and the economy may develop different spatio-temporal dynamics, including the formation of stable economic agglomerations and spatio-temporal economic cycles, depending on the other parameters in the model. Finally, numerical simulations support the results of the stability analysis, and illustrate the spatio-temporal dynamics generated by the model, suggesting that the economy as a whole benefits from the formation of economic agglomerations and cycles, with a higher capital transport cost reducing this gain.

Updates to the Demographic and Spatial Allocation Models to ...

Science.gov (United States)

EPA announced the availability of the draft report, Updates to the Demographic and Spatial Allocation Models to Produce Integrated Climate and Land Use Scenarios (ICLUS) for a 30-day public comment period. The ICLUS version 2 (v2) modeling tool furthered land change modeling by providing nationwide housing development scenarios up to 2100. ICLUS V2 includes updated population and land use data sets and addressing limitations identified in ICLUS v1 in both the migration and spatial allocation models. The companion user guide describes the development of ICLUS v2 and the updates that were made to the original data sets and the demographic and spatial allocation models. [2017 UPDATE] Get the latest version of ICLUS and stay up-to-date by signing up to the ICLUS mailing list. The GIS tool enables users to run SERGoM with the population projections developed for the ICLUS project and allows users to modify the spatial allocation housing density across the landscape.

Spatial dependence in wind and optimal wind power allocation: A copula-based analysis

International Nuclear Information System (INIS)

Grothe, Oliver; Schnieders, Julius

2011-01-01

The investment decision on the placement of wind turbines is, neglecting legal formalities, mainly driven by the aim to maximize the expected annual energy production of single turbines. The result is a concentration of wind farms at locations with high average wind speed. While this strategy may be optimal for single investors maximizing their own return on investment, the resulting overall allocation of wind turbines may be unfavorable for energy suppliers and the economy because of large fluctuations in the overall wind power output. This paper investigates to what extent optimal allocation of wind farms in Germany can reduce these fluctuations. We analyze stochastic dependencies of wind speed for a large data set of German on- and offshore weather stations and find that these dependencies turn out to be highly nonlinear but constant over time. Using copula theory we determine the value at risk of energy production for given allocation sets of wind farms and derive optimal allocation plans. We find that the optimized allocation of wind farms may substantially stabilize the overall wind energy supply on daily as well as hourly frequency. - Highlights: → Spatial modeling of wind forces in Germany. → A novel way to assess nonlinear dependencies of wind forces by copulas. → Wind turbine allocation by maximizing lower quantiles of energy production. → Optimal results show major increase in reliable part of wind energy.

Spatial capture-recapture models for search-encounter data

Science.gov (United States)

Royle, J. Andrew; Kery, Marc; Guelat, Jerome

2011-01-01

1. Spatial capture–recapture models make use of auxiliary data on capture location to provide density estimates for animal populations. Previously, models have been developed primarily for fixed trap arrays which define the observable locations of individuals by a set of discrete points. 2. Here, we develop a class of models for 'search-encounter' data, i.e. for detections of recognizable individuals in continuous space, not restricted to trap locations. In our hierarchical model, detection probability is related to the average distance between individual location and the survey path. The locations are allowed to change over time owing to movements of individuals, and individual locations are related formally by a model describing individual activity or home range centre which is itself regarded as a latent variable in the model. We provide a Bayesian analysis of the model in WinBUGS, and develop a custom MCMC algorithm in the R language. 3. The model is applied to simulated data and to territory mapping data for the Willow Tit from the Swiss Breeding Bird Survey MHB. While the observed density was 15 territories per nominal 1 km2 plot of unknown effective sample area, the model produced a density estimate of 21∙12 territories per square km (95% posterior interval: 17–26). 4. Spatial capture–recapture models are relevant to virtually all animal population studies that seek to estimate population size or density, yet existing models have been proposed mainly for conventional sampling using arrays of traps. Our model for search-encounter data, where the spatial pattern of searching can be arbitrary and may change over occasions, greatly expands the scope and utility of spatial capture–recapture models.

Modeling Spatial and Temporal Variability in Ammonia Emissions from Agricultural Fertilization

Science.gov (United States)

Balasubramanian, S.; Koloutsou-Vakakis, S.; Rood, M. J.

2013-12-01

. Representative temporal factors are being developed to capture crop-specific NH3 emission variability by combining knowledge of local crop management practices with high resolution cropland and soil maps. This improved spatially and temporally dependent NH3 emission inventory for agricultural fertilization is being prepared as a direct input to a state of the art air quality model to evaluate the effects of agricultural fertilization on regional air quality and atmospheric deposition of reactive nitrogen species.

Modelling spatial relationship between climatic conditions and annual parasite incidence of malaria in southern part of Sistan&Balouchistan Province of Iran using spatial statistic models

Directory of Open Access Journals (Sweden)

Mansour Halimi

2014-02-01

Full Text Available Objective: To model spatial relationship between climatic conditions and annual parasite incidence (API of malaria in southern part of Sistan&Balouchistan Province of Iran using spatial statistic models . Methods: A geographical weighted regression model was applied for predicting API by 3 climatic factors in order to model the spatial API of malaria in Sistan&Baluchistan Province of Iran. Results: The results indicated that most important climatic factor for explaining API in Sistan&Baluchistan was annual rainfall being of more importance in southern part of study area such as Chabahar, and Nikshar. The temperature and relative humidity are of the second and third priority respectively. The importance of these two climatic factors is higher in northern part of the studied region. The spatial autocorrelation (Moran ’s I for standard residual of applied geographical weighted regression model is -0.022 which indicated no spatial patterns. Conclusions: This model explained only 0.51 of API spatial variation (R2=0.51. Thus, the nonclimatic factors such as socioeconomic, lifestyle and the neighborhood position of this province with Afghanistan, and Pakistan also should be considered in epidemiological survey of malaria in Sistan&Baluchistan.

Non-Stationary Dependence Structures for Spatial Extremes

KAUST Repository

Huser, Raphaë l; Genton, Marc G.

2016-01-01

been developed, and fitted to various types of data. However, a recurrent problem is the modeling of non-stationarity. In this paper, we develop non-stationary max-stable dependence structures in which covariates can be easily incorporated. Inference

Spatial differentiation in characterisation modelling – what difference does it make?

DEFF Research Database (Denmark)

Hauschild, Michael Zwicky; Potting, José

2004-01-01

In the life cycle of a product, emissions take place at many different locations. The location of the sources and its surrounding condition influence the fate of the emission and the exposure it leads to but this source of variation is currently neglected in life cycle impact assessment, although...... results from the Danish LCA Methodology Development and Consensus Creation Project address this issue and provides a framework for spatially differentiated characterisation modelling together with easily applicable site-dependent factors for each European country and normalisation references for those...

Neural correlates of reward-based spatial learning in persons with cocaine dependence.

Science.gov (United States)

Tau, Gregory Z; Marsh, Rachel; Wang, Zhishun; Torres-Sanchez, Tania; Graniello, Barbara; Hao, Xuejun; Xu, Dongrong; Packard, Mark G; Duan, Yunsuo; Kangarlu, Alayar; Martinez, Diana; Peterson, Bradley S

2014-02-01

Dysfunctional learning systems are thought to be central to the pathogenesis of and impair recovery from addictions. The functioning of the brain circuits for episodic memory or learning that support goal-directed behavior has not been studied previously in persons with cocaine dependence (CD). Thirteen abstinent CD and 13 healthy participants underwent MRI scanning while performing a task that requires the use of spatial cues to navigate a virtual-reality environment and find monetary rewards, allowing the functional assessment of the brain systems for spatial learning, a form of episodic memory. Whereas both groups performed similarly on the reward-based spatial learning task, we identified disturbances in brain regions involved in learning and reward in CD participants. In particular, CD was associated with impaired functioning of medial temporal lobe (MTL), a brain region that is crucial for spatial learning (and episodic memory) with concomitant recruitment of striatum (which normally participates in stimulus-response, or habit, learning), and prefrontal cortex. CD was also associated with enhanced sensitivity of the ventral striatum to unexpected rewards but not to expected rewards earned during spatial learning. We provide evidence that spatial learning in CD is characterized by disturbances in functioning of an MTL-based system for episodic memory and a striatum-based system for stimulus-response learning and reward. We have found additional abnormalities in distributed cortical regions. Consistent with findings from animal studies, we provide the first evidence in humans describing the disruptive effects of cocaine on the coordinated functioning of multiple neural systems for learning and memory.

A physically based analytical spatial air temperature and humidity model

Science.gov (United States)

Yang Yang; Theodore A. Endreny; David J. Nowak

2013-01-01

Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat...

Towards a 3d Spatial Urban Energy Modelling Approach

Science.gov (United States)

Bahu, J.-M.; Koch, A.; Kremers, E.; Murshed, S. M.

2013-09-01

Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies

Spatial neutronics modelling to evaluate the temperature reactivity feedbacks in a lead-cooled fast reactor - 15288

International Nuclear Information System (INIS)

Lorenzi, S.; Cammi, A.; Luzzi, L.

2015-01-01

The qualitative and quantitative assessment of the thermal reactivity feedbacks occurring in a nuclear reactor is a crucial issue for the time-dependent evolution of the system and, in turn, it has a great impact on the development and validation of advanced control techniques. In the present work, in order to overcome the limitations of the classic Point Kinetics adopted in the control simulators, a spatial neutronics model, representing the neutron flux as sum of a spatial basis weighted by time-dependent coefficients, has been considered. The reference reactor is ALFRED, the European demonstrator of the Lead-cooled Fast Reactor technology. Average cross-sections for each fuel assembly, calculated by means of a Monte Carlo code, have been used to solve the partial differential equations of the neutron diffusion, exploiting the capabilities of the COMSOL software. Once obtained the spatial functions, the set of equations for studying the reactivity effects has been implemented in the MATLAB environment. Among the several temperature reactivity feedbacks, specific attention has been paid to the Doppler effect in the fuel and to the lead density effect. Several spatial bases have been calculated and their capability of representing the reactivity variation have been assessed. (authors)

Accounting for spatial effects in land use regression for urban air pollution modeling.

Science.gov (United States)

Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G

2015-01-01

In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Spatial analysis of agri-environmental policy uptake and expenditure in Scotland.

Science.gov (United States)

Yang, Anastasia L; Rounsevell, Mark D A; Wilson, Ronald M; Haggett, Claire

2014-01-15

Agri-environment is one of the most widely supported rural development policy measures in Scotland in terms of number of participants and expenditure. It comprises 69 management options and sub-options that are delivered primarily through the competitive 'Rural Priorities scheme'. Understanding the spatial determinants of uptake and expenditure would assist policy-makers in guiding future policy targeting efforts for the rural environment. This study is unique in examining the spatial dependency and determinants of Scotland's agri-environmental measures and categorised options uptake and payments at the parish level. Spatial econometrics is applied to test the influence of 40 explanatory variables on farming characteristics, land capability, designated sites, accessibility and population. Results identified spatial dependency for each of the dependent variables, which supported the use of spatially-explicit models. The goodness of fit of the spatial models was better than for the aspatial regression models. There was also notable improvement in the models for participation compared with the models for expenditure. Furthermore a range of expected explanatory variables were found to be significant and varied according to the dependent variable used. The majority of models for both payment and uptake showed a significant positive relationship with SSSI (Sites of Special Scientific Interest), which are designated sites prioritised in Scottish policy. These results indicate that environmental targeting efforts by the government for AEP uptake in designated sites can be effective. However habitats outside of SSSI, termed here the 'wider countryside' may not be sufficiently competitive to receive funding in the current policy system. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sun-controlled spatial and time-dependent cycles in the climatic/weather system

International Nuclear Information System (INIS)

Njau, E.C.

1990-11-01

We show, on the basis of meteorological records, that certain spatial and time-dependent cycles exist in the earth-atmosphere system (EAS). These cycles seem to be associated with sunspot cycles and hence have been referred to in the text as ''data-derived solar cycles''. Our analysis establishes three important characteristics of the data-derived solar cycles (DSC's). Firstly the crests and troughs of these data-derived solar cycles are mostly latitudinally aligned and have (zonal) spatial wavelengths greater than about 7 degrees of longitude. Secondly the DSC's have periods mostly lying between 6 years and 12 years. In certain stations, some DSC's coincide quite well with corresponding sunspot cycles. Thirdly the crests and troughs of the DSC's drift eastwards at speeds exceeding about 1.5 longitude degrees per year. Furthermore, these DSC's display peak-to-peak amplitudes of about 2 deg. C along East Africa. On the basis of earlier work and bearing in mind the considerable temperature-dependence of the stratospheric ozone layer, we predict existence of latitudinally aligned enhancement and depletion structures (corresponding to the DSC's) in the stratospheric ozone layer within cloudless midnight-to-predawn sectors. (author). 9 refs, 5 figs

Integrating remote sensing and spatially explicit epidemiological modeling

Science.gov (United States)

Finger, Flavio; Knox, Allyn; Bertuzzo, Enrico; Mari, Lorenzo; Bompangue, Didier; Gatto, Marino; Rinaldo, Andrea

2015-04-01

Spatially explicit epidemiological models are a crucial tool for the prediction of epidemiological patterns in time and space as well as for the allocation of health care resources. In addition they can provide valuable information about epidemiological processes and allow for the identification of environmental drivers of the disease spread. Most epidemiological models rely on environmental data as inputs. They can either be measured in the field by the means of conventional instruments or using remote sensing techniques to measure suitable proxies of the variables of interest. The later benefit from several advantages over conventional methods, including data availability, which can be an issue especially in developing, and spatial as well as temporal resolution of the data, which is particularly crucial for spatially explicit models. Here we present the case study of a spatially explicit, semi-mechanistic model applied to recurring cholera outbreaks in the Lake Kivu area (Democratic Republic of the Congo). The model describes the cholera incidence in eight health zones on the shore of the lake. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers. Human mobility and its effect on the disease spread is also taken into account. Several model configurations are tested on a data set of reported cases. The best models, accounting for different environmental drivers, and selected using the Akaike information criterion, are formally compared via cross validation. The best performing model accounts for seasonality, El Niño Southern Oscillation, precipitation and human mobility.

Optofluidic intracavity spectroscopy for spatially, temperature, and wavelength dependent refractometry

Science.gov (United States)

Kindt, Joel D.

