A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates

OpenAIRE

Lindström, Johan; Szpiro, Adam A; Sampson, Paul D; Oron, Assaf P; Richards, Mark; Larson, Tim V; Sheppard, Lianne

2013-01-01

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implem...

Spatiotemporal dynamics of large-scale brain activity

Science.gov (United States)

Neuman, Jeremy

Understanding the dynamics of large-scale brain activity is a tough challenge. One reason for this is the presence of an incredible amount of complexity arising from having roughly 100 billion neurons connected via 100 trillion synapses. Because of the extremely high number of degrees of freedom in the nervous system, the question of how the brain manages to properly function and remain stable, yet also be adaptable, must be posed. Neuroscientists have identified many ways the nervous system makes this possible, of which synaptic plasticity is possibly the most notable one. On the other hand, it is vital to understand how the nervous system also loses stability, resulting in neuropathological diseases such as epilepsy, a disease which affects 1% of the population. In the following work, we seek to answer some of these questions from two different perspectives. The first uses mean-field theory applied to neuronal populations, where the variables of interest are the percentages of active excitatory and inhibitory neurons in a network, to consider how the nervous system responds to external stimuli, self-organizes and generates epileptiform activity. The second method uses statistical field theory, in the framework of single neurons on a lattice, to study the concept of criticality, an idea borrowed from physics which posits that in some regime the brain operates in a collectively stable or marginally stable manner. This will be examined in two different neuronal networks with self-organized criticality serving as the overarching theme for the union of both perspectives. One of the biggest problems in neuroscience is the question of to what extent certain details are significant to the functioning of the brain. These details give rise to various spatiotemporal properties that at the smallest of scales explain the interaction of single neurons and synapses and at the largest of scales describe, for example, behaviors and sensations. In what follows, we will shed some

On generalized scaling laws with continuously varying exponents

International Nuclear Information System (INIS)

Sittler, Lionel; Hinrichsen, Haye

2002-01-01

Many physical systems share the property of scale invariance. Most of them show ordinary power-law scaling, where quantities can be expressed as a leading power law times a scaling function which depends on scaling-invariant ratios of the parameters. However, some systems do not obey power-law scaling, instead there is numerical evidence for a logarithmic scaling form, in which the scaling function depends on ratios of the logarithms of the parameters. Based on previous ideas by Tang we propose that this type of logarithmic scaling can be explained by a concept of local scaling invariance with continuously varying exponents. The functional dependence of the exponents is constrained by a homomorphism which can be expressed as a set of partial differential equations. Solving these equations we obtain logarithmic scaling as a special case. The other solutions lead to scaling forms where logarithmic and power-law scaling are mixed

Evaluation of white spot syndrome virus variable DNA loci as molecular markers of virus spread at intermediate spatiotemporal scales

NARCIS (Netherlands)

Bui Thi Minh Dieu,; Marks, H.; Zwart, M.P.; Vlak, J.M.

2010-01-01

Variable genomic loci have been employed in a number of molecular epidemiology studies of white spot syndrome virus (WSSV), but it is unknown which loci are suitable molecular markers for determining WSSV spread on different spatiotemporal scales. Although previous work suggests that multiple

Clinimetric properties of the Tinetti Mobility Test, Four Square Step Test, Activities-specific Balance Confidence Scale, and spatiotemporal gait measures in individuals with Huntington's disease.

Science.gov (United States)

Kloos, Anne D; Fritz, Nora E; Kostyk, Sandra K; Young, Gregory S; Kegelmeyer, Deb A

2014-09-01

Individuals with Huntington's disease (HD) experience balance and gait problems that lead to falls. Clinicians currently have very little information about the reliability and validity of outcome measures to determine the efficacy of interventions that aim to reduce balance and gait impairments in HD. This study examined the reliability and concurrent validity of spatiotemporal gait measures, the Tinetti Mobility Test (TMT), Four Square Step Test (FSST), and Activities-specific Balance Confidence (ABC) Scale in individuals with HD. Participants with HD [n = 20; mean age ± SD=50.9 ± 13.7; 7 male] were tested on spatiotemporal gait measures and the TMT, FSST, and ABC Scale before and after a six week period to determine test-retest reliability and minimal detectable change (MDC) values. Linear relationships between gait and clinical measures were estimated using Pearson's correlation coefficients. Spatiotemporal gait measures, the TMT total and the FSST showed good to excellent test-retest reliability (ICC > 0.75). MDC values were 0.30 m/s and 0.17 m/s for velocity in forward and backward walking respectively, four points for the TMT, and 3s for the FSST. The TMT and FSST were highly correlated with most spatiotemporal measures. The ABC Scale demonstrated lower reliability and less concurrent validity than other measures. The high test-retest reliability over a six week period and concurrent validity between the TMT, FSST, and spatiotemporal gait measures suggest that the TMT and FSST may be useful outcome measures for future intervention studies in ambulatory individuals with HD. Copyright © 2014 Elsevier B.V. All rights reserved.

Large scale stochastic spatio-temporal modelling with PCRaster

NARCIS (Netherlands)

Karssenberg, D.J.; Drost, N.; Schmitz, O.; Jong, K. de; Bierkens, M.F.P.

2013-01-01

PCRaster is a software framework for building spatio-temporal models of land surface processes (http://www.pcraster.eu). Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations are available to model

Photogrammetric techniques for across-scale soil erosion assessment

OpenAIRE

Eltner, Anette

2016-01-01

Soil erosion is a complex geomorphological process with varying influences of different impacts at different spatio-temporal scales. To date, measurement of soil erosion is predominantly realisable at specific scales, thereby detecting separate processes, e.g. interrill erosion contrary to rill erosion. It is difficult to survey soil surface changes at larger areal coverage such as field scale with high spatial resolution. Either net changes at the system outlet or remaining traces after the ...

Spatio-temporal variability of soil water content on the local scale in a Mediterranean mountain area (Vallcebre, North Eastern Spain). How different spatio-temporal scales reflect mean soil water content

Science.gov (United States)

Molina, Antonio J.; Latron, Jérôme; Rubio, Carles M.; Gallart, Francesc; Llorens, Pilar

2014-08-01

As a result of complex human-land interactions and topographic variability, many Mediterranean mountain catchments are covered by agricultural terraces that have locally modified the soil water content dynamic. Understanding these local-scale dynamics helps us grasp better how hydrology behaves on the catchment scale. Thus, this study examined soil water content variability in the upper 30 cm of the soil on a Mediterranean abandoned terrace in north-east Spain. Using a dataset of high spatial (regular grid of 128 automatic TDR probes at 2.5 m intervals) and temporal (20-min time step) resolution, gathered throughout a 84-day period, the spatio-temporal variability of soil water content at the local scale and the way that different spatio-temporal scales reflect the mean soil water content were investigated. Soil water content spatial variability and its relation to wetness conditions were examined, along with the spatial structuring of the soil water content within the terrace. Then, the ability of single probes and of different combinations of spatial measurements (transects and grids) to provide a good estimate of mean soil water content on the terrace scale was explored by means of temporal stability analyses. Finally, the effect of monitoring frequency on the magnitude of detectable daily soil water content variations was studied. Results showed that soil water content spatial variability followed a bimodal pattern of increasing absolute variability with increasing soil water content. In addition, a linear trend of decreasing soil water content as the distance from the inner part of the terrace increased was identified. Once this trend was subtracted, resulting semi-variograms suggested that the spatial resolution examined was too high to appreciate spatial structuring in the data. Thus, the spatial pattern should be considered as random. Of all the spatial designs tested, the 10 × 10 m mesh grid (9 probes) was considered the most suitable option for a good

Spatiotemporal bioeconomic performance of artificial shelters in a small-scale, rights-based managed Caribbean spiny lobster (Panulirus argus fishery

Directory of Open Access Journals (Sweden)

Maren Headley

2017-03-01

Full Text Available This study presents a bioeconomic analysis of artificial shelter performance in a fishery targeting a spiny lobster meta-population, with spatially allocated, individual exclusive benthic property rights for shelter introduction and harvest of species. Insights into fishers’ short-run decisions and fishing strategies are also provided. Spatiotemporal bioeconomic performance of shelters located in ten fishing areas during four seasons was compared using two-way ANOVAs and Pearson correlations. Results show that there was spatiotemporal heterogeneity in bioeconomic variables among fishing areas, with mean catch per unit effort (CPUE, kg shelter–1 ranging from 0.42 kg to 1.3 kg per trip, mean quasi-profits of variable costs per shelter harvested ranging from USD6.00 to USD19.57 per trip, and mean quasi-profits of variable costs ranging from USD338 to USD1069 per trip. Positive moderate correlations between shelter density and CPUE (kg shelter–1 km–2 were found. Bioeconomic performance of the shelters was influenced by spatiotemporal resource abundance and distribution, fishing area location in relation to the port, shelter density, heterogeneous fishing strategies and the management system. The results provide empirical information on the spatiotemporal performance of shelters and fishing strategies and can contribute to management at the local-scale of a meta-population distributed throughout the Caribbean Sea and Gulf of Mexico.

Spatio-temporal networks: reachability, centrality and robustness.

Science.gov (United States)

Williams, Matthew J; Musolesi, Mirco

2016-06-01

Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks.

A Tentative Application Of Morphological Filters To Time-Varying Images

Science.gov (United States)

Billard, D.; Poquillon, B.

1989-03-01

In this paper, morphological filters, which are commonly used to process either 2D or multidimensional static images, are generalized to the analysis of time-varying image sequence. The introduction of the time dimension induces then interesting prop-erties when designing such spatio-temporal morphological filters. In particular, the specification of spatio-temporal structuring ele-ments (equivalent to time-varying spatial structuring elements) can be adjusted according to the temporal variations of the image sequences to be processed : this allows to derive specific morphological transforms to perform noise filtering or moving objects discrimination on dynamic images viewed by a non-stationary sensor. First, a brief introduction to the basic principles underlying morphological filters will be given. Then, a straightforward gener-alization of these principles to time-varying images will be pro-posed. This will lead us to define spatio-temporal opening and closing and to introduce some of their possible applications to process dynamic images. At last, preliminary results obtained us-ing a natural forward looking infrared (FUR) image sequence are presented.

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China.

Science.gov (United States)

Cao, Chunxiang; Chen, Wei; Zheng, Sheng; Zhao, Jian; Wang, Jinfeng; Cao, Wuchun

2016-01-01

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method

International Nuclear Information System (INIS)

Nasser, Hassan; Cessac, Bruno; Marre, Olivier

2013-01-01

Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In the first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of these studies have focused on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In the second part, we present a new method based on Monte Carlo sampling which is suited for the fitting of large-scale spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatio-temporal models to large neural ensembles. (paper)

Transition to turbulence via spatiotemporal intermittency in stimulated Raman backscattering

International Nuclear Information System (INIS)

Skoric, M.M.; Jovanovic, M.S.; Rajkovic, M.R.

1996-01-01

The spatiotemporal evolution of stimulated Raman backscattering in a bounded, uniform, weakly dissipative plasma is studied. The nonlinear model of a three-wave interaction involves a quadratic coupling of slowly varying complex amplitudes of the laser pump, the backscattered and the electron plasma wave. The corresponding set of coupled partial differential equations with nonlinear phase detuning that is taken into account is solved numerically in space time with fixed nonzero source boundary conditions. The study of the above open, convective, weakly confined system reveals a quasiperiodic transition to spatiotemporal chaos via spatiotemporal intermittency. In the analysis of transitions a dual scheme borrowed from fields of nonlinear dynamics and statistical physics is applied. An introduction of a nonlinear three-wave interaction to a growing family of paradigmatic equations which exhibit a route to turbulence via spatiotemporal intermittency is outlined in this work. copyright 1996 The American Physical Society

Multiscale recurrence analysis of spatio-temporal data

Science.gov (United States)

Riedl, M.; Marwan, N.; Kurths, J.

2015-12-01

The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for this purpose, the mapogram approach allows the specific focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics. We illustrate this approach by application on mixed dynamics, such as traveling parallel wave fronts with additive noise, as well as more complicate examples, pseudo-random numbers and coupled map lattices with a semi-logistic mapping rule. Especially the complicate examples show the usefulness of the multi-scale consideration in order to take spatial pattern of different scales and with different rhythms into account. So, this mapogram approach promises new insights in problems of climatology, ecology, or medicine.

Non-reciprocal elastic wave propagation in 2D phononic membranes with spatiotemporally varying material properties

Science.gov (United States)

Attarzadeh, M. A.; Nouh, M.

2018-05-01

One-dimensional phononic materials with material fields traveling simultaneously in space and time have been shown to break elastodynamic reciprocity resulting in unique wave propagation features. In the present work, a comprehensive mathematical analysis is presented to characterize and fully predict the non-reciprocal wave dispersion in two-dimensional space. The analytical dispersion relations, in the presence of the spatiotemporal material variations, are validated numerically using finite 2D membranes with a prescribed number of cells. Using omnidirectional excitations at the membrane's center, wave propagations are shown to exhibit directional asymmetry that increases drastically in the direction of the material travel and vanishes in the direction perpendicular to it. The topological nature of the predicted dispersion in different propagation directions are evaluated using the computed Chern numbers. Finally, the degree of the 2D non-reciprocity is quantified using a non-reciprocity index (NRI) which confirms the theoretical dispersion predictions as well as the finite simulations. The presented framework can be extended to plate-type structures as well as 3D spatiotemporally modulated phononic crystals.

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China

Directory of Open Access Journals (Sweden)

Chunxiang Cao

2016-01-01

Full Text Available Severe acute respiratory syndrome (SARS is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

What Is Spatio-Temporal Data Warehousing?

Science.gov (United States)

Vaisman, Alejandro; Zimányi, Esteban

In the last years, extending OLAP (On-Line Analytical Processing) systems with spatial and temporal features has attracted the attention of the GIS (Geographic Information Systems) and database communities. However, there is no a commonly agreed definition of what is a spatio-temporal data warehouse and what functionality such a data warehouse should support. Further, the solutions proposed in the literature vary considerably in the kind of data that can be represented as well as the kind of queries that can be expressed. In this paper we present a conceptual framework for defining spatio-temporal data warehouses using an extensible data type system. We also define a taxonomy of different classes of queries of increasing expressive power, and show how to express such queries using an extension of the tuple relational calculus with aggregated functions.

Spatio-temporal modelling of atmospheric pollution based on observations provided by an air quality monitoring network at a regional scale

International Nuclear Information System (INIS)

Coman, A.

2008-01-01

This study is devoted to the spatio-temporal modelling of air pollution at a regional scale using a set of statistical methods in order to treat the measurements of pollutant concentrations (NO 2 , O 3 ) provided by an air quality monitoring network (AIRPARIF). The main objective is the improvement of the pollutant fields mapping using either interpolation methods based on the spatial or spatio-temporal structure of the data (spatial or spatio-temporal kriging) or some algorithms taking into account the observations, in order to correct the concentrations simulated by a deterministic model (Ensemble Kalman Filter). The results show that nitrogen dioxide mapping based only on spatial interpolation (kriging) gives the best results, while the spatial repartition of the monitoring sites is good. For the ozone mapping it is the sequential data assimilation that leads us to a better reconstruction of the plume's form and position for the analyzed cases. Complementary to the pollutant mapping, another objective was to perform a local prediction of ozone concentrations on a 24-hour horizon; this task was performed using Artificial Neural Networks. The performance indices obtained using two types of neural architectures indicate a fair accuracy especially for the first 8 hours of prediction horizon. (author)

Spatiotemporal Determinants of Urban Leptospirosis Transmission: Four-Year Prospective Cohort Study of Slum Residents in Brazil.

Science.gov (United States)

Hagan, José E; Moraga, Paula; Costa, Federico; Capian, Nicolas; Ribeiro, Guilherme S; Wunder, Elsio A; Felzemburgh, Ridalva D M; Reis, Renato B; Nery, Nivison; Santana, Francisco S; Fraga, Deborah; Dos Santos, Balbino L; Santos, Andréia C; Queiroz, Adriano; Tassinari, Wagner; Carvalho, Marilia S; Reis, Mitermayer G; Diggle, Peter J; Ko, Albert I

2016-01-01

Rat-borne leptospirosis is an emerging zoonotic disease in urban slum settlements for which there are no adequate control measures. The challenge in elucidating risk factors and informing approaches for prevention is the complex and heterogeneous environment within slums, which vary at fine spatial scales and influence transmission of the bacterial agent. We performed a prospective study of 2,003 slum residents in the city of Salvador, Brazil during a four-year period (2003-2007) and used a spatiotemporal modelling approach to delineate the dynamics of leptospiral transmission. Household interviews and Geographical Information System surveys were performed annually to evaluate risk exposures and environmental transmission sources. We completed annual serosurveys to ascertain leptospiral infection based on serological evidence. Among the 1,730 (86%) individuals who completed at least one year of follow-up, the infection rate was 35.4 (95% CI, 30.7-40.6) per 1,000 annual follow-up events. Male gender, illiteracy, and age were independently associated with infection risk. Environmental risk factors included rat infestation (OR 1.46, 95% CI, 1.00-2.16), contact with mud (OR 1.57, 95% CI 1.17-2.17) and lower household elevation (OR 0.92 per 10m increase in elevation, 95% CI 0.82-1.04). The spatial distribution of infection risk was highly heterogeneous and varied across small scales. Fixed effects in the spatiotemporal model accounted for the majority of the spatial variation in risk, but there was a significant residual component that was best explained by the spatial random effect. Although infection risk varied between years, the spatial distribution of risk associated with fixed and random effects did not vary temporally. Specific "hot-spots" consistently had higher transmission risk during study years. The risk for leptospiral infection in urban slums is determined in large part by structural features, both social and environmental. Our findings indicate that

Spatiotemporal Determinants of Urban Leptospirosis Transmission: Four-Year Prospective Cohort Study of Slum Residents in Brazil.

Directory of Open Access Journals (Sweden)

José E Hagan

2016-01-01

Full Text Available Rat-borne leptospirosis is an emerging zoonotic disease in urban slum settlements for which there are no adequate control measures. The challenge in elucidating risk factors and informing approaches for prevention is the complex and heterogeneous environment within slums, which vary at fine spatial scales and influence transmission of the bacterial agent.We performed a prospective study of 2,003 slum residents in the city of Salvador, Brazil during a four-year period (2003-2007 and used a spatiotemporal modelling approach to delineate the dynamics of leptospiral transmission. Household interviews and Geographical Information System surveys were performed annually to evaluate risk exposures and environmental transmission sources. We completed annual serosurveys to ascertain leptospiral infection based on serological evidence. Among the 1,730 (86% individuals who completed at least one year of follow-up, the infection rate was 35.4 (95% CI, 30.7-40.6 per 1,000 annual follow-up events. Male gender, illiteracy, and age were independently associated with infection risk. Environmental risk factors included rat infestation (OR 1.46, 95% CI, 1.00-2.16, contact with mud (OR 1.57, 95% CI 1.17-2.17 and lower household elevation (OR 0.92 per 10m increase in elevation, 95% CI 0.82-1.04. The spatial distribution of infection risk was highly heterogeneous and varied across small scales. Fixed effects in the spatiotemporal model accounted for the majority of the spatial variation in risk, but there was a significant residual component that was best explained by the spatial random effect. Although infection risk varied between years, the spatial distribution of risk associated with fixed and random effects did not vary temporally. Specific "hot-spots" consistently had higher transmission risk during study years.The risk for leptospiral infection in urban slums is determined in large part by structural features, both social and environmental. Our findings

Evolution of spatio-temporal drought characteristics: validation, projections and effect of adaptation scenarios

Science.gov (United States)

Vidal, J.-P.; Martin, E.; Kitova, N.; Najac, J.; Soubeyroux, J.-M.