A microfluidic refractometer was designed based on previous optofluidic intracavity spectroscopy (OFIS) chips utilized to distinguish healthy and cancerous cells. The optofluidic cavity is realized by adding high reflectivity dielectric mirrors to the top and bottom of a microfluidic channel. This creates a plane-plane Fabry-Perot optical cavity in which the resonant wavelengths are highly dependent on the optical path length inside the cavity. Refractometry is a useful method to determine the nature of fluids, including the concentration of a solute in a solvent as well as the temperature of the fluid. Advantages of microfluidic systems are the easy integration with lab-on-chip devices and the need for only small volumes of fluid. The unique abilities of the microfluidic refractometer in this thesis include its spatial, temperature, and wavelength dependence. Spatial dependence of the transmission spectrum is inherent through a spatial filtering process implemented with an optical fiber and microscope objective. A sequence of experimental observations guided the change from using the OFIS chip as a cell discrimination device to a complimentary refractometer. First, it was noted the electrode structure within the microfluidic channel, designed to trap and manipulate biological cells with dielectrophoretic (DEP) forces, caused the resonant wavelengths to blue-shift when the electrodes were energized. This phenomenon is consistent with the negative dn/dT property of water and water-based solutions. Next, it was necessary to develop a method to separate the optical path length into physical path length and refractive index. Air holes were placed near the microfluidic channel to exclusively measure the cavity length with the known refractive index of air. The cavity length was then interpolated across the microfluidic channel, allowing any mechanical changes to be taken into account. After the separation of physical path length and refractive index, it was of interest

The importance of distance to resources in the spatial modelling of bat foraging habitat.

Directory of Open Access Journals (Sweden)

Ana Rainho

Full Text Available Many bats are threatened by habitat loss, but opportunities to manage their habitats are now increasing. Success of management depends greatly on the capacity to determine where and how interventions should take place, so models predicting how animals use landscapes are important to plan them. Bats are quite distinctive in the way they use space for foraging because (i most are colonial central-place foragers and (ii exploit scattered and distant resources, although this increases flying costs. To evaluate how important distances to resources are in modelling foraging bat habitat suitability, we radio-tracked two cave-dwelling species of conservation concern (Rhinolophus mehelyi and Miniopterus schreibersii in a Mediterranean landscape. Habitat and distance variables were evaluated using logistic regression modelling. Distance variables greatly increased the performance of models, and distance to roost and to drinking water could alone explain 86 and 73% of the use of space by M. schreibersii and R. mehelyi, respectively. Land-cover and soil productivity also provided a significant contribution to the final models. Habitat suitability maps generated by models with and without distance variables differed substantially, confirming the shortcomings of maps generated without distance variables. Indeed, areas shown as highly suitable in maps generated without distance variables proved poorly suitable when distance variables were also considered. We concluded that distances to resources are determinant in the way bats forage across the landscape, and that using distance variables substantially improves the accuracy of suitability maps generated with spatially explicit models. Consequently, modelling with these variables is important to guide habitat management in bats and similarly mobile animals, particularly if they are central-place foragers or depend on spatially scarce resources.

Spherical Process Models for Global Spatial Statistics

KAUST Repository

Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G.

2017-01-01

Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture

Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

Directory of Open Access Journals (Sweden)

Zhensheng Wang

2017-02-01

Full Text Available The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research.

Scaling-up spatially-explicit ecological models using graphics processors

NARCIS (Netherlands)

Koppel, Johan van de; Gupta, Rohit; Vuik, Cornelis

2011-01-01

How the properties of ecosystems relate to spatial scale is a prominent topic in current ecosystem research. Despite this, spatially explicit models typically include only a limited range of spatial scales, mostly because of computing limitations. Here, we describe the use of graphics processors to

A nonlocal spatial model for Lyme disease

Science.gov (United States)

Yu, Xiao; Zhao, Xiao-Qiang

2016-07-01

This paper is devoted to the study of a nonlocal and time-delayed reaction-diffusion model for Lyme disease with a spatially heterogeneous structure. In the case of a bounded domain, we first prove the existence of the positive steady state and a threshold type result for the disease-free system, and then establish the global dynamics for the model system in terms of the basic reproduction number. In the case of an unbound domain, we obtain the existence of the disease spreading speed and its coincidence with the minimal wave speed. At last, we use numerical simulations to verify our analytic results and investigate the influence of model parameters and spatial heterogeneity on the disease infection risk.

Native birds and alien insects: spatial density dependence in songbird predation of invading oak gallwasps.

Directory of Open Access Journals (Sweden)

Karsten Schönrogge

Full Text Available Revealing the interactions between alien species and native communities is central to understanding the ecological consequences of range expansion. Much has been learned through study of the communities developing around invading herbivorous insects. Much less, however, is known about the significance of such aliens for native vertebrate predators for which invaders may represent a novel food source. We quantified spatial patterns in native bird predation of invading gall-inducing Andricus wasps associated with introduced Turkey oak (Quercus cerris at eight sites across the UK. These gallwasps are available at high density before the emergence of caterpillars that are the principle spring food of native insectivorous birds. Native birds showed positive spatial density dependence in gall attack rates at two sites in southern England, foraging most extensively on trees with highest gall densities. In a subsequent study at one of these sites, positive spatial density dependence persisted through four of five sequential week-long periods of data collection. Both patterns imply that invading galls are a significant resource for at least some native bird populations. Density dependence was strongest in southern UK bird populations that have had longest exposure to the invading gallwasps. We hypothesise that this pattern results from the time taken for native bird populations to learn how to exploit this novel resource.

The role of the spatial resolution of a three-dimensional hydrodynamic model for marine transport risk assessment

Directory of Open Access Journals (Sweden)

Oleg Andrejev

2011-05-01

Full Text Available The paper addresses the sensitivity of a novel method for quantifying the environmental risks associated with the current-driven transport of adverse impacts released from offshore sources (e.g. ship traffic with respect to the spatial resolution of the underlying hydrodynamic model. The risk is evaluated as the probability of particles released in different sea areas hitting the coast and in terms of the time after which the hit occurs (particle age on the basis of a statistical analysis of large sets of 10-day long Lagrangian trajectories calculated for 1987-1991 for the Gulf of Finland, the Baltic Sea. The relevant 2D maps are calculated using the OAAS model with spatial resolutions of 2, 1 and 0.5 nautical miles (nm and with identical initial, boundary and forcing conditions from the Rossby Centre 3D hydrodynamic model (RCO, Swedish Meteorological and Hydrological Institute. The spatially averaged values of the probability and particle age display hardly any dependence on the resolution. They both reach almost identical stationary levels (0.67-0.69 and ca 5.3 days respectively after a few years of simulations. Also, the spatial distributions of the relevant fields are qualitatively similar for all resolutions. In contrast, the optimum locations for fairways depend substantially on the resolution, whereas the results for the 2 nm model differ considerably from those obtained using finer-resolution models. It is concluded that eddy-permitting models with a grid step exceeding half the local baroclinic Rossby radius are suitable for a quick check of whether or not any potential gain from this method is feasible, whereas higher-resolution simulations with eddy-resolving models are necessary for detailed planning. The asymptotic values of the average probability and particle age are suggested as an indicator of the potential gain from the method in question and also as a new measure of the vulnerability of the nearshore of water bodies to

The quantitative modelling of human spatial habitability

Science.gov (United States)

Wise, James A.

1988-01-01

A theoretical model for evaluating human spatial habitability (HuSH) in the proposed U.S. Space Station is developed. Optimizing the fitness of the space station environment for human occupancy will help reduce environmental stress due to long-term isolation and confinement in its small habitable volume. The development of tools that operationalize the behavioral bases of spatial volume for visual kinesthetic, and social logic considerations is suggested. This report further calls for systematic scientific investigations of how much real and how much perceived volume people need in order to function normally and with minimal stress in space-based settings. The theoretical model presented in this report can be applied to any size or shape interior, at any scale of consideration, for the Space Station as a whole to an individual enclosure or work station. Using as a point of departure the Isovist model developed by Dr. Michael Benedikt of the U. of Texas, the report suggests that spatial habitability can become as amenable to careful assessment as engineering and life support concerns.

Hydrological model uncertainty due to spatial evapotranspiration estimation methods

Science.gov (United States)

Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub

2016-05-01

Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

Spin- and energy-dependent tunneling through a single molecule with intramolecular spatial resolution.

Science.gov (United States)

Brede, Jens; Atodiresei, Nicolae; Kuck, Stefan; Lazić, Predrag; Caciuc, Vasile; Morikawa, Yoshitada; Hoffmann, Germar; Blügel, Stefan; Wiesendanger, Roland

2010-07-23

We investigate the spin- and energy-dependent tunneling through a single organic molecule (CoPc) adsorbed on a ferromagnetic Fe thin film, spatially resolved by low-temperature spin-polarized scanning tunneling microscopy. Interestingly, the metal ion as well as the organic ligand show a significant spin dependence of tunneling current flow. State-of-the-art ab initio calculations including also van der Waals interactions reveal a strong hybridization of molecular orbitals and substrate 3d states. The molecule is anionic due to a transfer of one electron, resulting in a nonmagnetic (S=0) state. Nevertheless, tunneling through the molecule exhibits a pronounced spin dependence due to spin-split molecule-surface hybrid states.

Can spatial statistical river temperature models be transferred between catchments?

Science.gov (United States)

Jackson, Faye L.; Fryer, Robert J.; Hannah, David M.; Malcolm, Iain A.

2017-09-01

There has been increasing use of spatial statistical models to understand and predict river temperature (Tw) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses Tw data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum Tw (Twmax) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (Ta) metric (LS_Ta models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of Twmax. However, the predictions were biased when mean Twmax differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_Ta models transferred less well, with particularly poor performance when the relationship with the Ta metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between Twmax and the Ta metric, indicating that the Ta metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting Tw at unmonitored locations across

Visual dependence and spatial orientation in benign paroxysmal positional vertigo.

Science.gov (United States)

Nair, Maitreyi A; Mulavara, Ajitkumar P; Bloomberg, Jacob J; Sangi-Haghpeykar, Haleh; Cohen, Helen S

2018-01-01

People with benign paroxysmal positional vertigo (BPPV) probably have otoconial particles displaced from the utricle into the posterior semicircular canal. This unilateral change in the inertial load distributions of the labyrinth may result in visual dependence and may affect balance control. The goal of this study was to explore the interaction between visual dependence and balance control. We compared 23 healthy controls to 17 people with unilateral BPPV on the Clinical Test of Sensory Interaction and Balance on compliant foam with feet together, the Rod-and-Frame Test and a Mental Rotation Test. In controls, but not BPPV subjects, subjects with poor balance scores had significantly greater visual dependence, indicating that reliance on visual cues can affect balance control. BPPV and control subjects did not differ on the mental rotation task overall but BPPV reaction time was greater at greater orietantions, suggesting that this cognitive function was affected by BPPV. The side of impairment was strongly related to the side of perceived bias in the Earth vertical determined by BPPV subjects, indicating the relationship between the effect of asymmetric otolith unloading with simultaneous canal loading on spatial orientation perception.

Estimating temporal trend in the presence of spatial complexity: a Bayesian hierarchical model for a wetland plant population undergoing restoration.

Directory of Open Access Journals (Sweden)

Thomas J Rodhouse

Full Text Available Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas] population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations ("zones" with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity--a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach.

A spatial emergy model for Alachua County, Florida

Science.gov (United States)

Lambert, James David

A spatial model of the distribution of energy flows and storages in Alachua County, Florida, was created and used to analyze spatial patterns of energy transformation hierarchy in relation to spatial patterns of human settlement. Emergy, the available energy of one kind previously required directly or indirectly to make a product or service, was used as a measure of the quality of the different forms of energy flows and storages. Emergy provides a common unit of measure for comparing the productive contributions of natural processes with those of economic and social processes---it is an alternative to using money for measuring value. A geographic information system was used to create a spatial model and make maps that show the distribution and magnitude of different types of energy and emergy flows and storages occurring in one-hectare land units. Energy transformities were used to convert individual energy flows and storages into emergy units. Maps of transformities were created that reveal a clear spatial pattern of energy transformation hierarchy. The maps display patterns of widely-dispersed areas with lower transformity energy flows and storages, and smaller, centrally-located areas with higher transformities. Energy signature graphs and spatial unit transformities were used to characterize and compare the types and amounts of energy being consumed and stored according to land use classification, planning unit, and neighborhood categories. Emergy ratio maps and spatial unit ratios were created by dividing the values for specific emergy flows or storages by the values for other emergy flows or storages. Spatial context analysis was used to analyze the spatial distribution patterns of mean and maximum values for emergy flows and storages. The modeling method developed for this study is general and applicable to all types of landscapes and could be applied at any scale. An advantage of this general approach is that the results of other studies using this method

Investigation of Spatial Variation of Sea States Offshore of Humboldt Bay CA Using a Hindcast Model.

Energy Technology Data Exchange (ETDEWEB)

Dallman, Ann Renee; Neary, Vincent Sinclair

2014-10-01

Spatial variability of sea states is an important consideration when performing wave resource assessments and wave resource characterization studies for wave energy converter (WEC) test sites and commercial WEC deployments. This report examines the spatial variation of sea states offshore of Humboldt Bay, CA, using the wave model SWAN . The effect of depth and shoaling on bulk wave parameters is well resolved using the model SWAN with a 200 m grid. At this site, the degree of spatial variation of these bulk wave parameters, with shoaling generally perpendicular to the depth contours, is found to depend on the season. The variation in wave height , for example, was higher in the summer due to the wind and wave sheltering from the protruding land on the coastline north of the model domain. Ho wever, the spatial variation within an area of a potential Tier 1 WEC test site at 45 m depth and 1 square nautical mile is almost negligible; at most about 0.1 m in both winter and summer. The six wave characterization parameters recommended by the IEC 6 2600 - 101 TS were compared at several points along a line perpendicular to shore from the WEC test site . As expected, these parameters varied based on depth , but showed very similar seasonal trends.

Bayesian spatial transformation models with applications in neuroimaging data.

Science.gov (United States)

Miranda, Michelle F; Zhu, Hongtu; Ibrahim, Joseph G

2013-12-01

The aim of this article is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. The proposed STM include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov random field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. © 2013, The International Biometric Society.

Provincial-level spatial statistical modelling of the change in per capita disposable Family Income in Spain, 1975-1983

Directory of Open Access Journals (Sweden)

Daniel A. Griffith

1998-02-01

Full Text Available Computational simplifications for a space-time autoregressive response model specification are explored for the change in Spain's per capita disposable family income between 1975 and 1983. The geographic resolution for this analysis is the provincial partitioning of part of the Iberian peninsula into Spain's 47 coterminous provinces coupled with its 3 island clusters provinces. In keeping with the Paelinckian tradition of spatial econometrics, exploration focuses on both new spatial econometric estimators and model specifications that emphasize the capturing of spatial dependency effects in the mean response term. One goal of this analysis is to differentiate between spatial, temporal, and space-time interaction information contained in the per capita disposable family income data. A second objective of the application is to illustrate the utility of extending computational simplifications from the spatial to the space-time domain. And a third purpose is to gain some substantive insights into the economic development of one country in a changing Europe. A serendipitous outcome of this investigation is a detailed analysis of locational information latent in Spain's regionally disaggregated per capita disposable family income.

A spatial Mankiw-Romer-Weil model: Theory and evidence

OpenAIRE

Fischer, Manfred M.

2009-01-01

This paper presents a theoretical growth model that extends the Mankiw-Romer-Weil [MRW] model by accounting for technological interdependence among regional economies. Interdependence is assumed to work through spatial externalities caused by disembodied knowledge diffusion. The transition from theory to econometrics leads to a reduced-form empirical spatial Durbin model specification that explains the variation in regional levels of per worker output at steady state. A system ...

Spatial Economics Model Predicting Transport Volume

Directory of Open Access Journals (Sweden)

Lu Bo

2016-10-01

Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

A Computational Model of Spatial Development

Science.gov (United States)

Hiraki, Kazuo; Sashima, Akio; Phillips, Steven

Psychological experiments on children's development of spatial knowledge suggest experience at self-locomotion with visual tracking as important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to mentally track a target object (i.e., maintaining a representation of an object's position when outside the field-of-view) as a model for spatial development. Mental tracking is considered as prediction of an object's position given the previous environmental state and motor commands, and the current environment state resulting from movement. Following Jordan & Rumelhart's (1992) forward modeling architecture the system consists of two components: an inverse model of sensory input to desired motor commands; and a forward model of motor commands to desired sensory input (goals). The robot was tested on the `three cups' paradigm (where children are required to select the cup containing the hidden object under various movement conditions). Consistent with child development, without the capacity for self-locomotion the robot's errors are self-center based. When given the ability of self-locomotion the robot responds allocentrically.

Landscape Modelling and Simulation Using Spatial Data

Directory of Open Access Journals (Sweden)

Amjed Naser Mohsin AL-Hameedawi

2017-08-01

Full Text Available In this paper a procedure was performed for engendering spatial model of landscape acclimated to reality simulation. This procedure based on combining spatial data and field measurements with computer graphics reproduced using Blender software. Thereafter that we are possible to form a 3D simulation based on VIS ALL packages. The objective was to make a model utilising GIS, including inputs to the feature attribute data. The objective of these efforts concentrated on coordinating a tolerable spatial prototype, circumscribing facilitation scheme and outlining the intended framework. Thus; the eventual result was utilized in simulation form. The performed procedure contains not only data gathering, fieldwork and paradigm providing, but extended to supply a new method necessary to provide the respective 3D simulation mapping production, which authorises the decision makers as well as investors to achieve permanent acceptance an independent navigation system for Geoscience applications.

Comparing Spatial Predictions

KAUST Repository

Hering, Amanda S.

2011-11-01

Under a general loss function, we develop a hypothesis test to determine whether a significant difference in the spatial predictions produced by two competing models exists on average across the entire spatial domain of interest. The null hypothesis is that of no difference, and a spatial loss differential is created based on the observed data, the two sets of predictions, and the loss function chosen by the researcher. The test assumes only isotropy and short-range spatial dependence of the loss differential but does allow it to be non-Gaussian, non-zero-mean, and spatially correlated. Constant and nonconstant spatial trends in the loss differential are treated in two separate cases. Monte Carlo simulations illustrate the size and power properties of this test, and an example based on daily average wind speeds in Oklahoma is used for illustration. Supplemental results are available online. © 2011 American Statistical Association and the American Society for Qualitys.

Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance

Energy Technology Data Exchange (ETDEWEB)

Li, Ke; Chen, Guang-Hong, E-mail: gchen7@wisc.edu [Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, Wisconsin 53705 and Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, Wisconsin 53792 (United States); Garrett, John; Ge, Yongshuai [Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, Wisconsin 53705 (United States)

2014-07-15

Purpose: Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks. Methods: The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDI{sub vol} =4, 8, 12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIR (Veo{sup ®}, GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d′. Results: (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than

Competition in spatial location models

NARCIS (Netherlands)

Webers, H.M.

1996-01-01

Models of spatial competition are designed and analyzed to describe the fact that space, by its very nature, is a source of market power. This field of research, lying at the interface of game theory and economics, has attracted much interest because location problems are related to many aspects of

Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

Science.gov (United States)

Wilson, T.L.; Odei, J.B.; Hooten, M.B.; Edwards, T.C.

2010-01-01

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.

A dependence modelling study of extreme rainfall in Madeira Island

Science.gov (United States)

Gouveia-Reis, Délia; Guerreiro Lopes, Luiz; Mendonça, Sandra

2016-08-01

The dependence between variables plays a central role in multivariate extremes. In this paper, spatial dependence of Madeira Island's rainfall data is addressed within an extreme value copula approach through an analysis of maximum annual data. The impact of altitude, slope orientation, distance between rain gauge stations and distance from the stations to the sea are investigated for two different periods of time. The results obtained highlight the influence of the island's complex topography on the spatial distribution of extreme rainfall in Madeira Island.

Hippocampus-dependent spatial memory impairment due to molar tooth loss is ameliorated by an enriched environment.

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Kondo, Hiroko; Kurahashi, Minori; Mori, Daisuke; Iinuma, Mitsuo; Tamura, Yasuo; Mizutani, Kenmei; Shimpo, Kan; Sonoda, Shigeru; Azuma, Kagaku; Kubo, Kin-ya

2016-01-01

Teeth are crucial, not only for mastication, but for overall nutrition and general health, including cognitive function. Aged mice with chronic stress due to tooth loss exhibit impaired hippocampus-dependent learning and memory. Exposure to an enriched environment restores the reduced hippocampal function. Here, we explored the effects of an enriched environment on learning deficits and hippocampal morphologic changes in aged senescence-accelerated mouse strain P8 (SAMP8) mice with tooth loss. Eight-month-old male aged SAMP8 mice with molar intact or with molars removed were housed in either a standard environment or enriched environment for 3 weeks. The Morris water maze was performed for spatial memory test. The newborn cell proliferation, survival, and differentiation in the hippocampus were analyzed using 5-Bromodeoxyuridine (BrdU) immunohistochemical method. The hippocampal brain-derived neurotrophic factor (BDNF) levels were also measured. Mice with upper molars removed (molarless) exhibited a significant decline in the proliferation and survival of newborn cells in the dentate gyrus (DG) as well as in hippocampal BDNF levels. In addition, neuronal differentiation of newly generated cells was suppressed and hippocampus-dependent spatial memory was impaired. Exposure of molarless mice to an enriched environment attenuated the reductions in the hippocampal BDNF levels and neuronal differentiation, and partially improved the proliferation and survival of newborn cells, as well as the spatial memory ability. These findings indicated that an enriched environment could ameliorate the hippocampus-dependent spatial memory impairment induced by molar tooth loss. Copyright © 2015 Elsevier Ltd. All rights reserved.

Inducible Knockout of the Cyclin-Dependent Kinase 5 Activator p35 Alters Hippocampal Spatial Coding and Neuronal Excitability

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Eriko Kamiki

2018-05-01

Full Text Available p35 is an activating co-factor of Cyclin-dependent kinase 5 (Cdk5, a protein whose dysfunction has been implicated in a wide-range of neurological disorders including cognitive impairment and disease. Inducible deletion of the p35 gene in adult mice results in profound deficits in hippocampal-dependent spatial learning and synaptic physiology, however the impact of the loss of p35 function on hippocampal in vivo physiology and spatial coding remains unknown. Here, we recorded CA1 pyramidal cell activity in freely behaving p35 cKO and control mice and found that place cells in the mutant mice have elevated firing rates and impaired spatial coding, accompanied by changes in the temporal organization of spiking both during exploration and rest. These data shed light on the role of p35 in maintaining cellular and network excitability and provide a physiological correlate of the spatial learning deficits in these mice.

Inducible Knockout of the Cyclin-Dependent Kinase 5 Activator p35 Alters Hippocampal Spatial Coding and Neuronal Excitability

Science.gov (United States)

Kamiki, Eriko; Boehringer, Roman; Polygalov, Denis; Ohshima, Toshio; McHugh, Thomas J.

2018-01-01

p35 is an activating co-factor of Cyclin-dependent kinase 5 (Cdk5), a protein whose dysfunction has been implicated in a wide-range of neurological disorders including cognitive impairment and disease. Inducible deletion of the p35 gene in adult mice results in profound deficits in hippocampal-dependent spatial learning and synaptic physiology, however the impact of the loss of p35 function on hippocampal in vivo physiology and spatial coding remains unknown. Here, we recorded CA1 pyramidal cell activity in freely behaving p35 cKO and control mice and found that place cells in the mutant mice have elevated firing rates and impaired spatial coding, accompanied by changes in the temporal organization of spiking both during exploration and rest. These data shed light on the role of p35 in maintaining cellular and network excitability and provide a physiological correlate of the spatial learning deficits in these mice. PMID:29867369

The roles of scene gist and spatial dependency among objects in the semantic guidance of attention in real-world scenes.

Science.gov (United States)

Wu, Chia-Chien; Wang, Hsueh-Cheng; Pomplun, Marc

2014-12-01

A previous study (Vision Research 51 (2011) 1192-1205) found evidence for semantic guidance of visual attention during the inspection of real-world scenes, i.e., an influence of semantic relationships among scene objects on overt shifts of attention. In particular, the results revealed an observer bias toward gaze transitions between semantically similar objects. However, this effect is not necessarily indicative of semantic processing of individual objects but may be mediated by knowledge of the scene gist, which does not require object recognition, or by known spatial dependency among objects. To examine the mechanisms underlying semantic guidance, in the present study, participants were asked to view a series of displays with the scene gist excluded and spatial dependency varied. Our results show that spatial dependency among objects seems to be sufficient to induce semantic guidance. Scene gist, on the other hand, does not seem to affect how observers use semantic information to guide attention while viewing natural scenes. Extracting semantic information mainly based on spatial dependency may be an efficient strategy of the visual system that only adds little cognitive load to the viewing task. Copyright © 2014 Elsevier Ltd. All rights reserved.

Do neighbours influence value-added-tax introduction? A spatial duration analysis

NARCIS (Netherlands)

Cizek, Pavel; Lei, J.; Ligthart, J.E.

The spatial survival models typically impose frailties, which characterize unobserved heterogeneity, to be spatially correlated. However, the spatial effect may not only exist in the unobserved errors, but it can also be present in the baseline hazards and the dependent variables. A new spatial

Simulation of spatially dependent excitation rates and power deposition in RF discharges for plasma processing

International Nuclear Information System (INIS)

Kushner, M.J.; Anderson, H.M.; Hargis, P.J.

1985-01-01

In low pressure, radio frequency (RF) discharges of the type used in plasma processing of semiconductor materials, the rate of electron impact excitation and energy transfer processes depends upon both the phase of the RF excitation and position in the discharge. Electron impact collisions create radicals that diffuse or drift to the surfaces of interest where they are adsorbed or otherwise react. To the extent that these radicals have a finite lifetime, their transport time from point of creation to surface of interest is an important parameter. The spatial dependence of the rate of the initial electron impact collisions is therefore also an important parameter. The power that sustains the discharge is coupled into the system by two mechanisms: a high energy e-beam component of the electron distribution resulting from electrons falling through or being accelerated by the sheaths, and by joule heating in the body of the plasma. In this paper, the authors discuss the spatial dependence of excitation rates and the method of power deposition iin RF discharges of the type used for plasma processing

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.

Spatial modelling of disease using data- and knowledge-driven approaches.

Science.gov (United States)

Stevens, Kim B; Pfeiffer, Dirk U

2011-09-01

The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which are not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution. Copyright © 2011. Published by Elsevier Ltd.

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures.

Science.gov (United States)

Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent

2016-04-01

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Spatiotemporal Patterns in a Ratio-Dependent Food Chain Model with Reaction-Diffusion

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Lei Zhang

2014-01-01

Full Text Available Predator-prey models describe biological phenomena of pursuit-evasion interaction. And this interaction exists widely in the world for the necessary energy supplement of species. In this paper, we have investigated a ratio-dependent spatially extended food chain model. Based on the bifurcation analysis (Hopf and Turing, we give the spatial pattern formation via numerical simulation, that is, the evolution process of the system near the coexistence equilibrium point (u2*,v2*,w2*, and find that the model dynamics exhibits complex pattern replication. For fixed parameters, on increasing the control parameter c1, the sequence “holes → holes-stripe mixtures → stripes → spots-stripe mixtures → spots” pattern is observed. And in the case of pure Hopf instability, the model exhibits chaotic wave pattern replication. Furthermore, we consider the pattern formation in the case of which the top predator is extinct, that is, the evolution process of the system near the equilibrium point (u1*,v1*,0, and find that the model dynamics exhibits stripes-spots pattern replication. Our results show that reaction-diffusion model is an appropriate tool for investigating fundamental mechanism of complex spatiotemporal dynamics. It will be useful for studying the dynamic complexity of ecosystems.

A Matérn model of the spatial covariance structure of point rain rates

KAUST Repository

Sun, Ying; Bowman, Kenneth P.; Genton, Marc G.; Tokay, Ali

2014-01-01

It is challenging to model a precipitation field due to its intermittent and highly scale-dependent nature. Many models of point rain rates or areal rainfall observations have been proposed and studied for different time scales. Among them, the spectral model based on a stochastic dynamical equation for the instantaneous point rain rate field is attractive, since it naturally leads to a consistent space–time model. In this paper, we note that the spatial covariance structure of the spectral model is equivalent to the well-known Matérn covariance model. Using high-quality rain gauge data, we estimate the parameters of the Matérn model for different time scales and demonstrate that the Matérn model is superior to an exponential model, particularly at short time scales.

A Matérn model of the spatial covariance structure of point rain rates

KAUST Repository

Sun, Ying

2014-07-15

It is challenging to model a precipitation field due to its intermittent and highly scale-dependent nature. Many models of point rain rates or areal rainfall observations have been proposed and studied for different time scales. Among them, the spectral model based on a stochastic dynamical equation for the instantaneous point rain rate field is attractive, since it naturally leads to a consistent space–time model. In this paper, we note that the spatial covariance structure of the spectral model is equivalent to the well-known Matérn covariance model. Using high-quality rain gauge data, we estimate the parameters of the Matérn model for different time scales and demonstrate that the Matérn model is superior to an exponential model, particularly at short time scales.

A novel spatial performance metric for robust pattern optimization of distributed hydrological models

Science.gov (United States)

Stisen, S.; Demirel, C.; Koch, J.