2012-08-01

Drought events develop in both space and time and they are therefore best described through summary joint spatio-temporal characteristics, such as mean duration, mean affected area and total magnitude. This paper addresses the issue of future projections of such characteristics of drought events over France through three main research questions: (1) Are downscaled climate projections able to simulate spatio-temporal characteristics of meteorological and agricultural droughts in France over a present-day period? (2) How such characteristics will evolve over the 21st century? (3) How to use standardized drought indices to represent theoretical adaptation scenarios? These questions are addressed using the Isba land surface model, downscaled climate projections from the ARPEGE General Circulation Model under three emissions scenarios, as well as results from a previously performed 50-yr multilevel and multiscale drought reanalysis over France. Spatio-temporal characteristics of meteorological and agricultural drought events are computed using the Standardized Precipitation Index and the Standardized Soil Wetness Index, respectively, and for time scales of 3 and 12 months. Results first show that the distributions of joint spatio-temporal characteristics of observed events are well simulated by the downscaled hydroclimate projections over a present-day period. All spatio-temporal characteristics of drought events are then found to dramatically increase over the 21st century, with stronger changes for agricultural droughts. Two theoretical adaptation scenarios are eventually built based on hypotheses of adaptation to evolving climate and hydrological normals, either retrospective or prospective. The perceived spatio-temporal characteristics of drought events derived from these theoretical adaptation scenarios show much reduced changes, but they call for more realistic scenarios at both the catchment and national scale in order to accurately assess the combined effect of

Spatio-temporal Eigenvector Filtering: Application on Bioenergy Crop Impacts

Science.gov (United States)

Wang, M.; Kamarianakis, Y.; Georgescu, M.

2017-12-01

A suite of 10-year ensemble-based simulations was conducted to investigate the hydroclimatic impacts due to large-scale deployment of perennial bioenergy crops across the continental United States. Given the large size of the simulated dataset (about 60Tb), traditional hierarchical spatio-temporal statistical modelling cannot be implemented for the evaluation of physics parameterizations and biofuel impacts. In this work, we propose a filtering algorithm that takes into account the spatio-temporal autocorrelation structure of the data while avoiding spatial confounding. This method is used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations and observational datasets. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.

In Situ Spatiotemporal Mapping of Flow Fields around Seeded Stem Cells at the Subcellular Length Scale

Science.gov (United States)

Song, Min Jae; Dean, David; Knothe Tate, Melissa L.

2010-01-01

A major hurdle to understanding and exploiting interactions between the stem cell and its environment is the lack of a tool for precise delivery of mechanical cues concomitant to observing sub-cellular adaptation of structure. These studies demonstrate the use of microscale particle image velocimetry (μ-PIV) for in situ spatiotemporal mapping of flow fields around mesenchymal stem cells, i.e. murine embryonic multipotent cell line C3H10T1/2, at the subcellular length scale, providing a tool for real time observation and analysis of stem cell adaptation to the prevailing mechanical milieu. In the absence of cells, computational fluid dynamics (CFD) predicts flow regimes within 12% of μ-PIV measures, achieving the technical specifications of the chamber and the flow rates necessary to deliver target shear stresses at a particular height from the base of the flow chamber. However, our μ-PIV studies show that the presence of cells per se as well as the density at which cells are seeded significantly influences local flow fields. Furthermore, for any given cell or cell seeding density, flow regimes vary significantly along the vertical profile of the cell. Hence, the mechanical milieu of the stem cell exposed to shape changing shear stresses, induced by fluid drag, varies with respect to proximity of surrounding cells as well as with respect to apical height. The current study addresses a previously unmet need to predict and observe both flow regimes as well as mechanoadaptation of cells in flow chambers designed to deliver precisely controlled mechanical signals to live cells. An understanding of interactions and adaptation in response to forces at the interface between the surface of the cell and its immediate local environment may be key for de novo engineering of functional tissues from stem cell templates as well as for unraveling the mechanisms underlying multiscale development, growth and adaptation of organisms. PMID:20862249

In situ spatiotemporal mapping of flow fields around seeded stem cells at the subcellular length scale.

Directory of Open Access Journals (Sweden)

Min Jae Song

2010-09-01

Full Text Available A major hurdle to understanding and exploiting interactions between the stem cell and its environment is the lack of a tool for precise delivery of mechanical cues concomitant to observing sub-cellular adaptation of structure. These studies demonstrate the use of microscale particle image velocimetry (μ-PIV for in situ spatiotemporal mapping of flow fields around mesenchymal stem cells, i.e. murine embryonic multipotent cell line C3H10T1/2, at the subcellular length scale, providing a tool for real time observation and analysis of stem cell adaptation to the prevailing mechanical milieu. In the absence of cells, computational fluid dynamics (CFD predicts flow regimes within 12% of μ-PIV measures, achieving the technical specifications of the chamber and the flow rates necessary to deliver target shear stresses at a particular height from the base of the flow chamber. However, our μ-PIV studies show that the presence of cells per se as well as the density at which cells are seeded significantly influences local flow fields. Furthermore, for any given cell or cell seeding density, flow regimes vary significantly along the vertical profile of the cell. Hence, the mechanical milieu of the stem cell exposed to shape changing shear stresses, induced by fluid drag, varies with respect to proximity of surrounding cells as well as with respect to apical height. The current study addresses a previously unmet need to predict and observe both flow regimes as well as mechanoadaptation of cells in flow chambers designed to deliver precisely controlled mechanical signals to live cells. An understanding of interactions and adaptation in response to forces at the interface between the surface of the cell and its immediate local environment may be key for de novo engineering of functional tissues from stem cell templates as well as for unraveling the mechanisms underlying multiscale development, growth and adaptation of organisms.

Visual search of cyclic spatio-temporal events

Science.gov (United States)

Gautier, Jacques; Davoine, Paule-Annick; Cunty, Claire

2018-05-01

The analysis of spatio-temporal events, and especially of relationships between their different dimensions (space-time-thematic attributes), can be done with geovisualization interfaces. But few geovisualization tools integrate the cyclic dimension of spatio-temporal event series (natural events or social events). Time Coil and Time Wave diagrams represent both the linear time and the cyclic time. By introducing a cyclic temporal scale, these diagrams may highlight the cyclic characteristics of spatio-temporal events. However, the settable cyclic temporal scales are limited to usual durations like days or months. Because of that, these diagrams cannot be used to visualize cyclic events, which reappear with an unusual period, and don't allow to make a visual search of cyclic events. Also, they don't give the possibility to identify the relationships between the cyclic behavior of the events and their spatial features, and more especially to identify localised cyclic events. The lack of possibilities to represent the cyclic time, outside of the temporal diagram of multi-view geovisualization interfaces, limits the analysis of relationships between the cyclic reappearance of events and their other dimensions. In this paper, we propose a method and a geovisualization tool, based on the extension of Time Coil and Time Wave, to provide a visual search of cyclic events, by allowing to set any possible duration to the diagram's cyclic temporal scale. We also propose a symbology approach to push the representation of the cyclic time into the map, in order to improve the analysis of relationships between space and the cyclic behavior of events.

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex.

Directory of Open Access Journals (Sweden)

Laureline Logiaco

2015-08-01

Full Text Available The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex.

Science.gov (United States)

Logiaco, Laureline; Quilodran, René; Procyk, Emmanuel; Arleo, Angelo

2015-08-01

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.

Frontal Neurons Modulate Memory Retrieval across Widely Varying Temporal Scales

Science.gov (United States)

Zhang, Wen-Hua; Williams, Ziv M.

2015-01-01

Once a memory has formed, it is thought to undergo a gradual transition within the brain from short- to long-term storage. This putative process, however, also poses a unique problem to the memory system in that the same learned items must also be retrieved across broadly varying time scales. Here, we find that neurons in the ventrolateral…

Spatio-Temporal Rule Mining

DEFF Research Database (Denmark)

Gidofalvi, Gyozo; Pedersen, Torben Bach

2005-01-01

Recent advances in communication and information technology, such as the increasing accuracy of GPS technology and the miniaturization of wireless communication devices pave the road for Location-Based Services (LBS). To achieve high quality for such services, spatio-temporal data mining techniques...... are needed. In this paper, we describe experiences with spatio-temporal rule mining in a Danish data mining company. First, a number of real world spatio-temporal data sets are described, leading to a taxonomy of spatio-temporal data. Second, the paper describes a general methodology that transforms...... the spatio-temporal rule mining task to the traditional market basket analysis task and applies it to the described data sets, enabling traditional association rule mining methods to discover spatio-temporal rules for LBS. Finally, unique issues in spatio-temporal rule mining are identified and discussed....

Temporal stability of soil moisture under different land uses/cover in the Loess Plateau based on a finer spatiotemporal scale

OpenAIRE

Zhou, J.; Fu, B. J.; Lü, N.; Gao, G. Y.; Lü, Y. H.; Wang, S.

2013-01-01

The Temporal stability of soil moisture (TSSM) is an important factor to evaluate the value of available water resources in a water-controlled ecosystem. In this study we used the evapotranspiration-TSSM (ET-TSSM) model and a new sampling design to examine the soil water dynamics and water balance of different land uses/cover types in a hilly landscape of the Loess Plateau under a finer spatiotemporal scale. Our primary focus is to examine the difference amo...

Dying like rabbits: general determinants of spatio-temporal variability in survival.

Science.gov (United States)

Tablado, Zulima; Revilla, Eloy; Palomares, Francisco

2012-01-01

1. Identifying general patterns of how and why survival rates vary across space and time is necessary to truly understand population dynamics of a species. However, this is not an easy task given the complexity and interactions of processes involved, and the interpopulation differences in main survival determinants. 2. Here, using European rabbits (Oryctolagus cuniculus) as a model and information from local studies, we investigated whether we could make inferences about trends and drivers of survival of a species that are generalizable to large spatio-temporal scales. To do this, we first focused on overall survival and then examined cause-specific mortalities, mainly predation and diseases, which may lead to those patterns. 3. Our results show that within the large-scale variability in rabbit survival, there exist general patterns that are explained by the integration of factors previously known to be important at the local level (i.e. age, climate, diseases, predation or density dependence). We found that both inter- and intrastudy survival rates increased in magnitude and decreased in variability as rabbits grow old, although this tendency was less pronounced in populations with epidemic diseases. Some causes leading to these higher mortalities in young rabbits could be the stronger effect of rainfall at those ages, as well as, other death sources like malnutrition or infanticide. 4. Predation is also greater for newborns and juveniles, especially in population without diseases. Apart from the effect of diseases, predation patterns also depended on factors, such as, density, season, and type and density of predators. Finally, we observed that infectious diseases also showed general relationships with climate, breeding (i.e. new susceptible rabbits) and age, although the association type varied between myxomatosis and rabbit haemorrhagic disease. 5. In conclusion, large-scale patterns of spatio-temporal variability in rabbit survival emerge from the combination

AN ADAPTIVE ORGANIZATION METHOD OF GEOVIDEO DATA FOR SPATIO-TEMPORAL ASSOCIATION ANALYSIS

Directory of Open Access Journals (Sweden)

C. Wu

2015-07-01

Full Text Available Public security incidents have been increasingly challenging to address with their new features, including large-scale mobility, multi-stage dynamic evolution, spatio-temporal concurrency and uncertainty in the complex urban environment, which require spatio-temporal association analysis among multiple regional video data for global cognition. However, the existing video data organizational methods that view video as a property of the spatial object or position in space dissever the spatio-temporal relationship of scattered video shots captured from multiple video channels, limit the query functions on interactive retrieval between a camera and its video clips and hinder the comprehensive management of event-related scattered video shots. GeoVideo, which maps video frames onto a geographic space, is a new approach to represent the geographic world, promote security monitoring in a spatial perspective and provide a highly feasible solution to this problem. This paper analyzes the large-scale personnel mobility in public safety events and proposes a multi-level, event-related organization method with massive GeoVideo data by spatio-temporal trajectory. This paper designs a unified object identify(ID structure to implicitly store the spatio-temporal relationship of scattered video clips and support the distributed storage management of massive cases. Finally, the validity and feasibility of this method are demonstrated through suspect tracking experiments.

Spatio-Temporal Process Simulation of Dam-Break Flood Based on SPH

Science.gov (United States)

Wang, H.; Ye, F.; Ouyang, S.; Li, Z.

2018-04-01

On the basis of introducing the SPH (Smooth Particle Hydrodynamics) simulation method, the key research problems were given solutions in this paper, which ere the spatial scale and temporal scale adapting to the GIS(Geographical Information System) application, the boundary condition equations combined with the underlying surface, and the kernel function and parameters applicable to dam-break flood simulation. In this regards, a calculation method of spatio-temporal process emulation with elaborate particles for dam-break flood was proposed. Moreover the spatio-temporal process was dynamic simulated by using GIS modelling and visualization. The results show that the method gets more information, objectiveness and real situations.

Oscillations, complex spatiotemporal behavior, and information transport in networks of excitatory and inhibitory neurons

International Nuclear Information System (INIS)

Destexhe, A.

1994-01-01

Various types of spatiotemporal behavior are described for two-dimensional networks of excitatory and inhibitory neurons with time delayed interactions. It is described how the network behaves as several structural parameters are varied, such as the number of neurons, the connectivity, and the values of synaptic weights. A transition from spatially uniform oscillations to spatiotemporal chaos via intermittentlike behavior is observed. The properties of spatiotemporally chaotic solutions are investigated by evaluating the largest positive Lyapunov exponent and the loss of correlation with distance. Finally, properties of information transport are evaluated during uniform oscillations and spatiotemporal chaos. It is shown that the diffusion coefficient increases significantly in the spatiotemporal phase similar to the increase of transport coefficients at the onset of fluid turbulence. It is proposed that such a property should be seen in other media, such as chemical turbulence or networks of oscillators. The possibility of measuring information transport from appropriate experiments is also discussed

An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics

Science.gov (United States)

Williams, Perry J.; Hooten, Mevin B.; Womble, Jamie N.; Esslinger, George G.; Bower, Michael R.; Hefley, Trevor J.

2017-01-01

Ecological invasions and colonizations occur dynamically through space and time. Estimating the distribution and abundance of colonizing species is critical for efficient management or conservation. We describe a statistical framework for simultaneously estimating spatiotemporal occupancy and abundance dynamics of a colonizing species. Our method accounts for several issues that are common when modeling spatiotemporal ecological data including multiple levels of detection probability, multiple data sources, and computational limitations that occur when making fine-scale inference over a large spatiotemporal domain. We apply the model to estimate the colonization dynamics of sea otters (Enhydra lutris) in Glacier Bay, in southeastern Alaska.

Assessing global vegetation activity using spatio-temporal Bayesian modelling

Science.gov (United States)

Mulder, Vera L.; van Eck, Christel M.; Friedlingstein, Pierre; Regnier, Pierre A. G.

2016-04-01

This work demonstrates the potential of modelling vegetation activity using a hierarchical Bayesian spatio-temporal model. This approach allows modelling changes in vegetation and climate simultaneous in space and time. Changes of vegetation activity such as phenology are modelled as a dynamic process depending on climate variability in both space and time. Additionally, differences in observed vegetation status can be contributed to other abiotic ecosystem properties, e.g. soil and terrain properties. Although these properties do not change in time, they do change in space and may provide valuable information in addition to the climate dynamics. The spatio-temporal Bayesian models were calibrated at a regional scale because the local trends in space and time can be better captured by the model. The regional subsets were defined according to the SREX segmentation, as defined by the IPCC. Each region is considered being relatively homogeneous in terms of large-scale climate and biomes, still capturing small-scale (grid-cell level) variability. Modelling within these regions is hence expected to be less uncertain due to the absence of these large-scale patterns, compared to a global approach. This overall modelling approach allows the comparison of model behavior for the different regions and may provide insights on the main dynamic processes driving the interaction between vegetation and climate within different regions. The data employed in this study encompasses the global datasets for soil properties (SoilGrids), terrain properties (Global Relief Model based on SRTM DEM and ETOPO), monthly time series of satellite-derived vegetation indices (GIMMS NDVI3g) and climate variables (Princeton Meteorological Forcing Dataset). The findings proved the potential of a spatio-temporal Bayesian modelling approach for assessing vegetation dynamics, at a regional scale. The observed interrelationships of the employed data and the different spatial and temporal trends support

Spatiotemporal Data Mining: A Computational Perspective

Directory of Open Access Journals (Sweden)

Shashi Shekhar

2015-10-01

Full Text Available Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

Finite-Time Stability of Large-Scale Systems with Interval Time-Varying Delay in Interconnection

Directory of Open Access Journals (Sweden)

T. La-inchua

2017-01-01

Full Text Available We investigate finite-time stability of a class of nonlinear large-scale systems with interval time-varying delays in interconnection. Time-delay functions are continuous but not necessarily differentiable. Based on Lyapunov stability theory and new integral bounding technique, finite-time stability of large-scale systems with interval time-varying delays in interconnection is derived. The finite-time stability criteria are delays-dependent and are given in terms of linear matrix inequalities which can be solved by various available algorithms. Numerical examples are given to illustrate effectiveness of the proposed method.

Replication Strategy for Spatiotemporal Data Based on Distributed Caching System.

Science.gov (United States)

Xiong, Lian; Yang, Liu; Tao, Yang; Xu, Juan; Zhao, Lun

2018-01-14

The replica strategy in distributed cache can effectively reduce user access delay and improve system performance. However, developing a replica strategy suitable for varied application scenarios is still quite challenging, owing to differences in user access behavior and preferences. In this paper, a replication strategy for spatiotemporal data (RSSD) based on a distributed caching system is proposed. By taking advantage of the spatiotemporal locality and correlation of user access, RSSD mines high popularity and associated files from historical user access information, and then generates replicas and selects appropriate cache node for placement. Experimental results show that the RSSD algorithm is simple and efficient, and succeeds in significantly reducing user access delay.

Challenges in transferring knowledge between scales in coastal sediment dynamics

Directory of Open Access Journals (Sweden)

Shari L Gallop

2015-10-01

Full Text Available ‘Packaging’ coastal sediment transport into discrete temporal and spatial scale bands is necessary for measurement programs, modelling, and design. However, determining how to best measure and parameterize information, to transfer between scales, is not trivial. An overview is provided of the major complexities in transferring information on coastal sediment transport between scales. Key considerations that recur in the literature include: interaction between sediment transport and morphology; the influence of biota; episodic sediment transport; and recovery time-scales. The influence of bedforms and landforms, as well as sediment-biota interactions, varies with spatio-temporal scale. In some situations, episodic sediment dynamics is the main contributor to long-term sediment transport. Such events can also significantly alter biogeochemical and ecological processes, which interact with sediments. The impact of such episodic events is fundamentally influenced by recovery time-scales, which vary spatially. For the various approaches to scaling (e.g., bottom-up, aggregation, spatial hierarchies, there is a need for fundamental research on the assumptions inherent in each approach.

Dynamic characterizers of spatiotemporal intermittency

OpenAIRE

Gupte, Neelima; Jabeen, Zahera

2006-01-01

Systems of coupled sine circle maps show regimes of spatiotemporally intermittent behaviour with associated scaling exponents which belong to the DP class, as well as regimes of spatially intermittent behaviour (with associated regular dynamical behaviour) which do not belong to the DP class. Both types of behaviour are seen along the bifurcation boundaries of the synchronized solutions, and contribute distinct signatures to the dynamical characterizers of the system, viz. the distribution of...

Spatiotemporal radiotherapy planning using a global optimization approach

Science.gov (United States)

Adibi, Ali; Salari, Ehsan

2018-02-01

This paper aims at quantifying the extent of potential therapeutic gain, measured using biologically effective dose (BED), that can be achieved by altering the radiation dose distribution over treatment sessions in fractionated radiotherapy. To that end, a spatiotemporally integrated planning approach is developed, where the spatial and temporal dose modulations are optimized simultaneously. The concept of equivalent uniform BED (EUBED) is used to quantify and compare the clinical quality of spatiotemporally heterogeneous dose distributions in target and critical structures. This gives rise to a large-scale non-convex treatment-plan optimization problem, which is solved using global optimization techniques. The proposed spatiotemporal planning approach is tested on two stylized cancer cases resembling two different tumor sites and sensitivity analysis is performed for radio-biological and EUBED parameters. Numerical results validate that spatiotemporal plans are capable of delivering a larger BED to the target volume without increasing the BED in critical structures compared to conventional time-invariant plans. In particular, this additional gain is attributed to the irradiation of different regions of the target volume at different treatment sessions. Additionally, the trade-off between the potential therapeutic gain and the number of distinct dose distributions is quantified, which suggests a diminishing marginal gain as the number of dose distributions increases.