2017-12-01

Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing

Spatial Development Modeling Methodology Application Possibilities in Vilnius

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Lina Panavaitė

2017-05-01

Full Text Available In order to control the continued development of high-rise buildings and their irreversible visual impact on the overall silhouette of the city, the great cities of the world introduced new methodological principles to city’s spatial development models. These methodologies and spatial planning guidelines are focused not only on the controlled development of high-rise buildings, but on the spatial modelling of the whole city by defining main development criteria and estimating possible consequences. Vilnius city is no exception, however the re-establishment of independence of Lithuania caused uncontrolled urbanization process, so most of the city development regulations emerged as a consequence of unmanaged processes of investors’ expectations legalization. The importance of consistent urban fabric as well as conservation and representation of city’s most important objects gained attention only when an actual threat of overshadowing them with new architecture along with unmanaged urbanization in the city center or urban sprawl at suburbia, caused by land-use projects, had emerged. Current Vilnius’ spatial planning documents clearly define urban structure and key development principles, however the definitions are relatively abstract, causing uniform building coverage requirements for territories with distinct qualities and simplifying planar designs which do not meet quality standards. The overall quality of urban architecture is not regulated. The article deals with current spatial modeling methods, their individual parts, principles, the criteria for quality assessment and their applicability in Vilnius. The text contains an outline of possible building coverage regulations and impact assessment criteria for new development. The article contains a compendium of requirements for high-quality spatial planning and building design.

Spatial Models and Networks of Living Systems

DEFF Research Database (Denmark)

Juul, Jeppe Søgaard

When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number...... variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...

Spatial Preference Modelling for equitable infrastructure provision: an application of Sen's Capability Approach

Science.gov (United States)

Wismadi, Arif; Zuidgeest, Mark; Brussel, Mark; van Maarseveen, Martin

2014-01-01

To determine whether the inclusion of spatial neighbourhood comparison factors in Preference Modelling allows spatial decision support systems (SDSSs) to better address spatial equity, we introduce Spatial Preference Modelling (SPM). To evaluate the effectiveness of this model in addressing equity, various standardisation functions in both Non-Spatial Preference Modelling and SPM are compared. The evaluation involves applying the model to a resource location-allocation problem for transport infrastructure in the Special Province of Yogyakarta in Indonesia. We apply Amartya Sen's Capability Approach to define opportunity to mobility as a non-income indicator. Using the extended Moran's I interpretation for spatial equity, we evaluate the distribution output regarding, first, `the spatial distribution patterns of priority targeting for allocation' (SPT) and, second, `the effect of new distribution patterns after location-allocation' (ELA). The Moran's I index of the initial map and its comparison with six patterns for SPT as well as ELA consistently indicates that the SPM is more effective for addressing spatial equity. We conclude that the inclusion of spatial neighbourhood comparison factors in Preference Modelling improves the capability of SDSS to address spatial equity. This study thus proposes a new formal method for SDSS with specific attention on resource location-allocation to address spatial equity.

Modeling a secular trend by Monte Carlo simulation of height biased migration in a spatial network.

Science.gov (United States)

Groth, Detlef

2017-04-01

Background: In a recent Monte Carlo simulation, the clustering of body height of Swiss military conscripts within a spatial network with characteristic features of the natural Swiss geography was investigated. In this study I examined the effect of migration of tall individuals into network hubs on the dynamics of body height within the whole spatial network. The aim of this study was to simulate height trends. Material and methods: Three networks were used for modeling, a regular rectangular fishing net like network, a real world example based on the geographic map of Switzerland, and a random network. All networks contained between 144 and 148 districts and between 265-307 road connections. Around 100,000 agents were initially released with average height of 170 cm, and height standard deviation of 6.5 cm. The simulation was started with the a priori assumption that height variation within a district is limited and also depends on height of neighboring districts (community effect on height). In addition to a neighborhood influence factor, which simulates a community effect, body height dependent migration of conscripts between adjacent districts in each Monte Carlo simulation was used to re-calculate next generation body heights. In order to determine the direction of migration for taller individuals, various centrality measures for the evaluation of district importance within the spatial network were applied. Taller individuals were favored to migrate more into network hubs, backward migration using the same number of individuals was random, not biased towards body height. Network hubs were defined by the importance of a district within the spatial network. The importance of a district was evaluated by various centrality measures. In the null model there were no road connections, height information could not be delivered between the districts. Results: Due to the favored migration of tall individuals into network hubs, average body height of the hubs, and later

Is a matrix exponential specification suitable for the modeling of spatial correlation structures?

Science.gov (United States)

Strauß, Magdalena E; Mezzetti, Maura; Leorato, Samantha

2017-05-01

This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an alternative to the widely used spatial autoregressive models (SAR). To provide as complete a picture as possible, we extend the analysis to all the main spatial models governed by matrix exponentials comparing them with their spatial autoregressive counterparts. We propose a new implementation of Bayesian parameter estimation for the MESS model with vague prior distributions, which is shown to be precise and computationally efficient. Our implementations also account for spatially lagged regressors. We further allow for location-specific heterogeneity, which we model by including spatial splines. We conclude by comparing the performances of the different model specifications in applications to a real data set and by running simulations. Both the applications and the simulations suggest that the spatial splines are a flexible and efficient way to account for spatial heterogeneities governed by unknown mechanisms.

Revealing the Driving Forces of Mid-Cities Urban Growth Patterns Using Spatial Modeling: a Case Study of Los Ángeles, Chile

Directory of Open Access Journals (Sweden)

Mauricio I. Aguayo

2007-06-01

Full Text Available City growth and changes in land-use patterns cause various important social and environmental impacts. To understand the spatial and temporal dynamics of these processes, the factors that drive urban development must be identified and analyzed, especially those factors that can be used to predict future changes and their potential environmental effects. Our objectives were to quantify the relationship between urban growth and its driving forces and to predict the spatial growth pattern based on historical land-use changes for the city of Los Ángeles in central Chile. This involved the analysis of images from 1978, 1992, and 1998 and characterization of the spatial pattern of land-use change; the construction of digital coverage in GIS; the selection of predictive variables through univariate analysis; the construction of logistic regression models using growth vs. nongrowth for 1978-1992 as the dependent variable; and the prediction of the probability of land-use change by applying the regression model to the 1992-1998 period. To investigate the influence of spatial scale, we constructed several sets of models that contained (1 only distance variables, e.g., distance to highways; (2 only scale-dependent density variables, e.g., density of urban area within a 600-m radius; (3 both distance and density variables; and (4 both distance and density variables at several spatial scales. The environmental variables were included in all models. The combination of distance and density variables at several scales is required to appropriately capture the multiscale urban growth process. The best models correctly predict ~90% of the observed land-use changes for 1992-1998. The distance to access roads, densities of the urban road system and urbanized area at various scales, and soil type were the strongest predictors of the growth pattern. Other variables were less important or not significant in explaining the urban growth process. Our approach, which

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.

Rockfall hazard analysis using LiDAR and spatial modeling

Science.gov (United States)

Lan, Hengxing; Martin, C. Derek; Zhou, Chenghu; Lim, Chang Ho

2010-05-01

Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a high-resolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy.

Modelling the Spatial Isotope Variability of Precipitation in Syria

Energy Technology Data Exchange (ETDEWEB)

Kattan, Z.; Kattaa, B. [Department of Geology, Atomic Energy Commission of Syria (AECS), Damascus (Syrian Arab Republic)

2013-07-15

Attempts were made to model the spatial variability of environmental isotope ({sup 18}O, {sup 2}H and {sup 3}H) compositions of precipitation in syria. Rainfall samples periodically collected on a monthly basis from 16 different stations were used for processing and demonstrating the spatial distributions of these isotopes, together with those of deuterium excess (d) values. Mathematically, the modelling process was based on applying simple polynomial models that take into consideration the effects of major geographic factors (Lon.E., Lat.N., and altitude). The modelling results of spatial distribution of stable isotopes ({sup 18}O and {sup 2}H) were generally good, as shown from the high correlation coefficients (R{sup 2} = 0.7-0.8), calculated between the observed and predicted values. In the case of deuterium excess and tritium distributions, the results were most likely approximates (R{sup 2} = 0.5-0.6). Improving the simulation of spatial isotope variability probably requires the incorporation of other local meteorological factors, such as relative air humidity, precipitation amount and vapour pressure, which are supposed to play an important role in such an arid country. (author)

BOLD repetition decreases in object-responsive ventral visual areas depend on spatial attention.

Science.gov (United States)

Eger, E; Henson, R N A; Driver, J; Dolan, R J

2004-08-01

Functional imaging studies of priming-related repetition phenomena have become widely used to study neural object representation. Although blood oxygenation level-dependent (BOLD) repetition decreases can sometimes be observed without awareness of repetition, any role for spatial attention in BOLD repetition effects remains largely unknown. We used fMRI in 13 healthy subjects to test whether BOLD repetition decreases for repeated objects in ventral visual cortices depend on allocation of spatial attention to the prime. Subjects performed a size-judgment task on a probe object that had been attended or ignored in a preceding prime display of 2 lateralized objects. Reaction times showed faster responses when the probe was the same object as the attended prime, independent of the view tested (identical vs. mirror image). No behavioral effect was evident from unattended primes. BOLD repetition decreases for attended primes were found in lateral occipital and fusiform regions bilaterally, which generalized across identical and mirror-image repeats. No repetition decreases were observed for ignored primes. Our results suggest a critical role for attention in achieving visual representations of objects that lead to both BOLD signal decreases and behavioral priming on repeated presentation.

Spatial heterogeneity, frequency-dependent selection and polymorphism in host-parasite interactions

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Tellier Aurélien

2011-11-01

Full Text Available Abstract Background Genomic and pathology analysis has revealed enormous diversity in genes involved in disease, including those encoding host resistance and parasite effectors (also known in plant pathology as avirulence genes. It has been proposed that such variation may persist when an organism exists in a spatially structured metapopulation, following the geographic mosaic of coevolution. Here, we study gene-for-gene relationships governing the outcome of plant-parasite interactions in a spatially structured system and, in particular, investigate the population genetic processes which maintain balanced polymorphism in both species. Results Following previous theory on the effect of heterogeneous environments on maintenance of polymorphism, we analysed a model with two demes in which the demes have different environments and are coupled by gene flow. Environmental variation is manifested by different coefficients of natural selection, the costs to the host of resistance and to the parasite of virulence, the cost to the host of being diseased and the cost to an avirulent parasite of unsuccessfully attacking a resistant host. We show that migration generates negative direct frequency-dependent selection, a condition for maintenance of stable polymorphism in each deme. Balanced polymorphism occurs preferentially if there is heterogeneity for costs of resistance and virulence alleles among populations and to a lesser extent if there is variation in the cost to the host of being diseased. We show that the four fitness costs control the natural frequency of oscillation of host resistance and parasite avirulence alleles. If demes have different costs, their frequencies of oscillation differ and when coupled by gene flow, there is amplitude death of the oscillations in each deme. Numerical simulations show that for a multiple deme island model, costs of resistance and virulence need not to be present in each deme for stable polymorphism to occur

Spatial Fleming-Viot models with selection and mutation

CERN Document Server

Dawson, Donald A

2014-01-01

This book constructs a rigorous framework for analysing selected phenomena in evolutionary theory of populations arising due to the combined effects of migration, selection and mutation in a spatial stochastic population model, namely the evolution towards fitter and fitter types through punctuated equilibria. The discussion is based on a number of new methods, in particular multiple scale analysis, nonlinear Markov processes and their entrance laws, atomic measure-valued evolutions and new forms of duality (for state-dependent mutation and multitype selection) which are used to prove ergodic theorems in this context and are applicable for many other questions and renormalization analysis for a variety of phenomena (stasis, punctuated equilibrium, failure of naive branching approximations, biodiversity) which occur due to the combination of rare mutation, mutation, resampling, migration and selection and make it necessary to mathematically bridge the gap (in the limit) between time and space scales.

Spatially Informed Plant PRA Models for Security Assessment

International Nuclear Information System (INIS)

Wheeler, Timothy A.; Thomas, Willard; Thornsbury, Eric

2006-01-01

Traditional risk models can be adapted to evaluate plant response for situations where plant systems and structures are intentionally damaged, such as from sabotage or terrorism. This paper describes a process by which traditional risk models can be spatially informed to analyze the effects of compound and widespread harsh environments through the use of 'damage footprints'. A 'damage footprint' is a spatial map of regions of the plant (zones) where equipment could be physically destroyed or disabled as a direct consequence of an intentional act. The use of 'damage footprints' requires that the basic events from the traditional probabilistic risk assessment (PRA) be spatially transformed so that the failure of individual components can be linked to the destruction of or damage to specific spatial zones within the plant. Given the nature of intentional acts, extensive modifications must be made to the risk models to account for the special nature of the 'initiating events' associated with deliberate adversary actions. Intentional acts might produce harsh environments that in turn could subject components and structures to one or more insults, such as structural, fire, flood, and/or vibration and shock damage. Furthermore, the potential for widespread damage from some of these insults requires an approach that addresses the impacts of these potentially severe insults even when they occur in locations distant from the actual physical location of a component or structure modeled in the traditional PRA. (authors)

A minimal spatial cell lineage model of epithelium: tissue stratification and multi-stability

Science.gov (United States)

Yeh, Wei-Ting; Chen, Hsuan-Yi

2018-05-01

A minimal model which includes spatial and cell lineage dynamics for stratified epithelia is presented. The dependence of tissue steady state on cell differentiation models, cell proliferation rate, cell differentiation rate, and other parameters are studied numerically and analytically. Our minimal model shows some important features. First, we find that morphogen or mechanical stress mediated interaction is necessary to maintain a healthy stratified epithelium. Furthermore, comparing with tissues in which cell differentiation can take place only during cell division, tissues in which cell division and cell differentiation are decoupled can achieve relatively higher degree of stratification. Finally, our model also shows that in the presence of short-range interactions, it is possible for a tissue to have multiple steady states. The relation between our results and tissue morphogenesis or lesion is discussed.

Direct visualization of electroporation-assisted in vivo gene delivery to tumors using intravital microscopy – spatial and time dependent distribution

International Nuclear Information System (INIS)

Cemazar, Maja; Wilson, Ian; Dachs, Gabi U; Tozer, Gillian M; Sersa, Gregor

2004-01-01

Electroporation is currently receiving much attention as a way to increase drug and DNA delivery. Recent studies demonstrated the feasibility of electrogene therapy using a range of therapeutic genes for the treatment of experimental tumors. However, the transfection efficiency of electroporation-assisted DNA delivery is still low compared to viral methods and there is a clear need to optimize this approach. In order to optimize treatment, knowledge about spatial and time dependency of gene expression following delivery is of utmost importance in order to improve gene delivery. Intravital microscopy of tumors growing in dorsal skin fold window chambers is a useful method for monitoring gene transfection, since it allows non-invasive dynamic monitoring of gene expression in tumors in a live animal. Intravital microscopy was used to monitor real time spatial distribution of the green fluorescent protein (GFP) and time dependence of transfection efficiency in syngeneic P22 rat tumor model. DNA alone, liposome-DNA complexes and electroporation-assisted DNA delivery using two different sets of electric pulse parameters were compared. Electroporation-assisted DNA delivery using 8 pulses, 600 V/cm, 5 ms, 1 Hz was superior to other methods and resulted in 22% increase in fluorescence intensity in the tumors up to 6 days post-transfection, compared to the non-transfected area in granulation tissue. Functional GFP was detected within 5 h after transfection. Cells expressing GFP were detected throughout the tumor, but not in the surrounding tissue that was not exposed to electric pulses. Intravital microscopy was demonstrated to be a suitable method for monitoring time and spatial distribution of gene expression in experimental tumors and provided evidence that electroporation-assisted gene delivery using 8 pulses, 600 V/cm, 5 ms, 1 Hz is an effective method, resulting in early onset and homogenous distribution of gene expression in the syngeneic P22 rat tumor model

Applying Boundary Conditions Using a Time-Dependent Lagrangian for Modeling Laser-Plasma Interactions

Science.gov (United States)

Reyes, Jonathan; Shadwick, B. A.

2016-10-01

Modeling the evolution of a short, intense laser pulse propagating through an underdense plasma is of particular interest in the physics of laser-plasma interactions. Numerical models are typically created by first discretizing the equations of motion and then imposing boundary conditions. Using the variational principle of Chen and Sudan, we spatially discretize the Lagrangian density to obtain discrete equations of motion and a discrete energy conservation law which is exactly satisfied regardless of the spatial grid resolution. Modifying the derived equations of motion (e.g., enforcing boundary conditions) generally ruins energy conservation. However, time-dependent terms can be added to the Lagrangian which force the equations of motion to have the desired boundary conditions. Although some foresight is needed to choose these time-dependent terms, this approach provides a mechanism for energy to exit the closed system while allowing the conservation law to account for the loss. An appropriate time discretization scheme is selected based on stability analysis and resolution requirements. We present results using this variational approach in a co-moving coordinate system and compare such results to those using traditional second-order methods. This work was supported by the U. S. Department of Energy under Contract No. DE-SC0008382 and by the National Science Foundation under Contract No. PHY- 1104683.

Analysing the distribution of synaptic vesicles using a spatial point process model

DEFF Research Database (Denmark)

Khanmohammadi, Mahdieh; Waagepetersen, Rasmus; Nava, Nicoletta

2014-01-01

functionality by statistically modelling the distribution of the synaptic vesicles in two groups of rats: a control group subjected to sham stress and a stressed group subjected to a single acute foot-shock (FS)-stress episode. We hypothesize that the synaptic vesicles have different spatial distributions...... in the two groups. The spatial distributions are modelled using spatial point process models with an inhomogeneous conditional intensity and repulsive pairwise interactions. Our results verify the hypothesis that the two groups have different spatial distributions....

Criterion of Semi-Markov Dependent Risk Model

Institute of Scientific and Technical Information of China (English)

Xiao Yun MO; Xiang Qun YANG

2014-01-01

A rigorous definition of semi-Markov dependent risk model is given. This model is a generalization of the Markov dependent risk model. A criterion and necessary conditions of semi-Markov dependent risk model are obtained. The results clarify relations between elements among semi-Markov dependent risk model more clear and are applicable for Markov dependent risk model.

Modeling the frequency-dependent detective quantum efficiency of photon-counting x-ray detectors.

Science.gov (United States)

Stierstorfer, Karl

2018-01-01

To find a simple model for the frequency-dependent detective quantum efficiency (DQE) of photon-counting detectors in the low flux limit. Formula for the spatial cross-talk, the noise power spectrum and the DQE of a photon-counting detector working at a given threshold are derived. Parameters are probabilities for types of events like single counts in the central pixel, double counts in the central pixel and a neighboring pixel or single count in a neighboring pixel only. These probabilities can be derived in a simple model by extensive use of Monte Carlo techniques: The Monte Carlo x-ray propagation program MOCASSIM is used to simulate the energy deposition from the x-rays in the detector material. A simple charge cloud model using Gaussian clouds of fixed width is used for the propagation of the electric charge generated by the primary interactions. Both stages are combined in a Monte Carlo simulation randomizing the location of impact which finally produces the required probabilities. The parameters of the charge cloud model are fitted to the spectral response to a polychromatic spectrum measured with our prototype detector. Based on the Monte Carlo model, the DQE of photon-counting detectors as a function of spatial frequency is calculated for various pixel sizes, photon energies, and thresholds. The frequency-dependent DQE of a photon-counting detector in the low flux limit can be described with an equation containing only a small set of probabilities as input. Estimates for the probabilities can be derived from a simple model of the detector physics. © 2017 American Association of Physicists in Medicine.

Predictive spatio-temporal model for spatially sparse global solar radiation data

International Nuclear Information System (INIS)

André, Maïna; Dabo-Niang, Sophie; Soubdhan, Ted; Ould-Baba, Hanany

2016-01-01

This paper introduces a new approach for the forecasting of solar radiation series at a located station for very short time scale. We built a multivariate model in using few stations (3 stations) separated with irregular distances from 26 km to 56 km. The proposed model is a spatio temporal vector autoregressive VAR model specifically designed for the analysis of spatially sparse spatio-temporal data. This model differs from classic linear models in using spatial and temporal parameters where the available predictors are the lagged values at each station. A spatial structure of stations is defined by the sequential introduction of predictors in the model. Moreover, an iterative strategy in the process of our model will select the necessary stations removing the uninteresting predictors and also selecting the optimal p-order. We studied the performance of this model. The metric error, the relative root mean squared error (rRMSE), is presented at different short time scales. Moreover, we compared the results of our model to simple and well known persistence model and those found in literature. - Highlights: • A spatio-temporal VAR forecast model is used for spatially sparse data solar. • Lags and locations are selected by an optimization strategy. • Definition of spatial ordering of predictors influences forecasting results. • The model shows a better performance predictive at 30 min ahead in our context. • Benchmarking study shows a more accurate forecast at 1 h ahead with spatio-temporal VAR.

Panchromatic SED modelling of spatially resolved galaxies

Science.gov (United States)

Smith, Daniel J. B.; Hayward, Christopher C.

2018-05-01

We test the efficacy of the energy-balance spectral energy distribution (SED) fitting code MAGPHYS for recovering the spatially resolved properties of a simulated isolated disc galaxy, for which it was not designed. We perform 226 950 MAGPHYS SED fits to regions between 0.2 and 25 kpc in size across the galaxy's disc, viewed from three different sight-lines, to probe how well MAGPHYS can recover key galaxy properties based on 21 bands of UV-far-infrared model photometry. MAGPHYS yields statistically acceptable fits to >99 per cent of the pixels within the r-band effective radius and between 59 and 77 percent of pixels within 20 kpc of the nucleus. MAGPHYS is able to recover the distribution of stellar mass, star formation rate (SFR), specific SFR, dust luminosity, dust mass, and V-band attenuation reasonably well, especially when the pixel size is ≳ 1 kpc, whereas non-standard outputs (stellar metallicity and mass-weighted age) are recovered less well. Accurate recovery is more challenging in the smallest sub-regions of the disc (pixel scale ≲ 1 kpc), where the energy balance criterion becomes increasingly incorrect. Estimating integrated galaxy properties by summing the recovered pixel values, the true integrated values of all parameters considered except metallicity and age are well recovered at all spatial resolutions, ranging from 0.2 kpc to integrating across the disc, albeit with some evidence for resolution-dependent biases. These results must be considered when attempting to analyse the structure of real galaxies with actual observational data, for which the `ground truth' is unknown.

Comparison of alternative spatial resolutions in the application of a spatially distributed biogeochemical model over complex terrain

Science.gov (United States)

Turner, D.P.; Dodson, R.; Marks, D.

1996-01-01

Spatially distributed biogeochemical models may be applied over grids at a range of spatial resolutions, however, evaluation of potential errors and loss of information at relatively coarse resolutions is rare. In this study, a georeferenced database at the 1-km spatial resolution was developed to initialize and drive a process-based model (Forest-BGC) of water and carbon balance over a gridded 54976 km2 area covering two river basins in mountainous western Oregon. Corresponding data sets were also prepared at 10-km and 50-km spatial resolutions using commonly employed aggregation schemes. Estimates were made at each grid cell for climate variables including daily solar radiation, air temperature, humidity, and precipitation. The topographic structure, water holding capacity, vegetation type and leaf area index were likewise estimated for initial conditions. The daily time series for the climatic drivers was developed from interpolations of meteorological station data for the water year 1990 (1 October 1989-30 September 1990). Model outputs at the 1-km resolution showed good agreement with observed patterns in runoff and productivity. The ranges for model inputs at the 10-km and 50-km resolutions tended to contract because of the smoothed topography. Estimates for mean evapotranspiration and runoff were relatively insensitive to changing the spatial resolution of the grid whereas estimates of mean annual net primary production varied by 11%. The designation of a vegetation type and leaf area at the 50-km resolution often subsumed significant heterogeneity in vegetation, and this factor accounted for much of the difference in the mean values for the carbon flux variables. Although area wide means for model outputs were generally similar across resolutions, difference maps often revealed large areas of disagreement. Relatively high spatial resolution analyses of biogeochemical cycling are desirable from several perspectives and may be particularly important in the

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.

Modeling Soil Carbon Dynamics in Northern Forests: Effects of Spatial and Temporal Aggregation of Climatic Input Data.

Science.gov (United States)

Dalsgaard, Lise; Astrup, Rasmus; Antón-Fernández, Clara; Borgen, Signe Kynding; Breidenbach, Johannes; Lange, Holger; Lehtonen, Aleksi; Liski, Jari

2016-01-01

Boreal forests contain 30% of the global forest carbon with the majority residing in soils. While challenging to quantify, soil carbon changes comprise a significant, and potentially increasing, part of the terrestrial carbon cycle. Thus, their estimation is important when designing forest-based climate change mitigation strategies and soil carbon change estimates are required for the reporting of greenhouse gas emissions. Organic matter decomposition varies with climate in complex nonlinear ways, rendering data aggregation nontrivial. Here, we explored the effects of temporal and spatial aggregation of climatic and litter input data on regional estimates of soil organic carbon stocks and changes for upland forests. We used the soil carbon and decomposition model Yasso07 with input from the Norwegian National Forest Inventory (11275 plots, 1960-2012). Estimates were produced at three spatial and three temporal scales. Results showed that a national level average soil carbon stock estimate varied by 10% depending on the applied spatial and temporal scale of aggregation. Higher stocks were found when applying plot-level input compared to country-level input and when long-term climate was used as compared to annual or 5-year mean values. A national level estimate for soil carbon change was similar across spatial scales, but was considerably (60-70%) lower when applying annual or 5-year mean climate compared to long-term mean climate reflecting the recent climatic changes in Norway. This was particularly evident for the forest-dominated districts in the southeastern and central parts of Norway and in the far north. We concluded that the sensitivity of model estimates to spatial aggregation will depend on the region of interest. Further, that using long-term climate averages during periods with strong climatic trends results in large differences in soil carbon estimates. The largest differences in this study were observed in central and northern regions with strongly

Spatial Modeling for Resources Framework (SMRF)

Science.gov (United States)

Spatial Modeling for Resources Framework (SMRF) was developed by Dr. Scott Havens at the USDA Agricultural Research Service (ARS) in Boise, ID. SMRF was designed to increase the flexibility of taking measured weather data and distributing the point measurements across a watershed. SMRF was developed...

Systematic spatial bias in DNA microarray hybridization is caused by probe spot position-dependent variability in lateral diffusion.

Science.gov (United States)

Steger, Doris; Berry, David; Haider, Susanne; Horn, Matthias; Wagner, Michael; Stocker, Roman; Loy, Alexander

2011-01-01

The hybridization of nucleic acid targets with surface-immobilized probes is a widely used assay for the parallel detection of multiple targets in medical and biological research. Despite its widespread application, DNA microarray technology still suffers from several biases and lack of reproducibility, stemming in part from an incomplete understanding of the processes governing surface hybridization. In particular, non-random spatial variations within individual microarray hybridizations are often observed, but the mechanisms underpinning this positional bias remain incompletely explained. This study identifies and rationalizes a systematic spatial bias in the intensity of surface hybridization, characterized by markedly increased signal intensity of spots located at the boundaries of the spotted areas of the microarray slide. Combining observations from a simplified single-probe block array format with predictions from a mathematical model, the mechanism responsible for this bias is found to be a position-dependent variation in lateral diffusion of target molecules. Numerical simulations reveal a strong influence of microarray well geometry on the spatial bias. Reciprocal adjustment of the size of the microarray hybridization chamber to the area of surface-bound probes is a simple and effective measure to minimize or eliminate the diffusion-based bias, resulting in increased uniformity and accuracy of quantitative DNA microarray hybridization.

Benefits of incorporating spatial organisation of catchments for a semi-distributed hydrological model

Science.gov (United States)

Schumann, Andreas; Oppel, Henning

2017-04-01

To represent the hydrological behaviour of catchments a model should reproduce/reflect the hydrologically most relevant catchment characteristics. These are heterogeneously distributed within a watershed but often interrelated and subject of a certain spatial organisation. Since common models are mostly based on fundamental assumptions about hydrological processes, the reduction of variance of catchment properties as well as the incorporation of the spatial organisation of the catchment is desirable. We have developed a method that combines the idea of the width-function used for determination of the geomorphologic unit hydrograph with information about soil or topography. With this method we are able to assess the spatial organisation of selected catchment characteristics. An algorithm was developed that structures a watershed into sub-basins and other spatial units to minimise its heterogeneity. The outcomes of this algorithm are used for the spatial setup of a semi-distributed model. Since the spatial organisation of a catchment is not bound to a single characteristic, we have to embed information of multiple catchment properties. For this purpose we applied a fuzzy-based method to combine the spatial setup for multiple single characteristics into a union, optimal spatial differentiation. Utilizing this method, we are able to propose a spatial structure for a semi-distributed hydrological model, comprising the definition of sub-basins and a zonal classification within each sub-basin. Besides the improved spatial structuring, the performed analysis ameliorates modelling in another way. The spatial variability of catchment characteristics, which is considered by a minimum of heterogeneity in the zones, can be considered in a parameter constrained calibration scheme in a case study both options were used to explore the benefits of incorporating the spatial organisation and derived parameter constraints for the parametrisation of a HBV-96 model. We use two benchmark

The importance of spatial ability and mental models in learning anatomy

Science.gov (United States)

Chatterjee, Allison K.