GISpark: A Geospatial Distributed Computing Platform for Spatiotemporal Big Data

Science.gov (United States)

Wang, S.; Zhong, E.; Wang, E.; Zhong, Y.; Cai, W.; Li, S.; Gao, S.

2016-12-01

Geospatial data are growing exponentially because of the proliferation of cost effective and ubiquitous positioning technologies such as global remote-sensing satellites and location-based devices. Analyzing large amounts of geospatial data can provide great value for both industrial and scientific applications. Data- and compute- intensive characteristics inherent in geospatial big data increasingly pose great challenges to technologies of data storing, computing and analyzing. Such challenges require a scalable and efficient architecture that can store, query, analyze, and visualize large-scale spatiotemporal data. Therefore, we developed GISpark - a geospatial distributed computing platform for processing large-scale vector, raster and stream data. GISpark is constructed based on the latest virtualized computing infrastructures and distributed computing architecture. OpenStack and Docker are used to build multi-user hosting cloud computing infrastructure for GISpark. The virtual storage systems such as HDFS, Ceph, MongoDB are combined and adopted for spatiotemporal data storage management. Spark-based algorithm framework is developed for efficient parallel computing. Within this framework, SuperMap GIScript and various open-source GIS libraries can be integrated into GISpark. GISpark can also integrated with scientific computing environment (e.g., Anaconda), interactive computing web applications (e.g., Jupyter notebook), and machine learning tools (e.g., TensorFlow/Orange). The associated geospatial facilities of GISpark in conjunction with the scientific computing environment, exploratory spatial data analysis tools, temporal data management and analysis systems make up a powerful geospatial computing tool. GISpark not only provides spatiotemporal big data processing capacity in the geospatial field, but also provides spatiotemporal computational model and advanced geospatial visualization tools that deals with other domains related with spatial property. We

Replication Strategy for Spatiotemporal Data Based on Distributed Caching System

Science.gov (United States)

Xiong, Lian; Tao, Yang; Xu, Juan; Zhao, Lun

2018-01-01

The replica strategy in distributed cache can effectively reduce user access delay and improve system performance. However, developing a replica strategy suitable for varied application scenarios is still quite challenging, owing to differences in user access behavior and preferences. In this paper, a replication strategy for spatiotemporal data (RSSD) based on a distributed caching system is proposed. By taking advantage of the spatiotemporal locality and correlation of user access, RSSD mines high popularity and associated files from historical user access information, and then generates replicas and selects appropriate cache node for placement. Experimental results show that the RSSD algorithm is simple and efficient, and succeeds in significantly reducing user access delay. PMID:29342897

Spatiotemporal Thinking in the Geosciences

Science.gov (United States)

Shipley, T. F.; Manduca, C. A.; Ormand, C. J.; Tikoff, B.

2011-12-01

Reasoning about spatial relations is a critical skill for geoscientists. Within the geosciences different disciplines may reason about different sorts of relationships. These relationships may span vastly different spatial and temporal scales (from the spatial alignment in atoms in crystals to the changes in the shape of plates). As part of work in a research center on spatial thinking in STEM education, we have been working to classify the spatial skills required in geology, develop tests for each spatial skill, and develop the cognitive science tools to promote the critical spatial reasoning skills. Research in psychology, neurology and linguistics supports a broad classification of spatial skills along two dimensions: one versus many objects (which roughly translates to object- focused and navigation focused skills) and static versus dynamic spatial relations. The talk will focus on the interaction of space and time in spatial cognition in the geosciences. We are working to develop measures of skill in visualizing spatiotemporal changes. A new test developed to measure visualization of brittle deformations will be presented. This is a skill that has not been clearly recognized in the cognitive science research domain and thus illustrates the value of interdisciplinary work that combines geosciences with cognitive sciences. Teaching spatiotemporal concepts can be challenging. Recent theoretical work suggests analogical reasoning can be a powerful tool to aid student learning to reason about temporal relations using spatial skills. Recent work in our lab has found that progressive alignment of spatial and temporal scales promotes accurate reasoning about temporal relations at geological time scales.

Entropy Rate of Time-Varying Wireless Networks

DEFF Research Database (Denmark)

Cika, Arta; Badiu, Mihai Alin; Coon, Justin P.

2018-01-01

In this paper, we present a detailed framework to analyze the evolution of the random topology of a time-varying wireless network via the information theoretic notion of entropy rate. We consider a propagation channel varying over time with random node positions in a closed space and Rayleigh...... fading affecting the connections between nodes. The existence of an edge between two nodes at given locations is modeled by a Markov chain, enabling memory effects in network dynamics. We then derive a lower and an upper bound on the entropy rate of the spatiotemporal network. The entropy rate measures...

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement.

Science.gov (United States)

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-02-07

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L ₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

Science.gov (United States)

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-01-01

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements. PMID:29414893

Spatiotemporal models of global soil organic carbon stock to support land degradation assessments at regional and global scales: limitations, challenges and opportunities

Science.gov (United States)

Hengl, Tomislav; Heuvelink, Gerard; Sanderman, Jonathan; MacMillan, Robert

2017-04-01

There is an increasing interest in fitting and applying spatiotemporal models that can be used to assess and monitor soil organic carbon stocks (SOCS), for example, in support of the '4 pourmille' initiative aiming at soil carbon sequestration towards climate change adaptation and mitigation and UN's Land Degradation Neutrality indicators and similar degradation assessment projects at regional and global scales. The land cover mapping community has already produced several spatiotemporal data sets with global coverage and at relatively fine resolution e.g. USGS MODIS land cover annual maps for period 2000-2014; European Space Agency land cover maps at 300 m resolution for the year 2000, 2005 and 2010; Chinese GlobeLand30 dataset available for years 2000 and 2010; Columbia University's WRI GlobalForestWatch with deforestation maps at 30 m resolution for the period 2000-2016 (Hansen et al. 2013). These data sets can be used for land degradation assessment and scenario testing at global and regional scales (Wei et al 2014). Currently, however, no compatible global spatiotemporal data sets exist on status of soil quality and/or soil health (Powlson et al. 2013). This paper describes an initial effort to devise and evaluate a procedure for mapping spatio-temporal changes in SOC stocks using a complete stack of soil forming factors (climate, relief, land cover, land use, lithology and living organisms) represented mainly through remote sensing based time series of Earth images. For model building we used some 75,000 geo-referenced soil profiles and a stacks space-time covariates (land cover, land use, biomass, climate) at two standard resolutions: (1) 10 km resolution with data available for period 1920-2014 and (2) 1000 m resolution with data available for period 2000-2014. The initial results show that, although it is technically feasible to produce space time estimates of SOCS that demonstrate the procedure, the estimates are relatively uncertain (<45% of variation

Emergent dynamics of spatio-temporal chaos in a heterogeneous excitable medium.

Science.gov (United States)

Bittihn, Philip; Berg, Sebastian; Parlitz, Ulrich; Luther, Stefan

2017-09-01

Self-organized activation patterns in excitable media such as spiral waves and spatio-temporal chaos underlie dangerous cardiac arrhythmias. While the interaction of single spiral waves with different types of heterogeneity has been studied extensively, the effect of heterogeneity on fully developed spatio-temporal chaos remains poorly understood. We investigate how the complexity and stability properties of spatio-temporal chaos in the Bär-Eiswirth model of excitable media depend on the heterogeneity of the underlying medium. We employ different measures characterizing the chaoticity of the system and find that the spatial arrangement of multiple discrete lower excitability regions has a strong impact on the complexity of the dynamics. Varying the number, shape, and spatial arrangement of the heterogeneities, we observe strong emergent effects ranging from increases in chaoticity to the complete cessation of chaos, contrasting the expectation from the homogeneous behavior. The implications of our findings for the development and treatment of arrhythmias in the heterogeneous cardiac muscle are discussed.

Evaluation of high resolution spatio-temporal precipitation extremes from a stochastic weather generator

DEFF Research Database (Denmark)

Sørup, Hjalte Jomo Danielsen; Christensen, O. B.; Arnbjerg-Nielsen, Karsten

2017-01-01

Spatio-temporal rainfall is modelled for the North-Eastern part of Zealand (Denmark) using the Spatio-Temporal Neyman-Scott Rectangular Pulses model as implemented in the RainSim software. Hourly precipitation series for fitting the model are obtained from a dense network of tipping bucket rain...... gauges in the model area. The spatiotemporal performance of the model with respect to precipitation extremes is evaluated in the points of a 2x2 km regular grid covering the full model area. The model satisfactorily reproduces the extreme behaviour of the observed precipitation with respect to event...... intensity levels and unconditional spatial correlation when evaluated using an event based ranking approach at point scale and an advanced spatiotemporal coupling of extreme events. Prospectively the model can be used as a tool to evaluate the impact of climate change without relying on precipitation output...

Spatio-Temporal Data Analysis at Scale Using Models Based on Gaussian Processes

Energy Technology Data Exchange (ETDEWEB)

Stein, Michael [Univ. of Chicago, IL (United States)

2017-03-13

Gaussian processes are the most commonly used statistical model for spatial and spatio-temporal processes that vary continuously. They are broadly applicable in the physical sciences and engineering and are also frequently used to approximate the output of complex computer models, deterministic or stochastic. We undertook research related to theory, computation, and applications of Gaussian processes as well as some work on estimating extremes of distributions for which a Gaussian process assumption might be inappropriate. Our theoretical contributions include the development of new classes of spatial-temporal covariance functions with desirable properties and new results showing that certain covariance models lead to predictions with undesirable properties. To understand how Gaussian process models behave when applied to deterministic computer models, we derived what we believe to be the first significant results on the large sample properties of estimators of parameters of Gaussian processes when the actual process is a simple deterministic function. Finally, we investigated some theoretical issues related to maxima of observations with varying upper bounds and found that, depending on the circumstances, standard large sample results for maxima may or may not hold. Our computational innovations include methods for analyzing large spatial datasets when observations fall on a partially observed grid and methods for estimating parameters of a Gaussian process model from observations taken by a polar-orbiting satellite. In our application of Gaussian process models to deterministic computer experiments, we carried out some matrix computations that would have been infeasible using even extended precision arithmetic by focusing on special cases in which all elements of the matrices under study are rational and using exact arithmetic. The applications we studied include total column ozone as measured from a polar-orbiting satellite, sea surface temperatures over the

Effects of climate change adaptation scenarios on perceived spatio-temporal characteristics of drought events

Science.gov (United States)

Vidal, J.-P.; Martin, E.; Kitova, N.; Najac, J.; Soubeyroux, J.-M.

2012-04-01

Drought events develop in both space and time and they are therefore best described through summary joint spatio-temporal characteristics, like mean duration, mean affected area and total magnitude. This study addresses the issue of future projections of such characteristics of drought events over France through three main research questions: (1) Are downscaled climate projections able to reproduce spatio-temporal characteristics of meteorological and agricultural droughts in France over a present-day period? (2) How such characteristics will evolve over the 21st century under different emissions scenarios? (3) How would perceived drought characteristics evolve under theoretical adaptation scenarios? These questions are addressed using the Isba land surface model, downscaled climate projections from the ARPEGE General Circulation Model under three emissions scenarios, as well as results from a previously performed 50-year multilevel and multiscale drought reanalysis over France (Vidal et al., 2010). Spatio-temporal characteristics of meteorological and agricultural drought events are computed using the Standardized Precipitation Index (SPI) and the Standardized Soil Wetness Index (SSWI), respectively, and for time scales of 3 and 12 months. Results first show that the distributions of joint spatio-temporal characteristics of observed events are well reproduced by the downscaled hydroclimate projections over a present-day period. All spatio-temporal characteristics of drought events are then found to dramatically increase over the 21st century under all considered emissions scenarios, with stronger changes for agricultural droughts. Two theoretical adaptation scenarios are eventually built based on hypotheses of adaptation to evolving climate and hydrological normals. The two scenarios differ by the way the transient adaptation is performed for a given date in the future, with reference to the normals over either the previous 30-year window ("retrospective

Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model

Science.gov (United States)

Jalali, M. Ali; Ierodiaconou, Daniel; Gorfine, Harry; Monk, Jacquomo; Rattray, Alex

2015-01-01

Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100’s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics. PMID:25992800

Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model.

Directory of Open Access Journals (Sweden)

M Ali Jalali

Full Text Available Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100's of meters among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics.

The World Spatiotemporal Analytics and Mapping Project (WSTAMP): Discovering, Exploring, and Mapping Spatiotemporal Patterns Across Heterogenous Space-Time Data

Science.gov (United States)

Morton, A.; Stewart, R.; Held, E.; Piburn, J.; Allen, M. R.; McManamay, R.; Sanyal, J.; Sorokine, A.; Bhaduri, B. L.

2017-12-01

Spatiotemporal (ST) analytics applied to major spatio-temporal data sources from major vendors such as USGS, NOAA, World Bank and World Health Organization have tremendous value in shedding light on the evolution of physical, cultural, and geopolitical landscapes on a local and global level. Especially powerful is the integration of these physical and cultural datasets across multiple and disparate formats, facilitating new interdisciplinary analytics and insights. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, changing attributes, and content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at the Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 16000+ attributes covering 200+ countries for over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We report on these advances, provide an illustrative case study, and inform how others may freely access the tool.

A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution

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Marc Wieland

2017-04-01

Full Text Available With an ever-increasing volume and complexity of data collected from a variety of sources, the efficient management of geospatial information becomes a key topic in disaster risk management. For example, the representation of assets exposed to natural disasters is subjected to changes throughout the different phases of risk management reaching from pre-disaster mitigation to the response after an event and the long-term recovery of affected assets. Spatio-temporal changes need to be integrated into a sound conceptual and technological framework able to deal with data coming from different sources, at varying scales, and changing in space and time. Especially managing the information life-cycle, the integration of heterogeneous information and the distributed versioning and release of geospatial information are important topics that need to become essential parts of modern exposure modelling solutions. The main purpose of this study is to provide a conceptual and technological framework to tackle the requirements implied by disaster risk management for describing exposed assets in space and time. An information life-cycle management solution is proposed, based on a relational spatio-temporal database model coupled with Git and GeoGig repositories for distributed versioning. Two application scenarios focusing on the modelling of residential building stocks are presented to show the capabilities of the implemented solution. A prototype database model is shared on GitHub along with the necessary scenario data.

A FRAMEWORK FOR ONLINE SPATIO-TEMPORAL DATA VISUALIZATION BASED ON HTML5

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B. Mao

2012-07-01

Full Text Available Web is entering a new phase – HTML5. New features of HTML5 should be studied for online spatio-temporal data visualization. In the proposed framework, spatio-temporal data is stored in the data server and is sent to user browsers with WebSocket. Public geo-data such as Internet digital map is integrated into the browsers. Then animation is implemented through the canvas object defined by the HTML5 specification. To simulate the spatio-temporal data source, we collected the daily location of 15 users with GPS tracker. The current positions of the users are collected every minute and are recorded in a file. Based on this file, we generate a real time spatio-temporal data source which sends out current user location every second.By enlarging the real time scales by 60 times, we can observe the movement clearly. The data transmitted with WebSocket is the coordinates of users' current positions, which will can be demonstrated in client browsers.

Spatiotemporal Data Organization and Application Research

Science.gov (United States)

Tan, C.; Yan, S.

2017-09-01

Organization and management of spatiotemporal data is a key support technology for intelligence in all fields of the smart city. The construction of a smart city cannot be realized without spatiotemporal data. Oriented to support intelligent applications this paper proposes an organizational model for spatiotemporal data, and details the construction of a spatiotemporal big data calculation, analysis, and service framework for highly efficient management and intelligent application of spatiotemporal data for the entire data life cycle.

Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach.

Science.gov (United States)

Luan, Hui; Law, Jane; Quick, Matthew

2015-12-30

Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density. This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas. For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps. This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.

Spatiotemporal patterns and predictability of cyberattacks.

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Yu-Zhong Chen

Full Text Available A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term "spatio" refers to the IP address space. In particular, we focus on analyzing macroscopic properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack "fingerprints" and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches.

Projective synchronization of time-varying delayed neural network with adaptive scaling factors

International Nuclear Information System (INIS)

Ghosh, Dibakar; Banerjee, Santo

2013-01-01

Highlights: • Projective synchronization in coupled delayed neural chaotic systems with modulated delay time is introduced. • An adaptive rule for the scaling factors is introduced. • This scheme is highly applicable in secure communication. -- Abstract: In this work, the projective synchronization between two continuous time delayed neural systems with time varying delay is investigated. A sufficient condition for synchronization for the coupled systems with modulated delay is presented analytically with the help of the Krasovskii–Lyapunov approach. The effect of adaptive scaling factors on synchronization are also studied in details. Numerical simulations verify the effectiveness of the analytic results

Effective and efficient analysis of spatio-temporal data

Science.gov (United States)

Zhang, Zhongnan

Spatio-temporal data mining, i.e., mining knowledge from large amount of spatio-temporal data, is a highly demanding field because huge amounts of spatio-temporal data have been collected in various applications, ranging from remote sensing, to geographical information systems (GIS), computer cartography, environmental assessment and planning, etc. The collection data far exceeded human's ability to analyze which make it crucial to develop analysis tools. Recent studies on data mining have extended to the scope of data mining from relational and transactional datasets to spatial and temporal datasets. Among the various forms of spatio-temporal data, remote sensing images play an important role, due to the growing wide-spreading of outer space satellites. In this dissertation, we proposed two approaches to analyze the remote sensing data. The first one is about applying association rules mining onto images processing. Each image was divided into a number of image blocks. We built a spatial relationship for these blocks during the dividing process. This made a large number of images into a spatio-temporal dataset since each image was shot in time-series. The second one implemented co-occurrence patterns discovery from these images. The generated patterns represent subsets of spatial features that are located together in space and time. A weather analysis is composed of individual analysis of several meteorological variables. These variables include temperature, pressure, dew point, wind, clouds, visibility and so on. Local-scale models provide detailed analysis and forecasts of meteorological phenomena ranging from a few kilometers to about 100 kilometers in size. When some of above meteorological variables have some special change tendency, some kind of severe weather will happen in most cases. Using the discovery of association rules, we found that some special meteorological variables' changing has tight relation with some severe weather situation that will happen

Multi-Contextual Segregation and Environmental Justice Research: Toward Fine-Scale Spatiotemporal Approaches.

Science.gov (United States)

Park, Yoo Min; Kwan, Mei-Po

2017-10-10

Many environmental justice studies have sought to examine the effect of residential segregation on unequal exposure to environmental factors among different social groups, but little is known about how segregation in non-residential contexts affects such disparity. Based on a review of the relevant literature, this paper discusses the limitations of traditional residence-based approaches in examining the association between socioeconomic or racial/ethnic segregation and unequal environmental exposure in environmental justice research. It emphasizes that future research needs to go beyond residential segregation by considering the full spectrum of segregation experienced by people in various geographic and temporal contexts of everyday life. Along with this comprehensive understanding of segregation, the paper also highlights the importance of assessing environmental exposure at a high spatiotemporal resolution in environmental justice research. The successful integration of a comprehensive concept of segregation, high-resolution data and fine-grained spatiotemporal approaches to assessing segregation and environmental exposure would provide more nuanced and robust findings on the associations between segregation and disparities in environmental exposure and their health impacts. Moreover, it would also contribute to significantly expanding the scope of environmental justice research.