As a foundational course in medical education, gross anatomy serves to orient medical and veterinary students to the complex three-dimensional nature of the structures within the body. Understanding such spatial relationships is both fundamental and crucial for achievement in gross anatomy courses, and is essential for success as a practicing professional. Many things contribute to learning spatial relationships; this project focuses on a few key elements: (1) the type of multimedia resources, particularly computer-aided instructional (CAI) resources, medical students used to study and learn; (2) the influence of spatial ability on medical and veterinary students' gross anatomy grades and their mental models; and (3) how medical and veterinary students think about anatomy and describe the features of their mental models to represent what they know about anatomical structures. The use of computer-aided instruction (CAI) by gross anatomy students at Indiana University School of Medicine (IUSM) was assessed through a questionnaire distributed to the regional centers of the IUSM. Students reported using internet browsing, PowerPoint presentation software, and email on a daily bases to study gross anatomy. This study reveals that first-year medical students at the IUSM make limited use of CAI to study gross anatomy. Such studies emphasize the importance of examining students' use of CAI to study gross anatomy prior to development and integration of electronic media into the curriculum and they may be important in future decisions regarding the development of alternative learning resources. In order to determine how students think about anatomical relationships and describe the features of their mental models, personal interviews were conducted with select students based on students' ROT scores. Five typologies of the characteristics of students' mental models were identified and described: spatial thinking, kinesthetic approach, identification of anatomical structures

The impact of design-based modeling instruction on seventh graders' spatial abilities and model-based argumentation

Science.gov (United States)

McConnell, William J.

Due to the call of current science education reform for the integration of engineering practices within science classrooms, design-based instruction is receiving much attention in science education literature. Although some aspect of modeling is often included in well-known design-based instructional methods, it is not always a primary focus. The purpose of this study was to better understand how design-based instruction with an emphasis on scientific modeling might impact students' spatial abilities and their model-based argumentation abilities. In the following mixed-method multiple case study, seven seventh grade students attending a secular private school in the Mid-Atlantic region of the United States underwent an instructional intervention involving design-based instruction, modeling and argumentation. Through the course of a lesson involving students in exploring the interrelatedness of the environment and an animal's form and function, students created and used multiple forms of expressed models to assist them in model-based scientific argument. Pre/post data were collected through the use of The Purdue Spatial Visualization Test: Rotation, the Mental Rotation Test and interviews. Other data included a spatial activities survey, student artifacts in the form of models, notes, exit tickets, and video recordings of students throughout the intervention. Spatial abilities tests were analyzed using descriptive statistics while students' arguments were analyzed using the Instrument for the Analysis of Scientific Curricular Arguments and a behavior protocol. Models were analyzed using content analysis and interviews and all other data were coded and analyzed for emergent themes. Findings in the area of spatial abilities included increases in spatial reasoning for six out of seven participants, and an immense difference in the spatial challenges encountered by students when using CAD software instead of paper drawings to create models. Students perceived 3D printed

Nonparametric Bayesian models for a spatial covariance.

Science.gov (United States)

Reich, Brian J; Fuentes, Montserrat

2012-01-01

A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.

Context-dependent modulation of hippocampal and cortical recruitment during remote spatial memory retrieval.

Science.gov (United States)

Lopez, Joëlle; Herbeaux, Karin; Cosquer, Brigitte; Engeln, Michel; Muller, Christophe; Lazarus, Christine; Kelche, Christian; Bontempi, Bruno; Cassel, Jean-Christophe; de Vasconcelos, Anne Pereira

2012-04-01

According to systems consolidation, as hippocampal-dependent memories mature over time, they become additionally (or exclusively) dependent on extra-hippocampal structures. We assessed the recruitment of hippocampal and cortical structures on remote memory retrieval in a performance-degradation resistant (PDR; no performance degradation with time) versus performance-degradation prone (PDP; performance degraded with time) context. Using a water-maze task in two contexts with a hidden platform and three control conditions (home cage, visible platform with or without access to distal cues), we compared neuronal activation (c-Fos imaging) patterns in the dorsal hippocampus and the medial prefrontal cortex (mPFC) after the retrieval of recent (5 days) versus remote (25 days) spatial memory. In the PDR context, the hippocampus exhibited greater c-Fos protein expression on remote than recent memory retrieval, be it in the visible or hidden platform group. In the PDP context, hippocampal activation increased at the remote time point and only in the hidden platform group. In the anterior cingulate cortex, c-Fos expression was greater for remote than for recent memory retrieval and only in the PDR context. The necessity of the mPFC for remote memory retrieval in the PDR context was confirmed using region-specific lidocaine inactivation, which had no impact on recent memory. Conversely, inactivation of the dorsal hippocampus impaired both recent and remote memory in the PDR context, and only recent memory in the PDP context, in which remote memory performance was degraded. While confirming that neuronal circuits supporting spatial memory consolidation are reorganized in a time-dependent manner, our findings further indicate that mPFC and hippocampus recruitment (i) depends on the content and perhaps the strength of the memory and (ii) may be influenced by the environmental conditions (e.g., cue saliency, complexity) in which memories are initially formed and subsequently

Gaussian Process Regression Model in Spatial Logistic Regression

Science.gov (United States)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

China's Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model.

Science.gov (United States)

Cao, Qilong; Liang, Ying; Niu, Xueting

2017-09-18

Background : Air pollution has become an important factor restricting China's economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods : Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM 2.5 . Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results : It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM 2.5 pollutions in the control of other variables. Conclusions : Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables.

Spatial Modeling of Deforestation in FMU of Poigar, North Sulawesi

OpenAIRE

Ahmad, Afandi; Saleh, Muhammad Buce; Rusolono, Teddy

2016-01-01

Forest is a part of the ecosystem that provides environmental services. Deforestation may decrease forest function in an ecosystem. This study aims to build a spatial model of deforestation in a forest management unit (FMU) of Poigar. Deforestation analysis carried out by analyze the change of forest cover into non-forest cover with post classification comparison technique. Driving forces of deforestation carried out by spatial modeling using binary logistic regression models (LRM). Result of...

Velocity correlations and spatial dependencies between neighbors in a unidirectional flow of pedestrians

Science.gov (United States)

Porzycki, Jakub; WÄ s, Jarosław; Hedayatifar, Leila; Hassanibesheli, Forough; Kułakowski, Krzysztof

2017-08-01

The aim of the paper is an analysis of self-organization patterns observed in the unidirectional flow of pedestrians. On the basis of experimental data from Zhang et al. [J. Zhang et al., J. Stat. Mech. (2011) P06004, 10.1088/1742-5468/2011/06/P06004], we analyze the mutual positions and velocity correlations between pedestrians when walking along a corridor. The angular and spatial dependencies of the mutual positions reveal a spatial structure that remains stable during the crowd motion. This structure differs depending on the value of n , for the consecutive n th -nearest-neighbor position set. The preferred position for the first-nearest neighbor is on the side of the pedestrian, while for further neighbors, this preference shifts to the axis of movement. The velocity correlations vary with the angle formed by the pair of neighboring pedestrians and the direction of motion and with the time delay between pedestrians' movements. The delay dependence of the correlations shows characteristic oscillations, produced by the velocity oscillations when striding; however, a filtering of the main frequency of individual striding out reduces the oscillations only partially. We conclude that pedestrians select their path directions so as to evade the necessity of continuously adjusting their speed to their neighbors'. They try to keep a given distance, but follow the person in front of them, as well as accepting and observing pedestrians on their sides. Additionally, we show an empirical example that illustrates the shape of a pedestrian's personal space during movement.

Essays on investments and environment: a spatial econometrics perspective

OpenAIRE

Monteiro, José-Antonio; Grether, Jean-Marie

2011-01-01

This PhD dissertation investigates the links between foreign direct investment (FDI), pollution and environmental policies in an interdependent world. To tackle the issue of spatial dependence, I propose to apply new spatial estimators. The thesis consists of four papers. The first chapter, entitled Spatial Dynamic Panel and System GMM: a Monte-Carlo Investigation, investigates the finite sample properties of estimators for spatial dynamic panel models in the presence of several endogenous va...

Modeling the Spatial Distribution and Fruiting Pattern of a Key Tree Species in a Neotropical Forest: Methodology and Potential Applications

NARCIS (Netherlands)

Caillaud, D.; Crofoot, M.C.; Scarpino, S.V.; Jansen, P.A.; Garzon-Lopez, C.X.; Winkelhagen, A.J.S.; Bohlman, S.A.; Walsh, P.D.

2010-01-01

Background - The movement patterns of wild animals depend crucially on the spatial and temporal availability of resources in their habitat. To date, most attempts to model this relationship were forced to rely on simplified assumptions about the spatiotemporal distribution of food resources. Here we

Modeling the Spatial Distribution and Fruiting Pattern of a Key Tree Species in a Neotropical Forest : Methodology and Potential Applications

NARCIS (Netherlands)

Caillaud, Damien; Crofoot, Margaret C.; Scarpino, Samuel V.; Jansen, Patrick A.; Garzon-Lopez, Carol X.; Winkelhagen, Annemarie J. S.; Bohlman, Stephanie A.; Walsh, Peter D.

2010-01-01

Background: The movement patterns of wild animals depend crucially on the spatial and temporal availability of resources in their habitat. To date, most attempts to model this relationship were forced to rely on simplified assumptions about the spatiotemporal distribution of food resources. Here we

Automating an integrated spatial data-mining model for landfill site selection

Science.gov (United States)

Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Aziz, Hamidi Abdul

2017-10-01

An integrated programming environment represents a robust approach to building a valid model for landfill site selection. One of the main challenges in the integrated model is the complicated processing and modelling due to the programming stages and several limitations. An automation process helps avoid the limitations and improve the interoperability between integrated programming environments. This work targets the automation of a spatial data-mining model for landfill site selection by integrating between spatial programming environment (Python-ArcGIS) and non-spatial environment (MATLAB). The model was constructed using neural networks and is divided into nine stages distributed between Matlab and Python-ArcGIS. A case study was taken from the north part of Peninsular Malaysia. 22 criteria were selected to utilise as input data and to build the training and testing datasets. The outcomes show a high-performance accuracy percentage of 98.2% in the testing dataset using 10-fold cross validation. The automated spatial data mining model provides a solid platform for decision makers to performing landfill site selection and planning operations on a regional scale.

Planar-channeling spatial density under statistical equilibrium

International Nuclear Information System (INIS)

Ellison, J.A.; Picraux, S.T.

1978-01-01

The phase-space density for planar channeled particles has been derived for the continuum model under statistical equilibrium. This is used to obtain the particle spatial probability density as a function of incident angle. The spatial density is shown to depend on only two parameters, a normalized incident angle and a normalized planar spacing. This normalization is used to obtain, by numerical calculation, a set of universal curves for the spatial density and also for the channeled-particle wavelength as a function of amplitude. Using these universal curves, the statistical-equilibrium spatial density and the channeled-particle wavelength can be easily obtained for any case for which the continuum model can be applied. Also, a new one-parameter analytic approximation to the spatial density is developed. This parabolic approximation is shown to give excellent agreement with the exact calculations

Enhanced long-term and impaired short-term spatial memory in GluA1 AMPA receptor subunit knockout mice: evidence for a dual-process memory model.

Science.gov (United States)

Sanderson, David J; Good, Mark A; Skelton, Kathryn; Sprengel, Rolf; Seeburg, Peter H; Rawlins, J Nicholas P; Bannerman, David M

2009-06-01

The GluA1 AMPA receptor subunit is a key mediator of hippocampal synaptic plasticity and is especially important for a rapidly-induced, short-lasting form of potentiation. GluA1 gene deletion impairs hippocampus-dependent, spatial working memory, but spares hippocampus-dependent spatial reference memory. These findings may reflect the necessity of GluA1-dependent synaptic plasticity for short-term memory of recently visited places, but not for the ability to form long-term associations between a particular spatial location and an outcome. This hypothesis is in concordance with the theory that short-term and long-term memory depend on dissociable psychological processes. In this study we tested GluA1-/- mice on both short-term and long-term spatial memory using a simple novelty preference task. Mice were given a series of repeated exposures to a particular spatial location (the arm of a Y-maze) before their preference for a novel spatial location (the unvisited arm of the maze) over the familiar spatial location was assessed. GluA1-/- mice were impaired if the interval between the trials was short (1 min), but showed enhanced spatial memory if the interval between the trials was long (24 h). This enhancement was caused by the interval between the exposure trials rather than the interval prior to the test, thus demonstrating enhanced learning and not simply enhanced performance or expression of memory. This seemingly paradoxical enhancement of hippocampus-dependent spatial learning may be caused by GluA1 gene deletion reducing the detrimental effects of short-term memory on subsequent long-term learning. Thus, these results support a dual-process model of memory in which short-term and long-term memory are separate and sometimes competitive processes.

Spatial modeling of agricultural land use change at global scale

Science.gov (United States)

Meiyappan, P.; Dalton, M.; O'Neill, B. C.; Jain, A. K.

2014-11-01

Long-term modeling of agricultural land use is central in global scale assessments of climate change, food security, biodiversity, and climate adaptation and mitigation policies. We present a global-scale dynamic land use allocation model and show that it can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. The modeling approach integrates economic theory, observed land use history, and data on both socioeconomic and biophysical determinants of land use change, and estimates relationships using long-term historical data, thereby making it suitable for long-term projections. The underlying economic motivation is maximization of expected profits by hypothesized landowners within each grid cell. The model predicts fractional land use for cropland and pastureland within each grid cell based on socioeconomic and biophysical driving factors that change with time. The model explicitly incorporates the following key features: (1) land use competition, (2) spatial heterogeneity in the nature of driving factors across geographic regions, (3) spatial heterogeneity in the relative importance of driving factors and previous land use patterns in determining land use allocation, and (4) spatial and temporal autocorrelation in land use patterns. We show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factors), or those accounting for both land use history and driving factors by mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, we show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. The modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling

Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation

Directory of Open Access Journals (Sweden)

Tatem Andrew J

2012-05-01

Full Text Available Abstract The use of Global Positioning Systems (GPS and Geographical Information Systems (GIS in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models. Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites. In this paper we discuss the deficiencies of existing

Spatial dependence of void coefficient in the University of Arizona TRIGA research reactor

International Nuclear Information System (INIS)

Spriggs, Gregory D.; Doane, Harry; Wells, Robert

1980-01-01

The spatial dependence of the moderator void coefficient of reactivity in the axial direction was experimentally measured in the A-ring using a hollow, air-filled aluminum cylinder. It was found that the void coefficient was positive in the central region of the fuel section reaching a maximum value of approximately + .045 cents/cm 3 and was negative towards the outer edges of the fuel section reaching a maximum of - .09 cents/cm 3 . (author)

Thematic and spatial resolutions affect model-based predictions of tree species distribution.

Science.gov (United States)

Liang, Yu; He, Hong S; Fraser, Jacob S; Wu, ZhiWei

2013-01-01

Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.

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

An alternative to the standard spatial econometric approaches in hedonic house price models

DEFF Research Database (Denmark)

Veie, Kathrine Lausted; Panduro, Toke Emil

Hedonic models are subject to spatially correlated errors which are a symptom of omitted spatial variables, mis-specification or mismeasurement. Methods have been developed to address this problem through the use of spatial econometrics or spatial fixed effects. However, often spatial correlation is...... varying characteristics markedly. This suggests that omitted variable bias may remain an important problem. We advocate for an increased use of sensitivity analysis to determine robustness of estimates to different models of the (omitted) spatial processes....

Localization of Waves in Fractals : Spatial Behavior

NARCIS (Netherlands)

Vries, Pedro de; Raedt, Hans De; Lagendijk, Ad

1989-01-01

Localization of a quantum particle on two-dimensional percolating networks is investigated numerically. Solving the time-dependent Schrödinger equation for particular initial wave packets we study the spatial behavior of eigenstates for two tight-binding models: the quantum percolation model and the

Practical likelihood analysis for spatial generalized linear mixed models

DEFF Research Database (Denmark)

Bonat, W. H.; Ribeiro, Paulo Justiniano

2016-01-01

We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are......, respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...

Using high-resolution soil moisture modelling to assess the uncertainty of microwave remotely sensed soil moisture products at the correct spatial and temporal support

NARCIS (Netherlands)

Wanders, N.; Karssenberg, D.; Bierkens, M. F. P.; Van Dam, J. C.; De Jong, S. M.

Soil moisture is a key variable in the hydrological cycle and important in hydrological modelling. When assimilating soil moisture into flood forecasting models, the improvement of forecasting skills depends on the ability to accurately estimate the spatial and temporal patterns of soil moisture

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

Modeling Space-Time Dependent Helium Bubble Evolution in Tungsten Armor under IFE Conditions

International Nuclear Information System (INIS)

Qiyang Hu; Shahram Sharafat; Nasr Ghoniem

2006-01-01

The High Average Power Laser (HAPL) program is a coordinated effort to develop Laser Inertial Fusion Energy. The implosion of the D-T target produces a spectrum of neutrons, X-rays, and charged particles, which arrive at the first wall (FW) at different times within about 2.5 μs at a frequency of 5 to 10 Hz. Helium is one of several high-energy charged particle constituents impinging on the candidate tungsten armored low activation ferritic steel First Wall. The spread of the implanted debris and burn helium energies results in a unique space-time dependent implantation profile that spans about 10 μm in tungsten. Co-implantation of X-rays and other ions results in spatially dependent damage profiles and rapid space-time dependent temperature spikes and gradients. The rate of helium transport and helium bubble formation will vary significantly throughout the implanted region. Furthermore, helium will also be transported via the migration of helium bubbles and non-equilibrium helium-vacancy clusters. The HEROS code was developed at UCLA to model the spatial and time-dependent helium bubble nucleation, growth, coalescence, and migration under transient damage rates and transient temperature gradients. The HEROS code is based on kinetic rate theory, which includes clustering of helium and vacancies, helium mobility, helium-vacancy cluster stability, cavity nucleation and growth and other microstructural features such as interstitial loop evolution, grain boundaries, and precipitates. The HEROS code is based on space-time discretization of reaction-diffusion type equations to account for migration of mobile species between neighboring bins as single atoms, clusters, or bubbles. HAPL chamber FW implantation conditions are used to model helium bubble evolution in the implanted tungsten. Helium recycling rate predictions are compared with experimental results of helium ion implantation experiments. (author)

On Angular Sampling Methods for 3-D Spatial Channel Models

DEFF Research Database (Denmark)

Fan, Wei; Jämsä, Tommi; Nielsen, Jesper Ødum

2015-01-01

This paper discusses generating three dimensional (3D) spatial channel models with emphasis on the angular sampling methods. Three angular sampling methods, i.e. modified uniform power sampling, modified uniform angular sampling, and random pairing methods are proposed and investigated in detail....... The random pairing method, which uses only twenty sinusoids in the ray-based model for generating the channels, presents good results if the spatial channel cluster is with a small elevation angle spread. For spatial clusters with large elevation angle spreads, however, the random pairing method would fail...... and the other two methods should be considered....

Spatial connections in regional climate model rainfall outputs at different temporal scales: Application of network theory

Science.gov (United States)

Naufan, Ihsan; Sivakumar, Bellie; Woldemeskel, Fitsum M.; Raghavan, Srivatsan V.; Vu, Minh Tue; Liong, Shie-Yui

2018-01-01

Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961-1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network's local density) and degree distribution (a measure of the network's spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are

Spatially dependent burnup implementation into the nodal program based on the finite element response matrix method

International Nuclear Information System (INIS)

Yoriyaz, H.

1986-01-01

In this work a spatial burnup scheme and feedback effects has been implemented into the FERM ( 'Finite Element Response Matrix' )program. The spatially dependent neutronic parameters have been considered in three levels: zonewise calculation, assembly wise calculation and pointwise calculation. Flux and power distributions and the multiplication factor were calculated and compared with the results obtained by CITATIOn program. These comparisons showed that processing time in the Ferm code has been hundred of times shorter and no significant difference has been observed in the assembly average power distribution. (Author) [pt

Towards models of strategic spatial choice behaviour: theory and application issues

NARCIS (Netherlands)

Han, Q.; Timmermans, H.J.P.

2005-01-01

Models of spatial choice behaviour have been around in urban planning for decades to assess the feasibility of planning actions or to predict external (competition) effects on existing destinations. The well known spatial interaction models of the 1970s have gradually been replaced by discrete

Development of a Discrete Spatial-Temporal SEIR Simulator for Modeling Infectious Diseases

Energy Technology Data Exchange (ETDEWEB)

McKenna, S.A.

2000-11-01

Multiple techniques have been developed to model the temporal evolution of infectious diseases. Some of these techniques have also been adapted to model the spatial evolution of the disease. This report examines the application of one such technique, the SEIR model, to the spatial and temporal evolution of disease. Applications of the SEIR model are reviewed briefly and an adaptation to the traditional SEIR model is presented. This adaptation allows for modeling the spatial evolution of the disease stages at the individual level. The transmission of the disease between individuals is modeled explicitly through the use of exposure likelihood functions rather than the global transmission rate applied to populations in the traditional implementation of the SEIR model. These adaptations allow for the consideration of spatially variable (heterogeneous) susceptibility and immunity within the population. The adaptations also allow for modeling both contagious and non-contagious diseases. The results of a number of numerical experiments to explore the effect of model parameters on the spread of an example disease are presented.

Spatial scale effects in environmental risk-factor modelling for diseases

Directory of Open Access Journals (Sweden)

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.

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.

On the influence of temporal and spatial resolution of aircraft emission inventories for mesoscale modeling of pollutant dispersion

Energy Technology Data Exchange (ETDEWEB)

Franzkowiak, V.; Petry, H.; Ebel, A. [Cologne Univ. (Germany). Inst. for Geophysics and Meteorology

1997-12-31

The sensitivity of a mesoscale chemistry transport model to the temporal and spatial resolution of aircraft emission inventories is evaluated. A statistical analysis of air traffic in the North-Atlantic flight corridor is carried out showing a highly variable, fine structured spatial distribution and a pronounced daily variation. Sensitivity studies comparing different emission scenarios reveal a strong dependency to the emission time and location of both transport and response in chemical formation of subsequent products. The introduction of a pronounced daily variation leads to a 30% higher ozone production in comparison to uniformly distributed emissions. (author) 9 refs.

On the influence of temporal and spatial resolution of aircraft emission inventories for mesoscale modeling of pollutant dispersion

Energy Technology Data Exchange (ETDEWEB)

Franzkowiak, V; Petry, H; Ebel, A [Cologne Univ. (Germany). Inst. for Geophysics and Meteorology

1998-12-31

The sensitivity of a mesoscale chemistry transport model to the temporal and spatial resolution of aircraft emission inventories is evaluated. A statistical analysis of air traffic in the North-Atlantic flight corridor is carried out showing a highly variable, fine structured spatial distribution and a pronounced daily variation. Sensitivity studies comparing different emission scenarios reveal a strong dependency to the emission time and location of both transport and response in chemical formation of subsequent products. The introduction of a pronounced daily variation leads to a 30% higher ozone production in comparison to uniformly distributed emissions. (author) 9 refs.

Analysing earthquake slip models with the spatial prediction comparison test

KAUST Repository

Zhang, L.; Mai, Paul Martin; Thingbaijam, Kiran Kumar; Razafindrakoto, H. N. T.; Genton, Marc G.

2014-01-01

Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.

Analysing earthquake slip models with the spatial prediction comparison test

KAUST Repository

Zhang, L.

2014-11-10

Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.

Calibration of a distributed hydrologic model using observed spatial patterns from MODIS data

Science.gov (United States)

Demirel, Mehmet C.; González, Gorka M.; Mai, Juliane; Stisen, Simon

2016-04-01

Distributed hydrologic models are typically calibrated against streamflow observations at the outlet of the basin. Along with these observations from gauging stations, satellite based estimates offer independent evaluation data such as remotely sensed actual evapotranspiration (aET) and land surface temperature. The primary objective of the study is to compare model calibrations against traditional downstream discharge measurements with calibrations against simulated spatial patterns and combinations of both types of observations. While the discharge based model calibration typically improves the temporal dynamics of the model, it seems to give rise to minimum improvement of the simulated spatial patterns. In contrast, objective functions specifically targeting the spatial pattern performance could potentially increase the spatial model performance. However, most modeling studies, including the model formulations and parameterization, are not designed to actually change the simulated spatial pattern during calibration. This study investigates the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale hydrologic model (mHM). This model is selected as it allows for a change in the spatial distribution of key soil parameters through the optimization of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) values directly as input. In addition the simulated aET can be estimated at a spatial resolution suitable for comparison to the spatial patterns observed with MODIS data. To increase our control on spatial calibration we introduced three additional parameters to the model. These new parameters are part of an empirical equation to the calculate crop coefficient (Kc) from daily LAI maps and used to update potential evapotranspiration (PET) as model inputs. This is done instead of correcting/updating PET with just a uniform (or aspect driven) factor used in the mHM model

Research on the decision-making model of land-use spatial optimization

Science.gov (United States)

He, Jianhua; Yu, Yan; Liu, Yanfang; Liang, Fei; Cai, Yuqiu

2009-10-01

Using the optimization result of landscape pattern and land use structure optimization as constraints of CA simulation results, a decision-making model of land use spatial optimization is established coupled the landscape pattern model with cellular automata to realize the land use quantitative and spatial optimization simultaneously. And Huangpi district is taken as a case study to verify the rationality of the model.

The dynamic and indirect spatial effects of neighborhood conditions on land value, spatial panel dynamic econometrics model

Science.gov (United States)

Fitriani, Rahma; Sumarminingsih, Eni; Astutik, Suci

2017-05-01

Land value is the product of past decision of its use leading to its value, as well as the value of the surrounded land. It is also affected by the local characteristic and the spillover development demand of the previous time period. The effect of each factor on land value will have dynamic and spatial virtues. Thus, a spatial panel dynamic model is used to estimate the particular effects. The model will be useful for predicting the future land value or the effect of implemented policy on land value. The objective of this paper is to derive the dynamic and indirect spatial marginal effects of the land characteristic and the spillover development demand on land value. Each effect is the partial derivative of the expected land value based on the spatial dynamic model with respect to each variable, by considering different time period and different location. The results indicate that the instant change of local or neighborhood characteristics on land value affect the local and the immediate neighborhood land value. However, the longer the change take place, the effect will spread further, not only on the immediate neighborhood.

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...

Probing interaction and spatial curvature in the holographic dark energy model

International Nuclear Information System (INIS)

Li, Miao; Li, Xiao-Dong; Wang, Shuang; Wang, Yi; Zhang, Xin

2009-01-01

In this paper we place observational constraints on the interaction and spatial curvature in the holographic dark energy model. We consider three kinds of phenomenological interactions between holographic dark energy and matter, i.e., the interaction term Q is proportional to the energy densities of dark energy (ρ Λ ), matter (ρ m ), and matter plus dark energy (ρ m +ρ Λ ). For probing the interaction and spatial curvature in the holographic dark energy model, we use the latest observational data including the type Ia supernovae (SNIa) Constitution data, the shift parameter of the cosmic microwave background (CMB) given by the five-year Wilkinson Microwave Anisotropy Probe (WMAP5) observations, and the baryon acoustic oscillation (BAO) measurement from the Sloan Digital Sky Survey (SDSS). Our results show that the interaction and spatial curvature in the holographic dark energy model are both rather small. Besides, it is interesting to find that there exists significant degeneracy between the phenomenological interaction and the spatial curvature in the holographic dark energy model

Simulation of time-dependent Heisenberg models in one dimension

DEFF Research Database (Denmark)

Volosniev, A. G.; Hammer, H. -W.; Zinner, N. T.

2016-01-01

In this Letter, we provide a theoretical analysis of strongly interacting quantum systems confined by a time-dependent external potential in one spatial dimension. We show that such systems can be used to simulate spin chains described by Heisenberg Hamiltonians in which the exchange coupling...... constants can be manipulated by time-dependent driving of the shape of the external confinement. As illustrative examples, we consider a harmonic trapping potential with a variable frequency and an infinite square well potential with a time-dependent barrier in the middle....

Context-dependent spatially periodic activity in the human entorhinal cortex.

Science.gov (United States)

Nadasdy, Zoltan; Nguyen, T Peter; Török, Ágoston; Shen, Jason Y; Briggs, Deborah E; Modur, Pradeep N; Buchanan, Robert J

2017-04-25

The spatially periodic activity of grid cells in the entorhinal cortex (EC) of the rodent, primate, and human provides a coordinate system that, together with the hippocampus, informs an individual of its location relative to the environment and encodes the memory of that location. Among the most defining features of grid-cell activity are the 60° rotational symmetry of grids and preservation of grid scale across environments. Grid cells, however, do display a limited degree of adaptation to environments. It remains unclear if this level of environment invariance generalizes to human grid-cell analogs, where the relative contribution of visual input to the multimodal sensory input of the EC is significantly larger than in rodents. Patients diagnosed with nontractable epilepsy who were implanted with entorhinal cortical electrodes performing virtual navigation tasks to memorized locations enabled us to investigate associations between grid-like patterns and environment. Here, we report that the activity of human entorhinal cortical neurons exhibits adaptive scaling in grid period, grid orientation, and rotational symmetry in close association with changes in environment size, shape, and visual cues, suggesting scale invariance of the frequency, rather than the wavelength, of spatially periodic activity. Our results demonstrate that neurons in the human EC represent space with an enhanced flexibility relative to neurons in rodents because they are endowed with adaptive scalability and context dependency.