Multi-Contextual Segregation and Environmental Justice Research: Toward Fine-Scale Spatiotemporal Approaches

Directory of Open Access Journals (Sweden)

Yoo Min Park

2017-10-01

Full Text Available Many environmental justice studies have sought to examine the effect of residential segregation on unequal exposure to environmental factors among different social groups, but little is known about how segregation in non-residential contexts affects such disparity. Based on a review of the relevant literature, this paper discusses the limitations of traditional residence-based approaches in examining the association between socioeconomic or racial/ethnic segregation and unequal environmental exposure in environmental justice research. It emphasizes that future research needs to go beyond residential segregation by considering the full spectrum of segregation experienced by people in various geographic and temporal contexts of everyday life. Along with this comprehensive understanding of segregation, the paper also highlights the importance of assessing environmental exposure at a high spatiotemporal resolution in environmental justice research. The successful integration of a comprehensive concept of segregation, high-resolution data and fine-grained spatiotemporal approaches to assessing segregation and environmental exposure would provide more nuanced and robust findings on the associations between segregation and disparities in environmental exposure and their health impacts. Moreover, it would also contribute to significantly expanding the scope of environmental justice research.

The Berg Balance Scale has high intra- and inter-rater reliability but absolute reliability varies across the scale: a systematic review.

Science.gov (United States)

Downs, Stephen; Marquez, Jodie; Chiarelli, Pauline

2013-06-01

What is the intra-rater and inter-rater relative reliability of the Berg Balance Scale? What is the absolute reliability of the Berg Balance Scale? Does the absolute reliability of the Berg Balance Scale vary across the scale? Systematic review with meta-analysis of reliability studies. Any clinical population that has undergone assessment with the Berg Balance Scale. Relative intra-rater reliability, relative inter-rater reliability, and absolute reliability. Eleven studies involving 668 participants were included in the review. The relative intrarater reliability of the Berg Balance Scale was high, with a pooled estimate of 0.98 (95% CI 0.97 to 0.99). Relative inter-rater reliability was also high, with a pooled estimate of 0.97 (95% CI 0.96 to 0.98). A ceiling effect of the Berg Balance Scale was evident for some participants. In the analysis of absolute reliability, all of the relevant studies had an average score of 20 or above on the 0 to 56 point Berg Balance Scale. The absolute reliability across this part of the scale, as measured by the minimal detectable change with 95% confidence, varied between 2.8 points and 6.6 points. The Berg Balance Scale has a higher absolute reliability when close to 56 points due to the ceiling effect. We identified no data that estimated the absolute reliability of the Berg Balance Scale among participants with a mean score below 20 out of 56. The Berg Balance Scale has acceptable reliability, although it might not detect modest, clinically important changes in balance in individual subjects. The review was only able to comment on the absolute reliability of the Berg Balance Scale among people with moderately poor to normal balance. Copyright © 2013 Australian Physiotherapy Association. Published by .. All rights reserved.

Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000-2007

Science.gov (United States)

Yin, Ping; Mu, Lan; Madden, Marguerite; Vena, John E.

2014-10-01

Lung cancer is the second most commonly diagnosed cancer in both men and women in Georgia, USA. However, the spatio-temporal patterns of lung cancer risk in Georgia have not been fully studied. Hierarchical Bayesian models are used here to explore the spatio-temporal patterns of lung cancer incidence risk by race and gender in Georgia for the period of 2000-2007. With the census tract level as the spatial scale and the 2-year period aggregation as the temporal scale, we compare a total of seven Bayesian spatio-temporal models including two under a separate modeling framework and five under a joint modeling framework. One joint model outperforms others based on the deviance information criterion. Results show that the northwest region of Georgia has consistently high lung cancer incidence risk for all population groups during the study period. In addition, there are inverse relationships between the socioeconomic status and the lung cancer incidence risk among all Georgian population groups, and the relationships in males are stronger than those in females. By mapping more reliable variations in lung cancer incidence risk at a relatively fine spatio-temporal scale for different Georgian population groups, our study aims to better support healthcare performance assessment, etiological hypothesis generation, and health policy making.

Stability of neutrino parameters and self-complementarity relation with varying SUSY breaking scale

Science.gov (United States)

Singh, K. Sashikanta; Roy, Subhankar; Singh, N. Nimai

2018-03-01

The scale at which supersymmetry (SUSY) breaks (ms) is still unknown. The present article, following a top-down approach, endeavors to study the effect of varying ms on the radiative stability of the observational parameters associated with the neutrino mixing. These parameters get additional contributions in the minimal supersymmetric model (MSSM). A variation in ms will influence the bounds for which the Standard Model (SM) and MSSM work and hence, will account for the different radiative contributions received from both sectors, respectively, while running the renormalization group equations (RGE). The present work establishes the invariance of the self complementarity relation among the three mixing angles, θ13+θ12≈θ23 against the radiative evolution. A similar result concerning the mass ratio, m2:m1 is also found to be valid. In addition to varying ms, the work incorporates a range of different seesaw (SS) scales and tries to see how the latter affects the parameters.

Spatiotemporal variability and modeling of the solar irradiance transmissivity through a boreal forest

Science.gov (United States)

Nadeau, D.; Isabelle, P. E.; Asselin, M. H.; Parent, A. C.; Jutras, S.; Anctil, F.

2017-12-01

Solar irradiance is the largest driver of land-surface exchanges of energy, water and trace gases. Its absorption by a forest canopy generates considerable sensible and latent heat fluxes as well as tree temperature changes. A fraction of the irradiance gets transmitted through the canopy and powers another layer of energy fluxes, which can reach substantial values. Transmitted radiation is also of particular relevance to understory vegetation photosynthesis, snowpack energetics and soil temperature dynamics. Boreal forest canopy transmissivity needs to be quantified to properly reproduce land-atmosphere interactions in the circumpolar boreal biome, but its high spatiotemporal variability makes it a challenging task. The objective of this study is to characterize the spatiotemporal variability in under-canopy radiation and to evaluate the performance of various models in representing plot-scale observations. The study site is located in Montmorency Forest (47°N, 71°W), in southern Quebec, Canada. The vegetation includes mostly juvenile balsam firs, up to 6 to 8 m tall. Since January 2016, a 15-m flux tower measures the four components of radiation, as well as other relevant fluxes and meteorological variables, on a ≈10° northeast-facing slope. In summer 2016, 20 portable weather stations were mounted in a 150 m x 200 m grid around the flux tower. These stations were equipped with silicon-cell pyranometers and provided measurements of downwelling irradiance at a height of 2 m. This setup allowed us to compute irradiance transmissivity and to assess its spatiotemporal variability at the site. First, we show that the average of daily incoming energy varies tremendously across the sites, from 1 MJ/m2 to nearly 9 MJ/m2, due to large variations in canopy structure over short distances. Using a regression tree analysis, we show that transmissivity mostly depends on sun elevation, diffuse fraction of radiation, sky and sun view fraction and wind speed above canopy. We

Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution

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Nicholas Clinton

2014-08-01

Full Text Available Phenology response to climatic variables is a vital indicator for understanding changes in biosphere processes as related to possible climate change. We investigated global phenology relationships to precipitation and land surface temperature (LST at high spatial and temporal resolution for calendar years 2008–2011. We used cross-correlation between MODIS Enhanced Vegetation Index (EVI, MODIS LST and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN gridded rainfall to map phenology relationships at 1-km spatial resolution and weekly temporal resolution. We show these data to be rich in spatiotemporal information, illustrating distinct phenology patterns as a result of complex overlapping gradients of climate, ecosystem and land use/land cover. The data are consistent with broad-scale, coarse-resolution modeled ecosystem limitations to moisture, temperature and irradiance. We suggest that high-resolution phenology data are useful as both an input and complement to land use/land cover classifiers and for understanding climate change vulnerability in natural and anthropogenic landscapes.

Spatiotemporal Modeling of Community Risk

Science.gov (United States)

2016-03-01

Ertugay, and Sebnem Duzgun, “Exploratory and Inferential Methods for Spatio-Temporal Analysis of Residential Fire Clustering in Urban Areas,” Fire ...response in communities.”26 In “Exploratory and Inferential Methods for Spatio-temporal Analysis of Residential Fire Clustering in Urban Areas,” Ceyhan...of fire resources spread across the community. Spatiotemporal modeling shows that actualized risk is dynamic and relatively patterned. Though

Annotating spatio-temporal datasets for meaningful analysis in the Web

Science.gov (United States)

Stasch, Christoph; Pebesma, Edzer; Scheider, Simon

2014-05-01

More and more environmental datasets that vary in space and time are available in the Web. This comes along with an advantage of using the data for other purposes than originally foreseen, but also with the danger that users may apply inappropriate analysis procedures due to lack of important assumptions made during the data collection process. In order to guide towards a meaningful (statistical) analysis of spatio-temporal datasets available in the Web, we have developed a Higher-Order-Logic formalism that captures some relevant assumptions in our previous work [1]. It allows to proof on meaningful spatial prediction and aggregation in a semi-automated fashion. In this poster presentation, we will present a concept for annotating spatio-temporal datasets available in the Web with concepts defined in our formalism. Therefore, we have defined a subset of the formalism as a Web Ontology Language (OWL) pattern. It allows capturing the distinction between the different spatio-temporal variable types, i.e. point patterns, fields, lattices and trajectories, that in turn determine whether a particular dataset can be interpolated or aggregated in a meaningful way using a certain procedure. The actual annotations that link spatio-temporal datasets with the concepts in the ontology pattern are provided as Linked Data. In order to allow data producers to add the annotations to their datasets, we have implemented a Web portal that uses a triple store at the backend to store the annotations and to make them available in the Linked Data cloud. Furthermore, we have implemented functions in the statistical environment R to retrieve the RDF annotations and, based on these annotations, to support a stronger typing of spatio-temporal datatypes guiding towards a meaningful analysis in R. [1] Stasch, C., Scheider, S., Pebesma, E., Kuhn, W. (2014): "Meaningful spatial prediction and aggregation", Environmental Modelling & Software, 51, 149-165.

Spatiotemporal chaos from bursting dynamics

International Nuclear Information System (INIS)

Berenstein, Igal; De Decker, Yannick

2015-01-01

In this paper, we study the emergence of spatiotemporal chaos from mixed-mode oscillations, by using an extended Oregonator model. We show that bursting dynamics consisting of fast/slow mixed mode oscillations along a single attractor can lead to spatiotemporal chaotic dynamics, although the spatially homogeneous solution is itself non-chaotic. This behavior is observed far from the Hopf bifurcation and takes the form of a spatiotemporal intermittency where the system locally alternates between the fast and the slow phases of the mixed mode oscillations. We expect this form of spatiotemporal chaos to be generic for models in which one or several slow variables are coupled to activator-inhibitor type of oscillators

Elimination of spiral waves and spatiotemporal chaos by the pulse with a specific spatiotemporal configuration

International Nuclear Information System (INIS)

Yuan Guoyong; Yang Shiping; Wang Guangrui; Chen Shigang

2008-01-01

Spiral waves and spatiotemporal chaos are sometimes harmful and should be controlled. In this paper spiral waves and spatiotemporal chaos are successfully eliminated by the pulse with a very specific spatiotemporal configuration. The excited position D of spiral waves or spatiotemporal chaos is first recorded at an arbitrary time (t 0 ). When the system at the domain D enters a recovering state, the external pulse is injected into the domain. If the intensity and the working time of the pulse are appropriate, spiral waves and spatiotemporal chaos can finally be eliminated because counter-directional waves can be generated by the pulse. There are two advantages in the method. One is that the tip can be quickly eliminated together with the body of spiral wave, and the other is that the injected pulse may be weak and the duration can be very short so that the original system is nearly not affected, which is important for practical applications

Tensor-based spatiotemporal saliency detection

Science.gov (United States)

Dou, Hao; Li, Bin; Deng, Qianqian; Zhang, LiRui; Pan, Zhihong; Tian, Jinwen

2018-03-01

This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos. First, we construct the tensor representation of video frames. Then, the spatiotemporal saliency can be directly computed by the tensor distance between different tensors, which can preserve the complete temporal and spatial structure information of object in the spatiotemporal domain. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods.

The propagation of varied timescale perturbations in landscapes

Science.gov (United States)

Bingham, N.; Johnson, K. N.; Bookhagen, B.; Chadwick, O.

2016-12-01

The classic assumption of steady-state landscapes greatly simplifies models of earth-surface processes. Theoretically, steady-state denotes time independence, but in real landscapes steady-state requires a timescale over which to assume (or document) no change. In the past, poor spatiotemporal resolution of eroding landscapes necessitated that shorter timescale perturbations be ignored in favor of regional formulations of rock uplift = erosion, 105, 6 years. Now, novel techniques and technologies provide an opportunity to define local landscape response to various timescales of perturbations; thus, allowing us to consider multiple steady-states on adjacent watersheds or even along a single watershed. This study seeks to identify the physical propagation of varied timescale perturbations in landscapes in order to provide an updated geomorphic context for interpreting critical zone processes. At our study site - Santa Cruz Island (SCI), CA - perturbations include sea level and climate fluctuations over 105 years coupled with pulses of overgrazing and extreme storm events during the last 200 years. Comprehensive knickpoint location maps and dated marine and fill terraces tighten the spatiotemporal constraints on erosion for SCI. In addition, the island hosts a wide range of lithologies, allowing us to compare lithologic effects on landscape response to perturbations. Our study uses lidar point clouds and high resolution (0.25 and 1 m) digital elevation model analysis to segment landscapes by the degree of their response to perturbations. Landscape response is measured by increases in topographic roughness. We ascertain roughness by analyzing the changes in different terrain attributes on multiple spatial scales: catchment, sub-catchments and individual hillslopes. Terrain attributes utilized include slope, curvature, local relief, flowpath length and contributing catchment area. Statistical analysis of these properties indicates narrower ranges in values for regions

Spatio-temporal reasoning and decision support tools

OpenAIRE

Renso, Chiara; Wachowicz, Monica

2014-01-01

Currently, mobility data is revolutionizing the traditional fields of spatio-temporal reasoning and decision making analysis, not only to scale-up to the large and growing data volumes, but also to address complex questions related to change, trends, duration, and evolution. In mobility data, space and time are inextricably linked, since humans, robots and systems that dynamically act, and interact within social networks, are embedded in space, and any change is often the result of actions an...

Spatio-temporal flow maps for visualizing movement and contact patterns

Directory of Open Access Journals (Sweden)

Bing Ni

2017-03-01

Full Text Available The advanced telecom technologies and massive volumes of intelligent mobile phone users have yielded a huge amount of real-time data of people’s all-in-one telecommunication records, which we call telco big data. With telco data and the domain knowledge of an urban city, we are now able to analyze the movement and contact patterns of humans in an unprecedented scale. Flow map is widely used to display the movements of humans from one single source to multiple destinations by representing locations as nodes and movements as edges. However, it fails the task of visualizing both movement and contact data. In addition, analysts often need to compare and examine the patterns side by side, and do various quantitative analysis. In this work, we propose a novel spatio-temporal flow map layout to visualize when and where people from different locations move into the same places and make contact. We also propose integrating the spatiotemporal flow maps into existing spatiotemporal visualization techniques to form a suite of techniques for visualizing the movement and contact patterns. We report a potential application the proposed techniques can be applied to. The results show that our design and techniques properly unveil hidden information, while analysis can be achieved efficiently. Keywords: Spatio-temporal data, Flow map, Urban mobility

Decision-making by a soaring bird: time, energy and risk considerations at different spatio-temporal scales

Science.gov (United States)

Fluhr, Julie; Horvitz, Nir; Sarrazin, François; Hatzofe, Ohad

2016-01-01

Natural selection theory suggests that mobile animals trade off time, energy and risk costs with food, safety and other pay-offs obtained by movement. We examined how birds make movement decisions by integrating aspects of flight biomechanics, movement ecology and behaviour in a hierarchical framework investigating flight track variation across several spatio-temporal scales. Using extensive global positioning system and accelerometer data from Eurasian griffon vultures (Gyps fulvus) in Israel and France, we examined soaring–gliding decision-making by comparing inbound versus outbound flights (to or from a central roost, respectively), and these (and other) home-range foraging movements (up to 300 km) versus long-range movements (longer than 300 km). We found that long-range movements and inbound flights have similar features compared with their counterparts: individuals reduced journey time by performing more efficient soaring–gliding flight, reduced energy expenditure by flapping less and were more risk-prone by gliding more steeply between thermals. Age, breeding status, wind conditions and flight altitude (but not sex) affected time and energy prioritization during flights. We therefore suggest that individuals facing time, energy and risk trade-offs during movements make similar decisions across a broad range of ecological contexts and spatial scales, presumably owing to similarity in the uncertainty about movement outcomes. This article is part of the themed issue ‘Moving in a moving medium: new perspectives on flight’. PMID:27528787

A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States.

Science.gov (United States)

Beckerman, Bernardo S; Jerrett, Michael; Serre, Marc; Martin, Randall V; Lee, Seung-Jae; van Donkelaar, Aaron; Ross, Zev; Su, Jason; Burnett, Richard T

2013-07-02

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

Fine-scale hydrodynamics influence the spatio-temporal distribution of harbour porpoises at a coastal hotspot

Science.gov (United States)

Jones, A. R.; Hosegood, P.; Wynn, R. B.; De Boer, M. N.; Butler-Cowdry, S.; Embling, C. B.

2014-11-01

The coastal Runnelstone Reef, off southwest Cornwall (UK), is characterised by complex topography and strong tidal flows and is a known high-density site for harbour porpoise (Phocoena phocoena); a European protected species. Using a multidisciplinary dataset including: porpoise sightings from a multi-year land-based survey, Acoustic Doppler Current Profiling (ADCP), vertical profiling of water properties and high-resolution bathymetry; we investigate how interactions between tidal flow and topography drive the fine-scale porpoise spatio-temporal distribution at the site. Porpoise sightings were distributed non-uniformly within the survey area with highest sighting density recorded in areas with steep slopes and moderate depths. Greater numbers of sightings were recorded during strong westward (ebbing) tidal flows compared to strong eastward (flooding) flows and slack water periods. ADCP and Conductivity Temperature Depth (CTD) data identified fine-scale hydrodynamic features, associated with cross-reef tidal flows in the sections of the survey area with the highest recorded densities of porpoises. We observed layered, vertically sheared flows that were susceptible to the generation of turbulence by shear instability. Additionally, the intense, oscillatory near surface currents led to hydraulically controlled flow that transitioned from subcritical to supercritical conditions; indicating that highly turbulent and energetic hydraulic jumps were generated along the eastern and western slopes of the reef. The depression and release of isopycnals in the lee of the reef during cross-reef flows revealed that the flow released lee waves during upslope currents at specific phases of the tidal cycle when the highest sighting rates were recorded. The results of this unique, fine-scale field study provide new insights into specific hydrodynamic features, produced through tidal forcing, that may be important for creating predictable foraging opportunities for porpoises at a

Spatiotemporally enhancing time-series DMSP/OLS nighttime light imagery for assessing large-scale urban dynamics

Science.gov (United States)

Xie, Yanhua; Weng, Qihao

2017-06-01

Accurate, up-to-date, and consistent information of urban extents is vital for numerous applications central to urban planning, ecosystem management, and environmental assessment and monitoring. However, current large-scale urban extent products are not uniform with respect to definition, spatial resolution, temporal frequency, and thematic representation. This study aimed to enhance, spatiotemporally, time-series DMSP/OLS nighttime light (NTL) data for detecting large-scale urban changes. The enhanced NTL time series from 1992 to 2013 were firstly generated by implementing global inter-calibration, vegetation-based spatial adjustment, and urban archetype-based temporal modification. The dataset was then used for updating and backdating urban changes for the contiguous U.S.A. (CONUS) and China by using the Object-based Urban Thresholding method (i.e., NTL-OUT method, Xie and Weng, 2016b). The results showed that the updated urban extents were reasonably accurate, with city-scale RMSE (root mean square error) of 27 km2 and Kappa of 0.65 for CONUS, and 55 km2 and 0.59 for China, respectively. The backdated urban extents yielded similar accuracy, with RMSE of 23 km2 and Kappa of 0.63 in CONUS, while 60 km2 and 0.60 in China. The accuracy assessment further revealed that the spatial enhancement greatly improved the accuracy of urban updating and backdating by significantly reducing RMSE and slightly increasing Kappa values. The temporal enhancement also reduced RMSE, and improved the spatial consistency between estimated and reference urban extents. Although the utilization of enhanced NTL data successfully detected urban size change, relatively low locational accuracy of the detected urban changes was observed. It is suggested that the proposed methodology would be more effective for updating and backdating global urban maps if further fusion of NTL data with higher spatial resolution imagery was implemented.

Evaluation of high resolution spatio-temporal precipitation extremes from a stochastic weather generator

DEFF Research Database (Denmark)

Sørup, Hjalte Jomo Danielsen; Christensen, O. B.; Arnbjerg-Nielsen, Karsten

gauges in the model area. The spatio-temporal performance of the model with respect to precipitation extremes is evaluated in the points of a 2x2 km regular grid covering the full model area. The model satisfactorily reproduces the extreme behaviour of the observed precipitation with respect to event...... intensity levels and unconditional spatial correlation when evaluated using an event based ranking approach at point scale and an advanced spatio-temporal coupling of extreme events. Prospectively the model can be used as a tool to evaluate the impact of climate change without relying onprecipitation output......Spatio-temporal rainfall is modelled for the North-Eastern part of Zealand (Denmark) using the Spatio-Temporal Neyman-Scott Rectangular Pulses model as implemented in the RainSim software. Hourly precipitation series for fitting the model are obtained from a dense network of tipping bucket rain...

Spatio-temporal precipitation climatology over complex terrain using a censored additive regression model.

Science.gov (United States)

Stauffer, Reto; Mayr, Georg J; Messner, Jakob W; Umlauf, Nikolaus; Zeileis, Achim

2017-06-15

Flexible spatio-temporal models are widely used to create reliable and accurate estimates for precipitation climatologies. Most models are based on square root transformed monthly or annual means, where a normal distribution seems to be appropriate. This assumption becomes invalid on a daily time scale as the observations involve large fractions of zero observations and are limited to non-negative values. We develop a novel spatio-temporal model to estimate the full climatological distribution of precipitation on a daily time scale over complex terrain using a left-censored normal distribution. The results demonstrate that the new method is able to account for the non-normal distribution and the large fraction of zero observations. The new climatology provides the full climatological distribution on a very high spatial and temporal resolution, and is competitive with, or even outperforms existing methods, even for arbitrary locations.

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.

Micro- and macro-scale self-organization in a dissipative plasma

International Nuclear Information System (INIS)

Skoric, M.M.; Sato, T.; Maluckov, A.; Jovanovic, M.S.

1998-10-01

We study a nonlinear three-wave interaction in an open dissipative model of stimulated Raman backscattering in a plasma. A hybrid kinetic-fluid scheme is proposed to include anomalous kinetic dissipation due to electron trapping and plasma wave breaking. We simulate a finite plasma with open boundaries and vary a transport parameter to examine a route to spatio-temporal complexity. An interplay between self-organization at micro (kinetic) and macro (wave/fluid) scales is revealed through quasi-periodic and intermittent evolution of dynamical variables, dissipative structures and related entropy rates. An evidence that entropy rate extrema correspond to structural transitions is found. (author)

Finding equilibrium in the spatiotemporal chaos of the complex Ginzburg-Landau equation

Science.gov (United States)

Ballard, Christopher C.; Esty, C. Clark; Egolf, David A.

2016-11-01

Equilibrium statistical mechanics allows the prediction of collective behaviors of large numbers of interacting objects from just a few system-wide properties; however, a similar theory does not exist for far-from-equilibrium systems exhibiting complex spatial and temporal behavior. We propose a method for predicting behaviors in a broad class of such systems and apply these ideas to an archetypal example, the spatiotemporal chaotic 1D complex Ginzburg-Landau equation in the defect chaos regime. Building on the ideas of Ruelle and of Cross and Hohenberg that a spatiotemporal chaotic system can be considered a collection of weakly interacting dynamical units of a characteristic size, the chaotic length scale, we identify underlying, mesoscale, chaotic units and effective interaction potentials between them. We find that the resulting equilibrium Takahashi model accurately predicts distributions of particle numbers. These results suggest the intriguing possibility that a class of far-from-equilibrium systems may be well described at coarse-grained scales by the well-established theory of equilibrium statistical mechanics.

Neurogenomic signatures of spatiotemporal memories in time-trained forager honey bees

Science.gov (United States)

Naeger, Nicholas L.; Van Nest, Byron N.; Johnson, Jennifer N.; Boyd, Sam D.; Southey, Bruce R.; Rodriguez-Zas, Sandra L.; Moore, Darrell; Robinson, Gene E.

2011-01-01

Honey bees can form distinct spatiotemporal memories that allow them to return repeatedly to different food sources at different times of day. Although it is becoming increasingly clear that different behavioral states are associated with different profiles of brain gene expression, it is not known whether this relationship extends to states that are as dynamic and specific as those associated with foraging-related spatiotemporal memories. We tested this hypothesis by training different groups of foragers from the same colony to collect sucrose solution from one of two artificial feeders; each feeder was in a different location and had sucrose available at a different time, either in the morning or afternoon. Bees from both training groups were collected at both the morning and afternoon training times to result in one set of bees that was undergoing stereotypical food anticipatory behavior and another that was inactive for each time of day. Between the two groups with the different spatiotemporal memories, microarray analysis revealed that 1329 genes were differentially expressed in the brains of honey bees. Many of these genes also varied with time of day, time of training or state of food anticipation. Some of these genes are known to be involved in a variety of biological processes, including metabolism and behavior. These results indicate that distinct spatiotemporal foraging memories in honey bees are associated with distinct neurogenomic signatures, and the decomposition of these signatures into sets of genes that are also influenced by time or activity state hints at the modular composition of this complex neurogenomic phenotype. PMID:21346126

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

Science.gov (United States)

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,pQueensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430

Causal Scale of Rotors in a Cardiac System

Science.gov (United States)

Ashikaga, Hiroshi; Prieto-Castrillo, Francisco; Kawakatsu, Mari; Dehghani, Nima

2018-04-01

Rotors of spiral waves are thought to be one of the potential mechanisms that maintain atrial fibrillation (AF). However, disappointing clinical outcomes of rotor mapping and ablation to eliminate AF raise a serious doubt on rotors as a macro-scale mechanism that causes the micro-scale behavior of individual cardiomyocytes to maintain spiral waves. In this study, we aimed to elucidate the causal relationship between rotors and spiral waves in a numerical model of cardiac excitation. To accomplish the aim, we described the system in a series of spatiotemporal scales by generating a renormalization group, and evaluated the causal architecture of the system by quantifying causal emergence. Causal emergence is an information-theoretic metric that quantifies emergence or reduction between micro- and macro-scale behaviors of a system by evaluating effective information at each scale. We found that the cardiac system with rotors has a spatiotemporal scale at which effective information peaks. A positive correlation between the number of rotors and causal emergence was observed only up to the scale of peak causation. We conclude that rotors are not the universal mechanism to maintain spiral waves at all spatiotemporal scales. This finding may account for the conflicting benefit of rotor ablation in clinical studies.

Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts

International Nuclear Information System (INIS)

Fonseca, Jimeno A.; Schlueter, Arno

2015-01-01

Highlights: • A model to describe spatiotemporal building energy demand patterns was developed. • The model integrates existing methods in urban and energy planning domains. • The model is useful to analyze energy efficiency strategies in neighborhoods. • Applicability in educational, urban and energy planning practices was found. - Abstract: We introduce an integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts. The model addresses the need for a comprehensive method to identify present and potential states of building energy consumption in the context of urban transformation. The focus lies on determining the spatiotemporal variability of energy services in both standing and future buildings in the residential, commercial and industrial sectors. This detailed characterization facilitates the assessment of potential energy efficiency measures at the neighborhood and city district scales. In a novel approach we integrated existing methods in urban and energy planning domains such as spatial analysis, dynamic building energy modeling and energy mapping to provide a comprehensive, multi-scale and multi-dimensional model of analysis. The model is part of a geographic information system (GIS), which serves as a platform for the allocation and future dissemination of spatiotemporal data. The model is validated against measured data and a peer model for a city district in Switzerland. In this context, we present practical applications in the analysis of energy efficiency measures in buildings and urban zoning. We furthermore discuss potential applications in educational, urban and energy planning practices

Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products

Science.gov (United States)

Tang, Guoqiang; Behrangi, Ali; Long, Di; Li, Changming; Hong, Yang

2018-04-01

Rain gauge observations are commonly used to evaluate the quality of satellite precipitation products. However, the inherent difference between point-scale gauge measurements and areal satellite precipitation, i.e. a point of space in time accumulation v.s. a snapshot of time in space aggregation, has an important effect on the accuracy and precision of qualitative and quantitative evaluation results. This study aims to quantify the uncertainty caused by various combinations of spatiotemporal scales (0.1°-0.8° and 1-24 h) of gauge network designs in the densely gauged and relatively flat Ganjiang River basin, South China, in order to evaluate the state-of-the-art satellite precipitation, the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG). For comparison with the dense gauge network serving as "ground truth", 500 sparse gauge networks are generated through random combinations of gauge numbers at each set of spatiotemporal scales. Results show that all sparse gauge networks persistently underestimate the performance of IMERG according to most metrics. However, the probability of detection is overestimated because hit and miss events are more likely fewer than the reference numbers derived from dense gauge networks. A nonlinear error function of spatiotemporal scales and the number of gauges in each grid pixel is developed to estimate the errors of using gauges to evaluate satellite precipitation. Coefficients of determination of the fitting are above 0.9 for most metrics. The error function can also be used to estimate the required minimum number of gauges in each grid pixel to meet a predefined error level. This study suggests that the actual quality of satellite precipitation products could be better than conventionally evaluated or expected, and hopefully enables non-subject-matter-expert researchers to have better understanding of the explicit uncertainties when using point-scale gauge observations to evaluate areal products.

Distribution, genetic diversity and potential spatiotemporal scale of alien gene flow in crop wild relatives of rice (Oryza spp.) in Colombia.

Science.gov (United States)

Thomas, Evert; Tovar, Eduardo; Villafañe, Carolina; Bocanegra, José Leonardo; Moreno, Rodrigo

2017-12-01

Crop wild relatives (CWRs) of rice hold important traits that can contribute to enhancing the ability of cultivated rice (Oryza sativa and O. glaberrima) to produce higher yields, cope with the effects of climate change, and resist attacks of pests and diseases, among others. However, the genetic resources of these species remain dramatically understudied, putting at risk their future availability from in situ and ex situ sources. Here we assess the distribution of genetic diversity of the four rice CWRs known to occur in Colombia (O. glumaepatula, O. alta, O. grandiglumis, and O. latifolia). Furthermore, we estimated the degree of overlap between areas with suitable habitat for cultivated and wild rice, both under current and predicted future climate conditions to assess the potential spatiotemporal scale of potential gene flow from GM rice to its CWRs. Our findings suggest that part of the observed genetic diversity and structure, at least of the most exhaustively sampled species, may be explained by their glacial and post-glacial range dynamics. Furthermore, in assessing the expected impact of climate change and the potential spatiotemporal scale of gene flow between populations of CWRs and GM rice we find significant overlap between present and future suitable areas for cultivated rice and its four CWRs. Climate change is expected to have relatively limited negative effects on the rice CWRs, with three species showing opportunities to expand their distribution ranges in the future. Given (i) the sparse presence of CWR populations in protected areas (ii) the strong suitability overlap between cultivated rice and its four CWRs; and (iii) the complexity of managing and regulating areas to prevent alien gene flow, the first priority should be to establish representative ex situ collections for all CWR species, which currently do not exist. In the absence of studies under field conditions on the scale and extent of gene flow between cultivated rice and its Colombian

Determination of crystal growth rates during rapid solidification of polycrystalline aluminum by nano-scale spatio-temporal resolution in situ transmission electron microscopy

Energy Technology Data Exchange (ETDEWEB)

Zweiacker, K., E-mail: Kai@zweiacker.org; Liu, C.; Wiezorek, J. M. K. [Department of Mechanical Engineering and Materials Science, University of Pittsburgh, 648 Benedum Hall, 3700 OHara Street, Pittsburgh, Pennsylvania 15261 (United States); McKeown, J. T.; LaGrange, T.; Reed, B. W.; Campbell, G. H. [Materials Science Division, Physical and Life Science Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94551 (United States)

2016-08-07

In situ investigations of rapid solidification in polycrystalline Al thin films were conducted using nano-scale spatio-temporal resolution dynamic transmission electron microscopy. Differences in crystal growth rates and asymmetries in melt pool development were observed as the heat extraction geometry was varied by controlling the proximity of the laser-pulse irradiation and the associated induced melt pools to the edge of the transmission electron microscopy support grid, which acts as a large heat sink. Experimental parameters have been established to maximize the reproducibility of the material response to the laser-pulse-related heating and to ensure that observations of the dynamical behavior of the metal are free from artifacts, leading to accurate interpretations and quantifiable measurements with improved precision. Interface migration rate measurements revealed solidification velocities that increased consistently from ∼1.3 m s{sup −1} to ∼2.5 m s{sup −1} during the rapid solidification process of the Al thin films. Under the influence of an additional large heat sink, increased crystal growth rates as high as 3.3 m s{sup −1} have been measured. The in situ experiments also provided evidence for development of a partially melted, two-phase region prior to the onset of rapid solidification facilitated crystal growth. Using the experimental observations and associated measurements as benchmarks, finite-element modeling based calculations of the melt pool evolution after pulsed laser irradiation have been performed to obtain estimates of the temperature evolution in the thin films.

Spatiotemporal optical solitons

International Nuclear Information System (INIS)

Malomed, Boris A; Mihalache, Dumitru; Wise, Frank; Torner, Lluis

2005-01-01

In the course of the past several years, a new level of understanding has been achieved about conditions for the existence, stability, and generation of spatiotemporal optical solitons, which are nondiffracting and nondispersing wavepackets propagating in nonlinear optical media. Experimentally, effectively two-dimensional (2D) spatiotemporal solitons that overcome diffraction in one transverse spatial dimension have been created in quadratic nonlinear media. With regard to the theory, fundamentally new features of light pulses that self-trap in one or two transverse spatial dimensions and do not spread out in time, when propagating in various optical media, were thoroughly investigated in models with various nonlinearities. Stable vorticity-carrying spatiotemporal solitons have been predicted too, in media with competing nonlinearities (quadratic-cubic or cubic-quintic). This article offers an up-to-date survey of experimental and theoretical results in this field. Both achievements and outstanding difficulties are reviewed, and open problems are highlighted. Also briefly described are recent predictions for stable 2D and 3D solitons in Bose-Einstein condensates supported by full or low-dimensional optical lattices. (review article)

Spatio-Temporal Data Exchange Standards

DEFF Research Database (Denmark)

Jensen, Christian Søndergaard; Schmidt, Albrecht

2003-01-01

We believe that research that concerns aspects of spatio-temporal data management may benefit from taking into account the various standards for spatio-temporal data formats. For example, this may contribute to rendering prototype software “open” and more readily useful. This paper thus identifies...... and briefly surveys standardization in relation to primarily the exchange and integration of spatio-temporal data. An overview of several data exchange languages is offered, along with reviews their potential for facilitating the collection of test data and the leveraging of prototypes. The standards, most...... of which are XML-based, lend themselves to the integration of prototypes into middleware architectures, e.g., as Web services....

Second-order analysis of structured inhomogeneous spatio-temporal point processes

DEFF Research Database (Denmark)

Møller, Jesper; Ghorbani, Mohammad

Statistical methodology for spatio-temporal point processes is in its infancy. We consider second-order analysis based on pair correlation functions and K-functions for first general inhomogeneous spatio-temporal point processes and second inhomogeneous spatio-temporal Cox processes. Assuming...... spatio-temporal separability of the intensity function, we clarify different meanings of second-order spatio-temporal separability. One is second-order spatio-temporal independence and relates e.g. to log-Gaussian Cox processes with an additive covariance structure of the underlying spatio......-temporal Gaussian process. Another concerns shot-noise Cox processes with a separable spatio-temporal covariance density. We propose diagnostic procedures for checking hypotheses of second-order spatio-temporal separability, which we apply on simulated and real data (the UK 2001 epidemic foot and mouth disease data)....

Spatiotemporal Distribution and Assemblages of Planktonic Fungi in the Coastal Waters of the Bohai Sea

Directory of Open Access Journals (Sweden)

Yaqiong Wang

2018-03-01

Full Text Available Fungi play a critical role in the nutrient cycling and ecological function in terrestrial and freshwater ecosystems. Yet, many ecological aspects of their counterparts in coastal ecosystems remain largely elusive. Using high-throughput sequencing, quantitative PCR, and environmental data analyses, we studied the spatiotemporal changes in the abundance and diversity of planktonic fungi and their abiotic and biotic interactions in the coastal waters of three transects along the Bohai Sea. A total of 4362 ITS OTUs were identified and more than 60% of which were unclassified Fungi. Of the classified OTUs three major fungal phyla, Ascomycota, Basidiomycota, and Chytridiomycota were predominant with episodic low dominance phyla Cryptomycota and Mucoromycota (Mortierellales. The estimated average Fungi-specific 18S rRNA gene qPCR abundances varied within 4.28 × 106 and 1.13 × 107copies/L with significantly (P < 0.05 different abundances among the transects suggesting potential influence of the different riverine inputs. The spatiotemporal changes in the OTU abundance of Ascomycota and Basidiomycota phyla coincided significantly (P < 0.05 with nutrients traced to riverine inputs and phytoplankton detritus. Among the eight major fungal orders, the abundance of Hypocreales varied significantly (P < 0.01 across months while Capnodiales, Pleosporales, Eurotiales, and Sporidiobolales varied significantly (P < 0.05 across transects. In addition, our results likely suggest a tripartite interaction model for the association within members of Cryptomycota (hyperparasites, Chytridiomycota (both parasites and saprotrophs, and phytoplankton in the coastal waters. The fungal network featured several hubs and keystone OTUs besides the display of cooperative and competitive relationship within OTUs. These results support the notion that planktonic fungi, hitherto mostly undescribed, play diverse ecological roles in marine habitats and further outline niche processes

Spatiotemporal Dynamics of Scrub Typhus Transmission in Mainland China, 2006-2014.

Science.gov (United States)

Wu, Yi-Cheng; Qian, Quan; Soares Magalhaes, Ricardo J; Han, Zhi-Hai; Hu, Wen-Biao; Haque, Ubydul; Weppelmann, Thomas A; Wang, Yong; Liu, Yun-Xi; Li, Xin-Lou; Sun, Hai-Long; Sun, Yan-Song; Clements, Archie C A; Li, Shen-Long; Zhang, Wen-Yi

2016-08-01

Scrub typhus is endemic in the Asia-Pacific region including China, and the number of reported cases has increased dramatically in the past decade. However, the spatial-temporal dynamics and the potential risk factors in transmission of scrub typhus in mainland China have yet to be characterized. This study aims to explore the spatiotemporal dynamics of reported scrub typhus cases in mainland China between January 2006 and December 2014, to detect the location of high risk spatiotemporal clusters of scrub typhus cases, and identify the potential risk factors affecting the re-emergence of the disease. Monthly cases of scrub typhus reported at the county level between 2006 and 2014 were obtained from the Chinese Center for Diseases Control and Prevention. Time-series analyses, spatiotemporal cluster analyses, and spatial scan statistics were used to explore the characteristics of the scrub typhus incidence. To explore the association between scrub typhus incidence and environmental variables panel Poisson regression analysis was conducted. During the time period between 2006 and 2014 a total of 54,558 scrub typhus cases were reported in mainland China, which grew exponentially. The majority of cases were reported each year between July and November, with peak incidence during October every year. The spatiotemporal dynamics of scrub typhus varied over the study period with high-risk clusters identified in southwest, southern, and middle-eastern part of China. Scrub typhus incidence was positively correlated with the percentage of shrub and meteorological variables including temperature and precipitation. The results of this study demonstrate areas in China that could be targeted with public health interventions to mitigate the growing threat of scrub typhus in the country.

Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series

Science.gov (United States)

Lu, Meng; Pebesma, Edzer; Sanchez, Alber; Verbesselt, Jan

2016-07-01

Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis.