Multilevel discretized random field models with 'spin' correlations for the simulation of environmental spatial data

Science.gov (United States)

Žukovič, Milan; Hristopulos, Dionissios T.

2009-02-01

A current problem of practical significance is how to analyze large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between 'spins'. The spins represent multilevel discretizations of environmental variables with respect to a number of pre-defined thresholds. The spatial dependence between the 'spins' is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulations of spatially distributed variables from samples with missing data. Currently, the sampling and simulation points are assumed to be at the nodes of a regular grid. The conditional simulations of the 'spin system' are forced to respect locally the sample values and the system statistics globally. The second constraint is enforced by minimizing a cost function representing the deviation between normalized correlation energies of the simulated and the sample distributions. In the approach based on the Nc-state Potts model, each point is assigned to one of Nc classes. The interactions involve all the points simultaneously. In the Ising model approach, a sequential simulation scheme is used: the discretization at each simulation level is binomial (i.e., ± 1). Information propagates from lower to higher levels as the simulation proceeds. We compare the two approaches in terms of their ability to reproduce the target statistics (e.g., the histogram and the variogram of the sample distribution), to predict data at unsampled locations, as well as in terms of their computational complexity. The comparison is based on a non-Gaussian data set (derived from a digital elevation model of the Walker Lake area, Nevada, USA). We discuss the impact of relevant simulation parameters, such as the domain size, the number of

Lessons learned for spatial modelling of ecosystem services in support of ecosystem accounting

NARCIS (Netherlands)

Schroter, M.; Remme, R.P.; Sumarga, E.; Barton, D.N.; Hein, L.G.

2015-01-01

Assessment of ecosystem services through spatial modelling plays a key role in ecosystem accounting. Spatial models for ecosystem services try to capture spatial heterogeneity with high accuracy. This endeavour, however, faces several practical constraints. In this article we analyse the trade-offs

Spatial equity analysis on expressway network development in Japan: Empirical approach using the spatial computable general equilibrium model RAEM-light

NARCIS (Netherlands)

Koike, A.; Tavasszy, L.; Sato, K.

2009-01-01

The authors apply the RAEM-Light model to analyze the distribution of social benefits from expressway network projects from the viewpoint of spatial equity. The RAEM-Light model has some innovative features. The spatial behavior of producers and consumers is explicitly described and is endogenously

Spatial transferability of habitat suitability models of Nephrops norvegicus among fished areas in the Northeast Atlantic: sufficiently stable for marine resource conservation?

Directory of Open Access Journals (Sweden)

Valentina Lauria

Full Text Available Knowledge of the spatial distribution and habitat associations of species in relation to the environment is essential for their management and conservation. Habitat suitability models are useful in quantifying species-environment relationships and predicting species distribution patterns. Little is known, however, about the stability and performance of habitat suitability models when projected into new areas (spatial transferability and how this can inform resource management. The aims of this study were to model habitat suitability of Norway lobster (Nephrops norvegicus in five fished areas of the Northeast Atlantic (Aran ground, Irish Sea, Celtic Sea, Scotland Inshore and Fladen ground, and to test for spatial transferability of habitat models among multiple regions. Nephrops burrow density was modelled using generalised additive models (GAMs with predictors selected from four environmental variables (depth, slope, sediment and rugosity. Models were evaluated and tested for spatial transferability among areas. The optimum models (lowest AICc for different areas always included depth and sediment as predictors. Burrow densities were generally greater at depth and in finer sediments, but relationships for individual areas were sometimes more complex. Aside from an inclusion of depth and sediment, the optimum models differed between fished areas. When it came to tests of spatial transferability, however, most of the models were able to predict Nephrops density in other areas. Furthermore, transferability was not dependent on use of the optimum models since competing models were also able to achieve a similar level of transferability to new areas. A degree of decoupling between model 'fitting' performance and spatial transferability supports the use of simpler models when extrapolating habitat suitability maps to different areas. Differences in the form and performance of models from different areas may supply further information on the processes

Tukey max-stable processes for spatial extremes

KAUST Repository

Xu, Ganggang

2016-09-21

We propose a new type of max-stable process that we call the Tukey max-stable process for spatial extremes. It brings additional flexibility to modeling dependence structures among spatial extremes. The statistical properties of the Tukey max-stable process are demonstrated theoretically and numerically. Simulation studies and an application to Swiss rainfall data indicate the effectiveness of the proposed process. © 2016 Elsevier B.V.

The Dependent Poisson Race Model and Modeling Dependence in Conjoint Choice Experiments

Science.gov (United States)

Ruan, Shiling; MacEachern, Steven N.; Otter, Thomas; Dean, Angela M.

2008-01-01

Conjoint choice experiments are used widely in marketing to study consumer preferences amongst alternative products. We develop a class of choice models, belonging to the class of Poisson race models, that describe a "random utility" which lends itself to a process-based description of choice. The models incorporate a dependence structure which…

Using the gravity model to estimate the spatial spread of vector-borne diseases

NARCIS (Netherlands)

Barrios, J.M.; Verstraeten, W.W.; Maes, P.; Aerts, J.; Farifteh, J.; Coppin, P.

2012-01-01

The gravity models are commonly used spatial interaction models. They have been widely applied in a large set of domains dealing with interactions amongst spatial entities. The spread of vector-borne diseases is also related to the intensity of interaction between spatial entities, namely, the

Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model

NARCIS (Netherlands)

J.G. Velazco (Julio G.); M.X. Rodríguez-Álvarez (María Xosé); M.P. Boer (Martin); D.R. Jordan (David R.); P.H.C. Eilers (Paul); M. Malosetti (Marcos); F. van Eeuwijk (Fred)

2017-01-01

markdownabstract_Key message: A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials._ __Abstract:__ Adjustment for spatial trends in plant breeding field trials is essential for

Entrainment and Control of Bacterial Populations: An in Silico Study over a Spatially Extended Agent Based Model.

Science.gov (United States)

Mina, Petros; Tsaneva-Atanasova, Krasimira; Bernardo, Mario di

2016-07-15

We extend a spatially explicit agent based model (ABM) developed previously to investigate entrainment and control of the emergent behavior of a population of synchronized oscillating cells in a microfluidic chamber. Unlike most of the work in models of control of cellular systems which focus on temporal changes, we model individual cells with spatial dependencies which may contribute to certain behavioral responses. We use the model to investigate the response of both open loop and closed loop strategies, such as proportional control (P-control), proportional-integral control (PI-control) and proportional-integral-derivative control (PID-control), to heterogeinities and growth in the cell population, variations of the control parameters and spatial effects such as diffusion in the spatially explicit setting of a microfluidic chamber setup. We show that, as expected from the theory of phase locking in dynamical systems, open loop control can only entrain the cell population in a subset of forcing periods, with a wide variety of dynamical behaviors obtained outside these regions of entrainment. Closed-loop control is shown instead to guarantee entrainment in a much wider region of control parameter space although presenting limitations when the population size increases over a certain threshold. In silico tracking experiments are also performed to validate the ability of classical control approaches to achieve other reference behaviors such as a desired constant output or a linearly varying one. All simulations are carried out in BSim, an advanced agent-based simulator of microbial population which is here extended ad hoc to include the effects of control strategies acting onto the population.

Direct visualization of electroporation-assisted in vivo gene delivery to tumors using intravital microscopy – spatial and time dependent distribution

Directory of Open Access Journals (Sweden)

Dachs Gabi U

2004-11-01

Full Text Available Abstract Background Electroporation is currently receiving much attention as a way to increase drug and DNA delivery. Recent studies demonstrated the feasibility of electrogene therapy using a range of therapeutic genes for the treatment of experimental tumors. However, the transfection efficiency of electroporation-assisted DNA delivery is still low compared to viral methods and there is a clear need to optimize this approach. In order to optimize treatment, knowledge about spatial and time dependency of gene expression following delivery is of utmost importance in order to improve gene delivery. Intravital microscopy of tumors growing in dorsal skin fold window chambers is a useful method for monitoring gene transfection, since it allows non-invasive dynamic monitoring of gene expression in tumors in a live animal. Methods Intravital microscopy was used to monitor real time spatial distribution of the green fluorescent protein (GFP and time dependence of transfection efficiency in syngeneic P22 rat tumor model. DNA alone, liposome-DNA complexes and electroporation-assisted DNA delivery using two different sets of electric pulse parameters were compared. Results Electroporation-assisted DNA delivery using 8 pulses, 600 V/cm, 5 ms, 1 Hz was superior to other methods and resulted in 22% increase in fluorescence intensity in the tumors up to 6 days post-transfection, compared to the non-transfected area in granulation tissue. Functional GFP was detected within 5 h after transfection. Cells expressing GFP were detected throughout the tumor, but not in the surrounding tissue that was not exposed to electric pulses. Conclusions Intravital microscopy was demonstrated to be a suitable method for monitoring time and spatial distribution of gene expression in experimental tumors and provided evidence that electroporation-assisted gene delivery using 8 pulses, 600 V/cm, 5 ms, 1 Hz is an effective method, resulting in early onset and homogenous

Toward micro-scale spatial modeling of gentrification

Science.gov (United States)

O'Sullivan, David

A simple preliminary model of gentrification is presented. The model is based on an irregular cellular automaton architecture drawing on the concept of proximal space, which is well suited to the spatial externalities present in housing markets at the local scale. The rent gap hypothesis on which the model's cell transition rules are based is discussed. The model's transition rules are described in detail. Practical difficulties in configuring and initializing the model are described and its typical behavior reported. Prospects for further development of the model are discussed. The current model structure, while inadequate, is well suited to further elaboration and the incorporation of other interesting and relevant effects.

Recognition for old Arabic manuscripts using spatial gray level dependence (SGLD

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Ahmad M. Abd Al-Aziz

2011-03-01

Full Text Available Texture analysis forms the basis of object recognition and classification in several domains, one of these domains is historical document manuscripts because the manuscripts hold our culture heritage and also large numbers of undated manuscripts exist. This paper presents results for historical document classification of old Arabic manuscripts using texture analysis and a segmentation free approach. The main objective is to discriminate between historical documents of different writing styles to three different ages: Contemporary (Modern Age, Ottoman Age and Mamluk Age. This classification depends on a Spatial Gray-level Dependence (SGLD technique which provides eight distinct texture features for each sample document. We applied Stepwise Discriminant Analysis and Multiple discriminant analysis methods to decrease the dimensionality of features and extract training vector features from samples. To classify historical documents into three main historical age classes the decision tree classification is applied. The system has been tested on 48 Arabic historical manuscripts documents from the Dar Al-Kotob Al-Masria Library. Our results so far yield 95.83% correct classification for the historical Arabic documents.

Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model

NARCIS (Netherlands)

Velazco, Julio G.; Rodríguez-Álvarez, María Xosé; Boer, Martin P.; Jordan, David R.; Eilers, Paul H.C.; Malosetti, Marcos; Eeuwijk, van Fred A.

2017-01-01

Key message: A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials. Abstract: Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and

A physically based analytical spatial air temperature and humidity model

Science.gov (United States)

Yang, Yang; Endreny, Theodore A.; Nowak, David J.

2013-09-01

Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat storage based on semiempirical functions and generates spatially distributed estimates based on inputs of topography, land cover, and the weather data measured at a reference site. The model assumes that for all grids under the same mesoscale climate, grid air temperature and humidity are modified by local variation in absorbed solar radiation and the partitioning of sensible and latent heat. The model uses a reference grid site for time series meteorological data and the air temperature and humidity of any other grid can be obtained by solving the heat flux network equations. PASATH was coupled with the USDA iTree-Hydro water balance model to obtain evapotranspiration terms and run from 20 to 29 August 2010 at a 360 m by 360 m grid scale and hourly time step across a 285 km2 watershed including the urban area of Syracuse, NY. PASATH predictions were tested at nine urban weather stations representing variability in urban topography and land cover. The PASATH model predictive efficiency R2 ranged from 0.81 to 0.99 for air temperature and 0.77 to 0.97 for dew point temperature. PASATH is expected to have broad applications on environmental and ecological models.

Cyber Epidemic Models with Dependences

OpenAIRE

Xu, Maochao; Da, Gaofeng; Xu, Shouhuai

2016-01-01

Studying models of cyber epidemics over arbitrary complex networks can deepen our understanding of cyber security from a whole-system perspective. In this paper, we initiate the investigation of cyber epidemic models that accommodate the {\\em dependences} between the cyber attack events. Due to the notorious difficulty in dealing with such dependences, essentially all existing cyber epidemic models have assumed them away. Specifically, we introduce the idea of Copulas into cyber epidemic mode...

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

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Sungwon Kim

2015-06-01

Full Text Available The objective of this study is to develop artificial neural network (ANN models, including multilayer perceptron (MLP and Kohonen self-organizing feature map (KSOFM, for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop and 11-3-1 (Levenberg-Marquardt were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop and 1-3-11 (Levenberg–Marquardt, which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

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.

Spatial distribution of emissions to air - the SPREAD model

Energy Technology Data Exchange (ETDEWEB)

Plejdrup, M S; Gyldenkaerne, S

2011-04-15

The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark's obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long-range transboundary air pollution, CLRTAP. NERI has developed a model to distribute emissions from the national emission inventories on a 1x1 km grid covering the Danish land and sea territory. The new spatial high resolution distribution model for emissions to air (SPREAD) has been developed according to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously, a distribution on the 17x17 km EMEP grid has been set up and used in research projects combined with detailed distributions for a few sectors or sub-sectors e.g. a distribution for emissions from road traffic on 1x1 km resolution. SPREAD is developed to generate improved spatial emission data for e.g. air quality modelling in exposure studies. SPREAD includes emission distributions for each sector in the Danish inventory system; stationary combustion, mobile sources, fugitive emissions from fuels, industrial processes, solvents and other product use, agriculture and waste. This model enables generation of distributions for single sectors and for a number of sub-sectors and single sources as well. This report documents the methodologies in this first version of SPREAD and presents selected results. Further, a number of potential improvements for later versions of SPREAD are addressed and discussed. (Author)