Aspects of second-order analysis of structured inhomogeneous spatio-temporal processes

DEFF Research Database (Denmark)

Møller, Jesper; Ghorbani, Mohammad

2012-01-01

Statistical methodology for spatio-temporal point processes is in its infancy. We consider second-order analysis based on pair correlation functions and K-functions for general inhomogeneous spatio-temporal point processes and for inhomogeneous spatio-temporal Cox processes. Assuming spatio......-temporal separability of the intensity function, we clarify different meanings of second-order spatio-temporal separability. One is second-order spatio-temporal independence and relates to log-Gaussian Cox processes with an additive covariance structure of the underlying spatio-temporal Gaussian process. Another...... concerns shot-noise Cox processes with a separable spatio-temporal covariance density. We propose diagnostic procedures for checking hypotheses of second-order spatio-temporal separability, which we apply on simulated and real data....

Robust Stability of Scaled-Four-Channel Teleoperation with Internet Time-Varying Delays

Directory of Open Access Journals (Sweden)

Emma Delgado

2016-04-01

Full Text Available We describe the application of a generic stability framework for a teleoperation system under time-varying delay conditions, as addressed in a previous work, to a scaled-four-channel (γ-4C control scheme. Described is how varying delays are dealt with by means of dynamic encapsulation, giving rise to mu-test conditions for robust stability and offering an appealing frequency technique to deal with the stability robustness of the architecture. We discuss ideal transparency problems and we adapt classical solutions so that controllers are proper, without single or double differentiators, and thus avoid the negative effects of noise. The control scheme was fine-tuned and tested for complete stability to zero of the whole state, while seeking a practical solution to the trade-off between stability and transparency in the Internet-based teleoperation. These ideas were tested on an Internet-based application with two Omni devices at remote laboratory locations via simulations and real remote experiments that achieved robust stability, while performing well in terms of position synchronization and force transparency.

Robust Stability of Scaled-Four-Channel Teleoperation with Internet Time-Varying Delays.

Science.gov (United States)

Delgado, Emma; Barreiro, Antonio; Falcón, Pablo; Díaz-Cacho, Miguel

2016-04-26

We describe the application of a generic stability framework for a teleoperation system under time-varying delay conditions, as addressed in a previous work, to a scaled-four-channel (γ-4C) control scheme. Described is how varying delays are dealt with by means of dynamic encapsulation, giving rise to mu-test conditions for robust stability and offering an appealing frequency technique to deal with the stability robustness of the architecture. We discuss ideal transparency problems and we adapt classical solutions so that controllers are proper, without single or double differentiators, and thus avoid the negative effects of noise. The control scheme was fine-tuned and tested for complete stability to zero of the whole state, while seeking a practical solution to the trade-off between stability and transparency in the Internet-based teleoperation. These ideas were tested on an Internet-based application with two Omni devices at remote laboratory locations via simulations and real remote experiments that achieved robust stability, while performing well in terms of position synchronization and force transparency.

SPATIOTEMPORAL CONTRAST SENSITIVITY OF EARLY VISION

NARCIS (Netherlands)

Hateren, J.H. van

Based on the spatial and temporal statistics of natural images, a theory is developed that specifies spatiotemporal filters that maximize the flow of information through noisy channels of limited dynamic range. Sensitivities resulting from these spatiotemporal filters are very similar to the human

Investigating fine-scale spatio-temporal predator-prey patterns in dynamic marine ecosystems: a functional data analysis approach

NARCIS (Netherlands)

Embling, C.B.; Illian, J.; Armstrong, E.; van der Kooij, J.; Sharples, J.; Camphuysen, K.C.J.; Scott, B.E.

2012-01-01

1. Spatial management of marine ecosystems requires detailed knowledge of spatio-temporal mechanisms linking physical and biological processes. Tidal currents, the main driver of ecosystem dynamics in temperate coastal ecosystems, influence predator foraging ecology by affecting prey distribution

Scale effects on spatially varying relationships between urban landscape patterns and water quality.

Science.gov (United States)

Sun, Yanwei; Guo, Qinghai; Liu, Jian; Wang, Run

2014-08-01

Scientific interpretation of the relationships between urban landscape patterns and water quality is important for sustainable urban planning and watershed environmental protection. This study applied the ordinary least squares regression model and the geographically weighted regression model to examine the spatially varying relationships between 12 explanatory variables (including three topographical factors, four land use parameters, and five landscape metrics) and 15 water quality indicators in watersheds of Yundang Lake, Maluan Bay, and Xinglin Bay with varying levels of urbanization in Xiamen City, China. A local and global investigation was carried out at the watershed-level, with 50 and 200 m riparian buffer scales. This study found that topographical features and landscape metrics are the dominant factors of water quality, while land uses are too weak to be considered as a strong influential factor on water quality. Such statistical results may be related with the characteristics of land use compositions in our study area. Water quality variations in the 50 m buffer were dominated by topographical variables. The impact of landscape metrics on water quality gradually strengthen with expanding buffer zones. The strongest relationships are obtained in entire watersheds, rather than in 50 and 200 m buffer zones. Spatially varying relationships and effective buffer zones were verified in this study. Spatially varying relationships between explanatory variables and water quality parameters are more diversified and complex in less urbanized areas than in highly urbanized areas. This study hypothesizes that all these varying relationships may be attributed to the heterogeneity of landscape patterns in different urban regions. Adjustment of landscape patterns in an entire watershed should be the key measure to successfully improving urban lake water quality.

Spatio-temporal models of mental processes from fMRI.

Science.gov (United States)

Janoos, Firdaus; Machiraju, Raghu; Singh, Shantanu; Morocz, Istvan Ákos

2011-07-15

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Conventional analysis methods focus on the spatial dimension of the data discarding the information about brain function contained in the temporal dimension. This paper presents a fully spatio-temporal multivariate analysis method using a state-space model (SSM) for brain function that yields not only spatial maps of activity but also its temporal structure along with spatially varying estimates of the hemodynamic response. Efficient algorithms for estimating the parameters along with quantitative validations are given. A novel low-dimensional feature-space for representing the data, based on a formal definition of functional similarity, is derived. Quantitative validation of the model and the estimation algorithms is provided with a simulation study. Using a real fMRI study for mental arithmetic, the ability of this neurophysiologically inspired model to represent the spatio-temporal information corresponding to mental processes is demonstrated. Moreover, by comparing the models across multiple subjects, natural patterns in mental processes organized according to different mental abilities are revealed. Copyright © 2011 Elsevier Inc. All rights reserved.

A model for optimizing file access patterns using spatio-temporal parallelism

Energy Technology Data Exchange (ETDEWEB)

Boonthanome, Nouanesengsy [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Patchett, John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Geveci, Berk [Kitware Inc., Clifton Park, NY (United States); Ahrens, James [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Bauer, Andy [Kitware Inc., Clifton Park, NY (United States); Chaudhary, Aashish [Kitware Inc., Clifton Park, NY (United States); Miller, Ross G. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Shipman, Galen M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2013-01-01

For many years now, I/O read time has been recognized as the primary bottleneck for parallel visualization and analysis of large-scale data. In this paper, we introduce a model that can estimate the read time for a file stored in a parallel filesystem when given the file access pattern. Read times ultimately depend on how the file is stored and the access pattern used to read the file. The file access pattern will be dictated by the type of parallel decomposition used. We employ spatio-temporal parallelism, which combines both spatial and temporal parallelism, to provide greater flexibility to possible file access patterns. Using our model, we were able to configure the spatio-temporal parallelism to design optimized read access patterns that resulted in a speedup factor of approximately 400 over traditional file access patterns.

World Spatiotemporal Analytics and Mapping Project (wstamp): Discovering, Exploring, and Mapping Spatiotemporal Patterns across the World's Largest Open Soruce Data Sets

Science.gov (United States)

Stewart, R.; Piburn, J.; Sorokine, A.; Myers, A.; Moehl, J.; White, D.

2015-07-01

The application of spatiotemporal (ST) analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings.

Spatiotemporal Characteristics, Determinants and Scenario Analysis of CO2 Emissions in China Using Provincial Panel Data.

Science.gov (United States)

Wang, Shaojian; Fang, Chuanglin; Li, Guangdong

2015-01-01

This paper empirically investigated the spatiotemporal variations, influencing factors and future emission trends of China's CO2 emissions based on a provincial panel data set. A series of panel econometric models were used taking the period 1995-2011 into consideration. The results indicated that CO2 emissions in China increased over time, and were characterized by noticeable regional discrepancies; in addition, CO2 emissions also exhibited properties of spatial dependence and convergence. Factors such as population scale, economic level and urbanization level exerted a positive influence on CO2 emissions. Conversely, energy intensity was identified as having a negative influence on CO2 emissions. In addition, the significance of the relationship between CO2 emissions and the four variables varied across the provinces based on their scale of economic development. Scenario simulations further showed that the scenario of middle economic growth, middle population increase, low urbanization growth, and high technology improvement (here referred to as Scenario BTU), constitutes the best development model for China to realize the future sustainable development. Based on these empirical findings, we also provide a number of policy recommendations with respect to the future mitigation of CO2 emissions.

Spatiotemporal multiscaling analysis of impurity transport in plasma turbulence using proper orthogonal decomposition

International Nuclear Information System (INIS)

Futatani, S.; Benkadda, S.; Del-Castillo-Negrete, D.

2009-01-01

The spatiotemporal multiscale dynamics of the turbulent transport of impurities is studied in the context of the collisional drift wave turbulence. Two turbulence regimes are considered: a quasihydrodynamic regime and a quasiadiabatic regime. The impurity is assumed to be a passive scalar advected by the corresponding ExB turbulent flow in the presence of diffusion. Two mixing scenarios are studied: a freely decaying case, and a forced case in which the scalar is forced by an externally imposed gradient. The results of the direct numerical simulations are analyzed using proper orthogonal decomposition (POD) techniques. The multiscale analysis is based on a space-time separable POD of the impurity field. The low rank spatial POD eigenfunctions capture the large scale coherent structures and the high rank eigenfunctions capture the small scale fluctuations. The temporal evolution at each scale is dictated by the corresponding temporal POD eigenfunctions. Contrary to the decaying case in which the POD spectrum decays fast, the spectrum in the forced case is relatively flat. The most striking difference between these two mixing scenarios is in the temporal dynamics of the small scale structures. In the decaying case the POD reveals the presence of 'bursty' dynamics in which successively small (high POD rank) scales are intermittently activated during the mixing process. On the other hand, in the forced simulations the temporal dynamics exhibits stationary fluctuations. Spatial intermittency or 'patchiness' in the mixing process characterizes the distribution of the passive tracer in the decaying quasihydrodynamic regime. In particular, in this case the probability distribution function of the low rank POD spatial reconstruction error is non-Gaussian. The spatiotemporal POD scales exhibit a diffusive-type scaling in the quasiadiabatic regime. However, this scaling seems to be absent in the quasihydrodynamic regime that shows no scaling (in the decaying case) or two

Climate change and human infectious diseases: A synthesis of research findings from global and spatio-temporal perspectives.

Science.gov (United States)

Liang, Lu; Gong, Peng

2017-06-01

The life cycles and transmission of most infectious agents are inextricably linked with climate. In spite of a growing level of interest and progress in determining climate change effects on infectious disease, the debate on the potential health outcomes remains polarizing, which is partly attributable to the varying effects of climate change, different types of pathogen-host systems, and spatio-temporal scales. We summarize the published evidence and show that over the past few decades, the reported negative or uncertain responses of infectious diseases to climate change has been growing. A feature of the research tendency is the focus on temperature and insect-borne diseases at the local and decadal scale. Geographically, regions experiencing higher temperature anomalies have been given more research attention; unfortunately, the Earth's most vulnerable regions to climate variability and extreme events have been less studied. From local to global scales, agreements on the response of infectious diseases to climate change tend to converge. So far, an abundance of findings have been based on statistical methods, with the number of mechanistic studies slowly growing. Research gaps and trends identified in this study should be addressed in the future. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Directory of Open Access Journals (Sweden)

Liang Wu

2017-11-01

Full Text Available Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation.

Amplitude death and spatiotemporal bifurcations in nonlocally delay-coupled oscillators

International Nuclear Information System (INIS)

Guo, Yuxiao; Niu, Ben

2015-01-01

Amplitude death and spatiotemporal oscillations are remarkable patterns in coupled systems. We consider a ring of n identical oscillators with distance-dependent couplings and time delay. The amplitude death region is the intersection of three stable regions. Employing the method of multiple scales and normal form theory, the stability and criticality of spatiotemporal oscillations are determined. Around the amplitude death boundary there exist one branch of synchronized oscillations, n − 3 branches of co-existing phase-locked oscillations, n branches of mirror-reflecting oscillations, n branches of standing-wave oscillations, one branch of quasiperiodic oscillations and two branches of co-existing synchronized oscillations. It is proved that amplitude death is robust to small inhomogeneity of couplings, and the stability of synchronized or phase-locked oscillations inherits that of the individual decoupled oscillator. For the arbitrary form of coupling functions, some general results are also obtained for the thermodynamic limit. Finally, two examples are given to support the main results. (paper)

Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015

Directory of Open Access Journals (Sweden)

Ling Hu

2018-03-01

Full Text Available Gross primary productivity (GPP is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL, and Fraction of Photosynthetically Active Radiation (FPAR, to investigate the dominant factor for GPP dynamics. Results demonstrated that (1 the spatial distribution of accumulated GPP (AG in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2 interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3 interannual changes of accumulated GPP in the growing season (AGG at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (p < 0.05 during the period of 1982–2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4 an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas.

The impact of soil moisture extremes and their spatiotemporal variability on Zambian maize yields

Science.gov (United States)

Zhao, Y.; Estes, L. D.; Vergopolan, N.

2017-12-01

Food security in sub-Saharan Africa is highly sensitive to climate variability. While it is well understood that extreme heat has substantial negative impacts on crop yield, the impacts of precipitation extremes, particularly over large spatial extents, are harder to quantify. There are three primary reasons for this difficulty, which are (1) lack of high quality, high resolution precipitation data, (2) rainfall data provide incomplete information on plant water availability, the variable that most directly affects crop performance, and (3) the type of rainfall extreme that most affects crop yields varies throughout the crop development stage. With respect to the first reason, the spatial and temporal variation of precipitation is much greater than that of temperature, yet the spatial resolution of rainfall data is typically even coarser than it is for temperature, particularly within Africa. Even if there were high-resolution rainfall data, the amount of water available to crops also depends on other physical factors that affect evapotranspiration, which are strongly influenced by heterogeneity in the land surface related to topography, soil properties, and land cover. In this context, soil moisture provides a better measure of crop water availability than rainfall. Furthermore, soil moisture has significantly different influences on crop yield depending on the crop's growth stage. The goal of this study is to understand how the spatiotemporal scales of soil moisture extremes interact with crops, more specifically, the timing and the spatial scales of extreme events like droughts and flooding. In this study, we simulate daily-1km soil moisture using HydroBlocks - a physically based land surface model - and compare it with precipitation and remote sensing derived maize yields between 2000 and 2016 in Zambia. We use a novel combination of the SCYM (scalable satellite-based yield mapper) method with DSSAT crop model, which is a mechanistic model responsive to water

Spatio-Temporal Data Construction

Directory of Open Access Journals (Sweden)

Hai Ha Le

2013-08-01

Full Text Available On the route to a spatio-temporal geoscience information system, an appropriate data model for geo-objects in space and time has been developed. In this model, geo-objects are represented as sequences of geometries and properties with continuous evolution in each time interval. Because geomodeling software systems usually model objects at specific time instances, we want to interpolate the geometry and properties from two models of an object with only geometrical constraints (no physical or mechanical constraints. This process is called spatio-temporal data construction or morphological interpolation of intermediate geometries. This paper is strictly related to shape morphing, shape deformation, cross-parameterization and compatible remeshing and is only concerned with geological surfaces. In this study, two main sub-solutions construct compatible meshes and find trajectories in which vertices of the mesh evolve. This research aims to find an algorithm to construct spatio-temporal data with some constraints from the geosciences, such as cutting surfaces by faulting or fracturing phenomena and evolving boundaries attached to other surfaces. Another goal of this research is the implementation of the algorithm in a software product, namely a gOcad plug-in. The four main procedures of the algorithm are cutting the surfaces, setting up constraints, partitioning and calculating the parameterizations and trajectories. The software has been tested to construct data for a salt dome and other surfaces in regard to the geological processes of faulting, deposition and erosion. The result of this research is an algorithm and software for the construction of spatio-temporal data.

WORLD SPATIOTEMPORAL ANALYTICS AND MAPPING PROJECT (WSTAMP: DISCOVERING, EXPLORING, AND MAPPING SPATIOTEMPORAL PATTERNS ACROSS THE WORLD’S LARGEST OPEN SORUCE DATA SETS

Directory of Open Access Journals (Sweden)

R. Stewart

2015-07-01

Full Text Available The application of spatiotemporal (ST analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1 development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2 a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings.

Spatiotemporal Stochastic Resonance:Theory and Experiment

Science.gov (United States)

Peter, Jung

1996-03-01

The amplification of weak periodic signals in bistable or excitable systems via stochastic resonance has been studied intensively over the last years. We are going one step further and ask: Can noise enhance spatiotemporal patterns in excitable media and can this effect be observed in nature? To this end, we are looking at large, two dimensional arrays of coupled excitable elements. Due to the coupling, excitation can propagate through the array in form of nonlinear waves. We observe target waves, rotating spiral waves and other wave forms. If the coupling between the elements is below a critical threshold, any excitational pattern will die out in the absence of noise. Below this threshold, large scale rotating spiral waves - as they are observed above threshold - can be maintained by a proper level of the noise[1]. Furthermore, their geometric features, such as the curvature can be controlled by the homogeneous noise level[2]. If the noise level is too large, break up of spiral waves and collisions with spontaneously nucleated waves yields spiral turbulence. Driving our array with a spatiotemporal pattern, e.g. a rotating spiral wave, we show that for weak coupling the excitational response of the array shows stochastic resonance - an effect we have termed spatiotemporal stochastic resonance. In the last part of the talk I'll make contact with calcium waves, observed in astrocyte cultures and hippocampus slices[3]. A. Cornell-Bell and collaborators[3] have pointed out the role of calcium waves for long-range glial signaling. We demonstrate the similarity of calcium waves with nonlinear waves in noisy excitable media. The noise level in the tissue is characterized by spontaneous activity and can be controlled by applying neuro-transmitter substances[3]. Noise effects in our model are compared with the effect of neuro-transmitters on calcium waves. [1]P. Jung and G. Mayer-Kress, CHAOS 5, 458 (1995). [2]P. Jung and G. Mayer-Kress, Phys. Rev. Lett.62, 2682 (1995). [3

Patchiness of Ciliate Communities Sampled at Varying Spatial Scales along the New England Shelf.

Directory of Open Access Journals (Sweden)

Jean-David Grattepanche

Full Text Available Although protists (microbial eukaryotes provide an important link between bacteria and Metazoa in food webs, we do not yet have a clear understanding of the spatial scales on which protist diversity varies. Here, we use a combination of DNA fingerprinting (denaturant gradient gel electrophoresis or DGGE and high-throughput sequencing (HTS to assess the ciliate community in the class Spirotrichea at varying scales of 1-3 km sampled in three locations separated by at least 25 km-offshore, midshelf and inshore-along the New England shelf. Analyses of both abundant community (DGGE and the total community (HTS members reveal that: 1 ciliate communities are patchily distributed inshore (i.e. the middle station of a transect is distinct from its two neighboring stations, whereas communities are more homogeneous among samples within the midshelf and offshore stations; 2 a ciliate closely related to Pelagostrobilidium paraepacrum 'blooms' inshore and; 3 environmental factors may differentially impact the distributions of individual ciliates (i.e. OTUs rather than the community as a whole as OTUs tend to show distinct biogeographies (e.g. some OTUs are restricted to the offshore locations, some to the surface, etc.. Together, these data show the complexity underlying the spatial distributions of marine protists, and suggest that biogeography may be a property of ciliate species rather than communities.

The Voronoi spatio-temporal data structure

Science.gov (United States)

Mioc, Darka

2002-04-01

Current GIS models cannot integrate the temporal dimension of spatial data easily. Indeed, current GISs do not support incremental (local) addition and deletion of spatial objects, and they can not support the temporal evolution of spatial data. Spatio-temporal facilities would be very useful in many GIS applications: harvesting and forest planning, cadastre, urban and regional planning, and emergency planning. The spatio-temporal model that can overcome these problems is based on a topological model---the Voronoi data structure. Voronoi diagrams are irregular tessellations of space, that adapt to spatial objects and therefore they are a synthesis of raster and vector spatial data models. The main advantage of the Voronoi data structure is its local and sequential map updates, which allows us to automatically record each event and performed map updates within the system. These map updates are executed through map construction commands that are composed of atomic actions (geometric algorithms for addition, deletion, and motion of spatial objects) on the dynamic Voronoi data structure. The formalization of map commands led to the development of a spatial language comprising a set of atomic operations or constructs on spatial primitives (points and lines), powerful enough to define the complex operations. This resulted in a new formal model for spatio-temporal change representation, where each update is uniquely characterized by the numbers of newly created and inactivated Voronoi regions. This is used for the extension of the model towards the hierarchical Voronoi data structure. In this model, spatio-temporal changes induced by map updates are preserved in a hierarchical data structure that combines events and corresponding changes in topology. This hierarchical Voronoi data structure has an implicit time ordering of events visible through changes in topology, and it is equivalent to an event structure that can support temporal data without precise temporal

Spatiotemporal Wave Patterns: Information Dynamics

Energy Technology Data Exchange (ETDEWEB)

Mikhail Rabinovich; Lev Tsimring

2006-01-20

Pattern formation has traditionally been studied in non-equilibrium physics from the viewpoint of describing the basic structures and their interactions. While this is still an important area of research, the emphasis in the last few years has shifted towards analysis of specific properties of patterns in various complex media. For example, diverse and unexpected phenomena occur in neuro-like media that are characterized by highly non-trivial local dynamics. We carried out an active research program on analysis of spatio-temporal patterns in various physical systems (convection, oscillating fluid layer, soap film), as well as in neuro-like media, with an emphasis on informational aspects of the dynamics. Nonlinear nonequilibrium media and their discrete analogs have a unique ability to represent, memorize, and process the information contained in spatio-temporal patterns. Recent neurophysiological experiments demonstrated a certain universality of spatio-temporal representation of information by neural ensembles. Information processing is also revealed in the spatio-temporal dynamics of cellular patterns in nonequilibrium media. It is extremely important for many applications to study the informational aspects of these dynamics, including the origins and mechanisms of information generation, propagation and storage. Some of our results are: the discovery of self-organization of periodically oscillatory patterns in chaotic heterogeneous media; the analysis of the propagation of the information along a chaotic media as function of the entropy of the signal; the analysis of wave propagation in discrete non-equilibrium media with autocatalytic properties, which simulates the calcium dynamics in cellular membranes. Based on biological experiments we suggest the mechanism by which the spatial sensory information is transferred into the spatio-temporal code in the neural media. We also found a new mechanism of self-pinning in cellular structures and the related phenomenon

PARALLEL SPATIOTEMPORAL SPECTRAL CLUSTERING WITH MASSIVE TRAJECTORY DATA

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Y. Z. Gu

2017-09-01

Full Text Available Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining. However, the traditional spectral clustering lacks the temporal expansion on the algorithm and limited in its applicability to large-scale problems due to its high computational complexity. This paper presents a parallel spatiotemporal spectral clustering based on multiple acceleration solutions to make the algorithm more effective and efficient, the performance is proved due to the experiment carried out on the massive taxi trajectory dataset in Wuhan city, China.

Distributed Cerebral Blood Flow estimation using a spatiotemporal hemodynamic response model and a Kalman-like Filter approach

KAUST Repository

Belkhatir, Zehor

2015-11-23

This paper discusses the estimation of distributed Cerebral Blood Flow (CBF) using spatiotemporal traveling wave model. We consider a damped wave partial differential equation that describes a physiological relationship between the blood mass density and the CBF. The spatiotemporal model is reduced to a finite dimensional system using a cubic b-spline continuous Galerkin method. A Kalman Filter with Unknown Inputs without Direct Feedthrough (KF-UI-WDF) is applied on the obtained reduced differential model to estimate the source term which is the CBF scaled by a factor. Numerical results showing the performances of the adopted estimator are provided.

A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia

Directory of Open Access Journals (Sweden)

Craig Anderson

2017-02-01

Full Text Available The field of spatio-temporal modelling has witnessed a recent surge as a result of developments in computational power and increased data collection. These developments allow analysts to model the evolution of health outcomes in both space and time simultaneously. This paper models the trends in ischaemic heart disease (IHD in New South Wales, Australia over an eight-year period between 2006 and 2013. A number of spatio-temporal models are considered, and we propose a novel method for determining the goodness-of-fit for these models by outlining a spatio-temporal extension of the Moran’s I statistic. We identify an overall decrease in the rates of IHD, but note that the extent of this health improvement varies across the state. In particular, we identified a number of remote areas in the north and west of the state where the risk stayed constant or even increased slightly.

A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk

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Juan Guillermo eDiaz Ochoa

2013-01-01

Full Text Available In this study, we focus on a novel multi-scale modeling approach for spatiotemporal prediction of the distribution of substances and resulting hepatotoxicity by combining cellular models, a 2D liver model, and whole-body model. As a case study, we focused on predicting human hepatotoxicity upon treatment with acetaminophen based on in vitro toxicity data and potential inter-individual variability in gene expression and enzyme activities. By aggregating mechanistic, genome-based in silico cells to a novel 2D liver model and eventually to a whole body model, we predicted pharmacokinetic properties, metabolism, and the onset of hepatotoxicity in an in silico patient. Depending on the concentration of acetaminophen in the liver and the accumulation of toxic metabolites, cell integrity in the liver as a function of space and time as well as changes in the elimination rate of substances were estimated. We show that the variations in elimination rates also influence the distribution of acetaminophen and its metabolites in the whole body. Our results are in agreement with experimental results. What is more, the integrated model also predicted variations in drug toxicity depending on alterations of metabolic enzyme activities. Variations in enzyme activity, in turn, reflect genetic characteristics or diseases of individuals. In conclusion, this framework presents an important basis for efficiently integrating inter-individual variability data into models, paving the way for personalized or stratified predictions of drug toxicity and efficacy.

A Unified Spatiotemporal Modeling Approach for Predicting Concentrations of Multiple Air Pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Science.gov (United States)

Olives, Casey; Kim, Sun-Young; Sheppard, Lianne; Sampson, Paul D.; Szpiro, Adam A.; Oron, Assaf P.; Lindström, Johan; Vedal, Sverre; Kaufman, Joel D.

2014-01-01

Background: Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time. Objectives: We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Methods: We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants’ homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations. Results: Prediction accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV ranged from 0.23 to 0.92. Conclusions: This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies. Citation: Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, Oron AP, Lindström J, Vedal S, Kaufman JD. 2015. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the Multi

Improving Prediction of Large-scale Regime Transitions

Science.gov (United States)

Gyakum, J. R.; Roebber, P.; Bosart, L. F.; Honor, A.; Bunker, E.; Low, Y.; Hart, J.; Bliankinshtein, N.; Kolly, A.; Atallah, E.; Huang, Y.

2017-12-01

Cool season atmospheric predictability over the CONUS on subseasonal times scales (1-4 weeks) is critically dependent upon the structure, configuration, and evolution of the North Pacific jet stream (NPJ). The NPJ can be perturbed on its tropical side on synoptic time scales by recurving and transitioning tropical cyclones (TCs) and on subseasonal time scales by longitudinally varying convection associated with the Madden-Julian Oscillation (MJO). Likewise, the NPJ can be perturbed on its poleward side on synoptic time scales by midlatitude and polar disturbances that originate over the Asian continent. These midlatitude and polar disturbances can often trigger downstream Rossby wave propagation across the North Pacific, North America, and the North Atlantic. The project team is investigating the following multiscale processes and features: the spatiotemporal distribution of cyclone clustering over the Northern Hemisphere; cyclone clustering as influenced by atmospheric blocking and the phases and amplitudes of the major teleconnection indices, ENSO and the MJO; composite and case study analyses of representative cyclone clustering events to establish the governing dynamics; regime change predictability horizons associated with cyclone clustering events; Arctic air mass generation and modification; life cycles of the MJO; and poleward heat and moisture transports of subtropical air masses. A critical component of the study is weather regime classification. These classifications are defined through: the spatiotemporal clustering of surface cyclogenesis; a general circulation metric combining data at 500-hPa and the dynamic tropopause; Self Organizing Maps (SOM), constructed from dynamic tropopause and 850 hPa equivalent potential temperature data. The resultant lattice of nodes is used to categorize synoptic classes and their predictability, as well as to determine the robustness of the CFSv2 model climate relative to observations. Transition pathways between these

Analyzing Spatiotemporal Anomalies through Interactive Visualization

Directory of Open Access Journals (Sweden)

Tao Zhang

2014-06-01

Full Text Available As we move into the big data era, data grows not just in size, but also in complexity, containing a rich set of attributes, including location and time information, such as data from mobile devices (e.g., smart phones, natural disasters (e.g., earthquake and hurricane, epidemic spread, etc. We are motivated by the rising challenge and build a visualization tool for exploring generic spatiotemporal data, i.e., records containing time location information and numeric attribute values. Since the values often evolve over time and across geographic regions, we are particularly interested in detecting and analyzing the anomalous changes over time/space. Our analytic tool is based on geographic information system and is combined with spatiotemporal data mining algorithms, as well as various data visualization techniques, such as anomaly grids and anomaly bars superimposed on the map. We study how effective the tool may guide users to find potential anomalies through demonstrating and evaluating over publicly available spatiotemporal datasets. The tool for spatiotemporal anomaly analysis and visualization is useful in many domains, such as security investigation and monitoring, situation awareness, etc.

Unsupervised Learning of Spatiotemporal Features by Video Completion

OpenAIRE

Nallabolu, Adithya Reddy

2017-01-01

In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose to learn the spatiotemporal features by training a 3D convolutional neural network (CNN) using video completion as a surrogate task. Using a large collection of unlabeled videos, we train the CNN to predict the missing pixels of a spatiotemporal hole given the remaining parts of the video through minimizing per...

Comparison of Spatiotemporal Fusion Models: A Review

Directory of Open Access Journals (Sweden)

Bin Chen

2015-02-01

Full Text Available Simultaneously capturing spatial and temporal dynamics is always a challenge for the remote sensing community. Spatiotemporal fusion has gained wide interest in various applications for its superiority in integrating both fine spatial resolution and frequent temporal coverage. Though many advances have been made in spatiotemporal fusion model development and applications in the past decade, a unified comparison among existing fusion models is still limited. In this research, we classify the models into three categories: transformation-based, reconstruction-based, and learning-based models. The objective of this study is to (i compare four fusion models (STARFM, ESTARFM, ISTAFM, and SPSTFM under a one Landsat-MODIS (L-M pair prediction mode and two L-M pair prediction mode using time-series datasets from the Coleambally irrigation area and Poyang Lake wetland; (ii quantitatively assess prediction accuracy considering spatiotemporal comparability, landscape heterogeneity, and model parameter selection; and (iii discuss the advantages and disadvantages of the three categories of spatiotemporal fusion models.

Characterizing the Spatio-Temporal Pattern of Land Surface Temperature through Time Series Clustering: Based on the Latent Pattern and Morphology

Directory of Open Access Journals (Sweden)

Huimin Liu

2018-04-01

Full Text Available Land Surface Temperature (LST is a critical component to understand the impact of urbanization on the urban thermal environment. Previous studies were inclined to apply only one snapshot to analyze the pattern and dynamics of LST without considering the non-stationarity in the temporal domain, or focus on the diurnal, seasonal, and annual pattern analysis of LST which has limited support for the understanding of how LST varies with the advancing of urbanization. This paper presents a workflow to extract the spatio-temporal pattern of LST through time series clustering by focusing on the LST of Wuhan, China, from 2002 to 2017 with a 3-year time interval with 8-day MODerate-resolution Imaging Spectroradiometer (MODIS satellite image products. The Latent pattern of LST (LLST generated by non-parametric Multi-Task Gaussian Process Modeling (MTGP and the Multi-Scale Shape Index (MSSI which characterizes the morphology of LLST are coupled for pattern recognition. Specifically, spatio-temporal patterns are discovered after the extraction of spatial patterns conducted by the incorporation of k -means and the Back-Propagation neural networks (BP-Net. The spatial patterns of the 6 years form a basic understanding about the corresponding temporal variances. For spatio-temporal pattern recognition, LLSTs and MSSIs of the 6 years are regarded as geo-referenced time series. Multiple algorithms including traditional k -means with Euclidean Distance (ED, shape-based k -means with the constrained Dynamic Time Warping ( c DTW distance measure, and the Dynamic Time Warping Barycenter Averaging (DBA centroid computation method ( k - c DBA and k -shape are applied. Ten external indexes are employed to evaluate the performance of the three algorithms and reveal k - c DBA as the optimal time series clustering algorithm for our study. The study area is divided into 17 geographical time series clusters which respectively illustrate heterogeneous temporal dynamics of LST

Spatiotemporal Patterns of Urbanization in a Developed Region of Eastern Coastal China

OpenAIRE

Li, Jiadan; Deng, Jinsong; Wang, Ke; Li, Jun; Huang, Tao; Lin, Yi; Yu, Haiyan

2014-01-01

This study presents a practical methodology to monitor the spatiotemporal characteristics of urban expansion in response to rapid urbanization at the provincial scale by integrating remote sensing, urban built-up area boundaries, spatial metrics and spatial regression. Sixty-seven cities were investigated to examine the differences of urbanization intensity, urbanization patterns and urban land use efficiency in conjunction with the identification of socio-economic indicators and planning str...

Habitat landscape pattern and connectivity indices : used at varying spatial scales for harmonized reporting in the EBONE project

NARCIS (Netherlands)

Estreguil, C.; Caudullo, G.; Whitmore, C.

2012-01-01

This study is motivated by biodiversity related policy information needs on ecosystem fragmentation and connectivity. The aim is to propose standardized and repeatable methods to characterize ecosystem landscape structure in a harmonized way at varying spatial scales and thematic resolutions

Simultaneous spatio-temporal focusing for tissue manipulation

Directory of Open Access Journals (Sweden)

Squier J.

2013-11-01

Full Text Available Simultaneous spatiotemporal focusing (SSTF is applied to lens tissue and compared directly with standard femtosecond micromachining of the tissue at the same numerical aperture. Third harmonic generation imaging is used for spatio-temporal characterization of the processing conditions obtained with both a standard and SSTF focus.

Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE

Science.gov (United States)

Kuo, K. S.; Rilee, M. L.

2017-12-01

coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.

Semi-supervised tracking of extreme weather events in global spatio-temporal climate datasets

Science.gov (United States)

Kim, S. K.; Prabhat, M.; Williams, D. N.

2017-12-01

Deep neural networks have been successfully applied to solve problem to detect extreme weather events in large scale climate datasets and attend superior performance that overshadows all previous hand-crafted methods. Recent work has shown that multichannel spatiotemporal encoder-decoder CNN architecture is able to localize events in semi-supervised bounding box. Motivated by this work, we propose new learning metric based on Variational Auto-Encoders (VAE) and Long-Short-Term-Memory (LSTM) to track extreme weather events in spatio-temporal dataset. We consider spatio-temporal object tracking problems as learning probabilistic distribution of continuous latent features of auto-encoder using stochastic variational inference. For this, we assume that our datasets are i.i.d and latent features is able to be modeled by Gaussian distribution. In proposed metric, we first train VAE to generate approximate posterior given multichannel climate input with an extreme climate event at fixed time. Then, we predict bounding box, location and class of extreme climate events using convolutional layers given input concatenating three features including embedding, sampled mean and standard deviation. Lastly, we train LSTM with concatenated input to learn timely information of dataset by recurrently feeding output back to next time-step's input of VAE. Our contribution is two-fold. First, we show the first semi-supervised end-to-end architecture based on VAE to track extreme weather events which can apply to massive scaled unlabeled climate datasets. Second, the information of timely movement of events is considered for bounding box prediction using LSTM which can improve accuracy of localization. To our knowledge, this technique has not been explored neither in climate community or in Machine Learning community.

Nonreciprocal Thermal Material by Spatiotemporal Modulation

Science.gov (United States)

Torrent, Daniel; Poncelet, Olivier; Batsale, Jean-Chirstophe

2018-03-01

The thermal properties of a material with a spatiotemporal modulation, in the form of a traveling wave, in both the thermal conductivity and the specific heat capacity are studied. It is found that these materials behave as materials with an internal convectionlike term that provides them with nonreciprocal properties, in the sense that the heat flux has different properties when it propagates in the same direction or in the opposite one to the modulation of the parameters. An effective medium description is presented which accurately describes the modulated material, and numerical simulations support this description and verify the nonreciprocal properties of the material. It is found that these materials are promising candidates for the design of thermal diodes and other advanced devices for the control of the heat flow at all scales.

Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform

International Nuclear Information System (INIS)

Tascikaraoglu, Akin; Sanandaji, Borhan M.; Poolla, Kameshwar; Varaiya, Pravin

2016-01-01

Highlights: • We propose a spatio-temporal approach for wind speed forecasting. • The method is based on a combination of Wavelet decomposition and structured-sparse recovery. • Our analyses confirm that low-dimensional structures govern the interactions between stations. • Our method particularly shows improvements for profiles with high ramps. • We examine our approach on real data and illustrate its superiority over a set of benchmark models. - Abstract: Integration of renewable energy resources into the power grid is essential in achieving the envisioned sustainable energy future. Stochasticity and intermittency characteristics of renewable energies, however, present challenges for integrating these resources into the existing grid in a large scale. Reliable renewable energy integration is facilitated by accurate wind forecasts. In this paper, we propose a novel wind speed forecasting method which first utilizes Wavelet Transform (WT) for decomposition of the wind speed data into more stationary components and then uses a spatio-temporal model on each sub-series for incorporating both temporal and spatial information. The proposed spatio-temporal forecasting approach on each sub-series is based on the assumption that there usually exists an intrinsic low-dimensional structure between time series data in a collection of meteorological stations. Our approach is inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms. Based on detailed case studies, we show that the proposed approach based on exploiting the sparsity of correlations between a large set of meteorological stations and decomposing time series for higher-accuracy forecasts considerably improve the short-term forecasts compared to the temporal and spatio-temporal benchmark methods.

Analysis and modelling of spatio-temporal properties of daily rainfall over the Danube basin

Science.gov (United States)

Serinaldi, F.; Kilsby, C. G.

2012-04-01

Central and Eastern Europe are prone to severe floods due to heavy rainfall that cause societal and economic damages, ranging from agriculture to water resources, from the insurance/reinsurance sector to the energy industry. To improve the flood risk analysis, a better characterisation and modelling of the rainfall patterns over this area, which involves the Danube river watershed, is strategically important. In this study, we analyse the spatio-temporal properties of a large data set of daily rainfall time series from 15 countries in the Central Eastern Europe through different lagged and non-lagged indices of associations that quantify both the overall dependence and extreme dependence of pairwise observations. We also show that these measures are linked to each other and can be written in a unique and coherent notation within the copula framework. Moreover, the lagged version of these measures allows exploring some important spatio-temporal properties of the rainfall fields. The exploratory analysis is complemented by the preliminary results of a spatio-temporal rainfall simulation performed via a compound model based upon the Generalized Additive Models for Location, Scale and Shape (GAMLSS) and meta-elliptical multivariate distributions.

Visualization and assessment of spatio-temporal covariance properties

KAUST Repository

Huang, Huang

2017-11-23

Spatio-temporal covariances are important for describing the spatio-temporal variability of underlying random fields in geostatistical data. For second-order stationary random fields, there exist subclasses of covariance functions that assume a simpler spatio-temporal dependence structure with separability and full symmetry. However, it is challenging to visualize and assess separability and full symmetry from spatio-temporal observations. In this work, we propose a functional data analysis approach that constructs test functions using the cross-covariances from time series observed at each pair of spatial locations. These test functions of temporal lags summarize the properties of separability or symmetry for the given spatial pairs. We use functional boxplots to visualize the functional median and the variability of the test functions, where the extent of departure from zero at all temporal lags indicates the degree of non-separability or asymmetry. We also develop a rank-based nonparametric testing procedure for assessing the significance of the non-separability or asymmetry. Essentially, the proposed methods only require the analysis of temporal covariance functions. Thus, a major advantage over existing approaches is that there is no need to estimate any covariance matrix for selected spatio-temporal lags. The performances of the proposed methods are examined by simulations with various commonly used spatio-temporal covariance models. To illustrate our methods in practical applications, we apply it to real datasets, including weather station data and climate model outputs.

Dynamic decomposition of spatiotemporal neural signals.

Directory of Open Access Journals (Sweden)

Luca Ambrogioni

2017-05-01

Full Text Available Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

A Multiphysics Framework to Learn and Predict in Presence of Multiple Scales

Science.gov (United States)

Tomin, P.; Lunati, I.

2015-12-01

Modeling complex phenomena in the subsurface remains challenging due to the presence of multiple interacting scales, which can make it impossible to focus on purely macroscopic phenomena (relevant in most applications) and neglect the processes at the micro-scale. We present and discuss a general framework that allows us to deal with the situation in which the lack of scale separation requires the combined use of different descriptions at different scale (for instance, a pore-scale description at the micro-scale and a Darcy-like description at the macro-scale) [1,2]. The method is based on conservation principles and constructs the macro-scale problem by numerical averaging of micro-scale balance equations. By employing spatiotemporal adaptive strategies, this approach can efficiently solve large-scale problems [2,3]. In addition, being based on a numerical volume-averaging paradigm, it offers a tool to illuminate how macroscopic equations emerge from microscopic processes, to better understand the meaning of microscopic quantities, and to investigate the validity of the assumptions routinely used to construct the macro-scale problems. [1] Tomin, P., and I. Lunati, A Hybrid Multiscale Method for Two-Phase Flow in Porous Media, Journal of Computational Physics, 250, 293-307, 2013 [2] Tomin, P., and I. Lunati, Local-global splitting and spatiotemporal-adaptive Multiscale Finite Volume Method, Journal of Computational Physics, 280, 214-231, 2015 [3] Tomin, P., and I. Lunati, Spatiotemporal adaptive multiphysics simulations of drainage-imbibition cycles, Computational Geosciences, 2015 (under review)

Analysis of spatiotemporal soil moisture patterns at the catchment scale using a wireless sensor network

Science.gov (United States)

Bogena, Heye R.; Huisman, Johan A.; Rosenbaum, Ulrike; Weuthen, Ansgar; Vereecken, Harry

2010-05-01

Soil water content plays a key role in partitioning water and energy fluxes and controlling the pattern of groundwater recharge. Despite the importance of soil water content, it is not yet measured in an operational way at larger scales. The aim of this paper is to present the potential of real-time monitoring for the analysis of soil moisture patterns at the catchment scale using the recently developed wireless sensor network SoilNet [1], [2]. SoilNet is designed to measure soil moisture, salinity and temperature in several depths (e.g. 5, 20 and 50 cm). Recently, a small forest catchment Wüstebach (~27 ha) has been instrumented with 150 sensor nodes and more than 1200 soil sensors in the framework of the Transregio32 and the Helmholtz initiative TERENO (Terrestrial Environmental Observatories). From August to November 2009, more than 6 million soil moisture measurements have been performed. We will present first results from a statistical and geostatistical analysis of the data. The observed spatial variability of soil moisture corresponds well with the 800-m scale variability described in [3]. The very low scattering of the standard deviation versus mean soil moisture plots indicates that sensor network data shows less artificial soil moisture variations than soil moisture data originated from measurement campaigns. The variograms showed more or less the same nugget effect, which indicates that the sum of the sub-scale variability and the measurement error is rather time-invariant. Wet situations showed smaller spatial variability, which is attributed to saturated soil water content, which poses an upper limit and is typically not strongly variable in headwater catchments with relatively homogeneous soil. The spatiotemporal variability in soil moisture at 50 cm depth was significantly lower than at 5 and 20 cm. This finding indicates that the considerable variability of the top soil is buffered deeper in the soil due to lateral and vertical water fluxes

NeuroMap: A spline-based interactive open-source software for spatiotemporal mapping of 2D and 3D MEA data

Directory of Open Access Journals (Sweden)

Oussama eAbdoun

2011-01-01

Full Text Available A major characteristic of neural networks is the complexity of their organization at various spatial scales, from microscopic local circuits to macroscopic brain-scale areas. Understanding how neural information is processed thus entails the ability to study them at multiple scales simultaneously. This is made possible using microelectrodes array (MEA technology. Indeed, high-density MEAs provide large-scale covering (several mm² of whole neural structures combined with microscopic resolution (about 50µm of unit activity. Yet, current options for spatiotemporal representation of MEA-collected data remain limited. Here we present NeuroMap, a new interactive Matlab-based software for spatiotemporal mapping of MEA data. NeuroMap uses thin plate spline interpolation, which provides several assets with respect to conventional mapping methods used currently. First, any MEA design can be considered, including 2D or 3D, regular or irregular, arrangements of electrodes. Second, spline interpolation allows the estimation of activity across the tissue with local extrema not necessarily at recording sites. Finally, this interpolation approach provides a straightforward analytical estimation of the spatial Laplacian for better current sources localization. In this software, coregistration of 2D MEA data on the anatomy of the neural tissue is made possible by fine matching of anatomical data with electrode positions using rigid deformation based correction of anatomical pictures. Overall, NeuroMap provides substantial material for detailed spatiotemporal analysis of MEA data. The package is distributed under GNU General Public License (GPL and available at http://sites.google.com/site/neuromapsoftware.

NeuroMap: A Spline-Based Interactive Open-Source Software for Spatiotemporal Mapping of 2D and 3D MEA Data.

Science.gov (United States)

Abdoun, Oussama; Joucla, Sébastien; Mazzocco, Claire; Yvert, Blaise

2011-01-01

A major characteristic of neural networks is the complexity of their organization at various spatial scales, from microscopic local circuits to macroscopic brain-scale areas. Understanding how neural information is processed thus entails the ability to study them at multiple scales simultaneously. This is made possible using microelectrodes array (MEA) technology. Indeed, high-density MEAs provide large-scale coverage (several square millimeters) of whole neural structures combined with microscopic resolution (about 50 μm) of unit activity. Yet, current options for spatiotemporal representation of MEA-collected data remain limited. Here we present NeuroMap, a new interactive Matlab-based software for spatiotemporal mapping of MEA data. NeuroMap uses thin plate spline interpolation, which provides several assets with respect to conventional mapping methods used currently. First, any MEA design can be considered, including 2D or 3D, regular or irregular, arrangements of electrodes. Second, spline interpolation allows the estimation of activity across the tissue with local extrema not necessarily at recording sites. Finally, this interpolation approach provides a straightforward analytical estimation of the spatial Laplacian for better current sources localization. In this software, coregistration of 2D MEA data on the anatomy of the neural tissue is made possible by fine matching of anatomical data with electrode positions using rigid-deformation-based correction of anatomical pictures. Overall, NeuroMap provides substantial material for detailed spatiotemporal analysis of MEA data. The package is distributed under GNU General Public License and available at http://sites.google.com/site/neuromapsoftware.

Spatio-temporal light shaping for parallel nano-biophotonics

DEFF Research Database (Denmark)

Glückstad, Jesper; Palima, Darwin

followed separate tracks. Width-shaping, or spatial techniques, have mostly ignored light’s thickness (using continuous-wave lasers), while thickness-shaping, or temporal techniques, typically ignored the beam width. This disconnected spatial and temporal track also shows in our own research where we....... Another step is to vary light’s pulsewidth (thickness) as it propagates to get maximum compression (and highest energy density) at a chosen target plane. This temporal focusing can selectively look at a defined crosssection within a sample with only minimal disturbance from other regions. It can also do...... plane-byplane micromachining for faster laser processing compared to scanning a focused laser spot. Our previous work on spatial light shaping, together with the interplay between spatial and temporal modulation, invariably provides a strong position to pursue application-oriented spatiotemporal...

Controlling spatiotemporal chaos in one- and two-dimensional coupled logistic map lattices

International Nuclear Information System (INIS)

Astakhov, V.V.; Anishchenko, V.S.; Strelkova, G.I.; Shabunin, A.V.

1996-01-01

A method of control of spatiotemporal chaos in lattices of coupled maps is proposed in this work. Forms of spatiotemporal perturbations of a system parameter are analytically determined for one- and two-dimensional logistic map lattices with different kinds of coupling to stabilize chosen spatiotemporal states previously unstable. The results are illustrated by numerical simulation. Controlled transition from the regime of spatiotemporal chaos to the previously chosen regular spatiotemporal patterns is demonstrated. copyright 1996 American Institute of Physics

Fine-scale spatio-temporal variation in tiger Panthera tigris diet: Effect of study duration and extent on estimates of tiger diet in Chitwan National Park, Nepal

Science.gov (United States)

Kapfer, Paul M.; Streby, Henry M.; Gurung, B.; Simcharoen, A.; McDougal, C.C.; Smith, J.L.D.

2011-01-01

Attempts to conserve declining tiger Panthera tigris populations and distributions have experienced limited success. The poaching of tiger prey is a key threat to tiger persistence; a clear understanding of tiger diet is a prerequisite to conserve dwindling populations. We used unpublished data on tiger diet in combination with two previously published studies to examine fine-scale spatio-temporal changes in tiger diet relative to prey abundance in Chitwan National Park, Nepal, and aggregated data from the three studies to examine the effect that study duration and the size of the study area have on estimates of tiger diet. Our results correspond with those of previous studies: in all three studies, tiger diet was dominated by members of Cervidae; small to medium-sized prey was important in one study. Tiger diet was unrelated to prey abundance, and the aggregation of studies indicates that increasing study duration and study area size both result in increased dietary diversity in terms of prey categories consumed, and increasing study duration changed which prey species contributed most to tiger diet. Based on our results, we suggest that managers focus their efforts on minimizing the poaching of all tiger prey, and that future studies of tiger diet be of long duration and large spatial extent to improve our understanding of spatio-temporal variation in estimates of tiger diet. ?? 2011 Wildlife Biology, NKV.

Spatiotemporal psychopathology I: No rest for the brain's resting state activity in depression? Spatiotemporal psychopathology of depressive symptoms.

Science.gov (United States)

Northoff, Georg

2016-01-15

Despite intense neurobiological investigation in psychiatric disorders like major depressive disorder (MDD), the basic disturbance that underlies the psychopathological symptoms of MDD remains, nevertheless, unclear. Neuroimaging has focused mainly on the brain's extrinsic activity, specifically task-evoked or stimulus-induced activity, as related to the various sensorimotor, affective, cognitive, and social functions. Recently, the focus has shifted to the brain's intrinsic activity, otherwise known as its resting state activity. While various abnormalities have been observed during this activity, their meaning and significance for depression, along with its various psychopathological symptoms, are yet to be defined. Based on findings in healthy brain resting state activity and its particular spatial and temporal structure - defined in a functional and physiological sense rather than anatomical and structural - I claim that the various depressive symptoms are spatiotemporal disturbances of the resting state activity and its spatiotemporal structure. This is supported by recent findings that link ruminations and increased self-focus in depression to abnormal spatial organization of resting state activity. Analogously, affective and cognitive symptoms like anhedonia, suicidal ideation, and thought disorder can be traced to an increased focus on the past, increased past-focus as basic temporal disturbance o the resting state. Based on these findings, I conclude that the various depressive symptoms must be conceived as spatiotemporal disturbances of the brain's resting state's activity and its spatiotemporal structure. Importantly, this entails a new form of psychopathology, "Spatiotemporal Psychopathology" that directly links the brain and psyche, therefore having major diagnostic and therapeutic implications for clinical practice. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatiotemporal patterns formed by deformed adhesive in peeling

International Nuclear Information System (INIS)

Yamazaki, Yoshihiro; Toda, Akihiko

2007-01-01

Dynamical properties of peeling an adhesive tape are investigated experimentally as an analogy of sliding friction. An adhesive tape is peeled by pulling an elastic spring connected to the tape. Controlling its spring constant k and pulling speed V, peel force is measured and spatiotemporal patterns formed on the peeled tape by deformed adhesive are observed. It is found that there exist two kinds of adhesive state in peeling front. The emergence of multiple states is caused by the stability of a characteristic structure (tunnel structure) formed by deformed adhesive. Tunnel structures are distributed spatiotemporally on adhesive tape after peeling. Based on the spatiotemporal distribution, a morphology-dynamical phase diagram is constructed on k-V space and is divided into the four regions: (A) uniform pattern with tunnel structure, (B) uniform pattern without tunnel structure, (C) striped pattern with oscillatory peeling, and (D) spatiotemporally coexistent pattern

Large-Scale Spatio-Temporal Patterns of Mediterranean Cephalopod Diversity.

Directory of Open Access Journals (Sweden)

Stefanie Keller

Full Text Available Species diversity is widely recognized as an important trait of ecosystems' functioning and resilience. Understanding the causes of diversity patterns and their interaction with the environmental conditions is essential in order to effectively assess and preserve existing diversity. While diversity patterns of most recurrent groups such as fish are commonly studied, other important taxa such as cephalopods have received less attention. In this work we present spatio-temporal trends of cephalopod diversity across the entire Mediterranean Sea during the last 19 years, analysing data from the annual bottom trawl survey MEDITS conducted by 5 different Mediterranean countries using standardized gears and sampling protocols. The influence of local and regional environmental variability in different Mediterranean regions is analysed applying generalized additive models, using species richness and the Shannon Wiener index as diversity descriptors. While the western basin showed a high diversity, our analyses do not support a steady eastward decrease of diversity as proposed in some previous studies. Instead, high Shannon diversity was also found in the Adriatic and Aegean Seas, and high species richness in the eastern Ionian Sea. Overall diversity did not show any consistent trend over the last two decades. Except in the Adriatic Sea, diversity showed a hump-shaped trend with depth in all regions, being highest between 200-400 m depth. Our results indicate that high Chlorophyll a concentrations and warmer temperatures seem to enhance species diversity, and the influence of these parameters is stronger for richness than for Shannon diversity.

Learning large-scale dynamic discrete choice models of spatio-temporal preferences with application to migratory pastoralism in East Africa

Science.gov (United States)

Understanding spatio-temporal resource preferences is paramount in the design of policies for sustainable development. Unfortunately, resource preferences are often unknown to policy-makers and have to be inferred from data. In this paper we consider the problem of inferring agents’ preferences fro...

Human seizures couple across spatial scales through travelling wave dynamics

Science.gov (United States)

Martinet, L.-E.; Fiddyment, G.; Madsen, J. R.; Eskandar, E. N.; Truccolo, W.; Eden, U. T.; Cash, S. S.; Kramer, M. A.

2017-04-01

Epilepsy--the propensity toward recurrent, unprovoked seizures--is a devastating disease affecting 65 million people worldwide. Understanding and treating this disease remains a challenge, as seizures manifest through mechanisms and features that span spatial and temporal scales. Here we address this challenge through the analysis and modelling of human brain voltage activity recorded simultaneously across microscopic and macroscopic spatial scales. We show that during seizure large-scale neural populations spanning centimetres of cortex coordinate with small neural groups spanning cortical columns, and provide evidence that rapidly propagating waves of activity underlie this increased inter-scale coupling. We develop a corresponding computational model to propose specific mechanisms--namely, the effects of an increased extracellular potassium concentration diffusing in space--that support the observed spatiotemporal dynamics. Understanding the multi-scale, spatiotemporal dynamics of human seizures--and connecting these dynamics to specific biological mechanisms--promises new insights to treat this devastating disease.

Scale Dependence of Spatiotemporal Intermittence of Rain

Science.gov (United States)

Kundu, Prasun K.; Siddani, Ravi K.

2011-01-01

It is a common experience that rainfall is intermittent in space and time. This is reflected by the fact that the statistics of area- and/or time-averaged rain rate is described by a mixed distribution with a nonzero probability of having a sharp value zero. In this paper we have explored the dependence of the probability of zero rain on the averaging space and time scales in large multiyear data sets based on radar and rain gauge observations. A stretched exponential fannula fits the observed scale dependence of the zero-rain probability. The proposed formula makes it apparent that the space-time support of the rain field is not quite a set of measure zero as is sometimes supposed. We also give an ex.planation of the observed behavior in tenus of a simple probabilistic model based on the premise that rainfall process has an intrinsic memory.

A hybrid spatio-temporal data indexing method for trajectory databases.

Science.gov (United States)

Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting

2014-07-21

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

Directory of Open Access Journals (Sweden)

Shengnan Ke

2014-07-01

Full Text Available In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

Science.gov (United States)

Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting

2014-01-01

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type. PMID:25051028

Phenology Data Products to Support Assessment and Forecasting of Phenology on Multiple Spatiotemporal Scales

Science.gov (United States)

Gerst, K.; Enquist, C.; Rosemartin, A.; Denny, E. G.; Marsh, L.; Moore, D. J.; Weltzin, J. F.

2014-12-01

The USA National Phenology Network (USA-NPN; www.usanpn.org) serves science and society by promoting a broad understanding of plant and animal phenology and the relationships among phenological patterns and environmental change. The National Phenology Database maintained by USA-NPN now has over 3.7 million records for plants and animals for the period 1954-2014, with the majority of these observations collected since 2008 as part of a broad, national contributory science strategy. These data have been used in a number of science, conservation and resource management applications, including national assessments of historical and potential future trends in phenology, regional assessments of spatio-temporal variation in organismal activity, and local monitoring for invasive species detection. Customizable data downloads are freely available, and data are accompanied by FGDC-compliant metadata, data-use and data-attribution policies, vetted and documented methodologies and protocols, and version control. While users are free to develop custom algorithms for data cleaning, winnowing and summarization prior to analysis, the National Coordinating Office of USA-NPN is developing a suite of standard data products to facilitate use and application by a diverse set of data users. This presentation provides a progress report on data product development, including: (1) Quality controlled raw phenophase status data; (2) Derived phenometrics (e.g. onset, duration) at multiple scales; (3) Data visualization tools; (4) Tools to support assessment of species interactions and overlap; (5) Species responsiveness to environmental drivers; (6) Spatially gridded phenoclimatological products; and (7) Algorithms for modeling and forecasting future phenological responses. The prioritization of these data products is a direct response to stakeholder needs related to informing management and policy decisions. We anticipate that these products will contribute to broad understanding of plant

Clinical performance of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced pediatric abdominal MR angiography.

Science.gov (United States)

Zhang, Tao; Yousaf, Ufra; Hsiao, Albert; Cheng, Joseph Y; Alley, Marcus T; Lustig, Michael; Pauly, John M; Vasanawala, Shreyas S

2015-10-01

Pediatric contrast-enhanced MR angiography is often limited by respiration, other patient motion and compromised spatiotemporal resolution. To determine the reliability of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography method for depicting abdominal arterial anatomy in young children. With IRB approval and informed consent, we retrospectively identified 27 consecutive children (16 males and 11 females; mean age: 3.8 years, range: 14 days to 8.4 years) referred for contrast-enhanced MR angiography at our institution, who had undergone free-breathing spatiotemporally accelerated time-resolved contrast-enhanced MR angiography studies. A radio-frequency-spoiled gradient echo sequence with Cartesian variable density k-space sampling and radial view ordering, intrinsic motion navigation and intermittent fat suppression was developed. Images were reconstructed with soft-gated parallel imaging locally low-rank method to achieve both motion correction and high spatiotemporal resolution. Quality of delineation of 13 abdominal arteries in the reconstructed images was assessed independently by two radiologists on a five-point scale. Ninety-five percent confidence intervals of the proportion of diagnostically adequate cases were calculated. Interobserver agreements were also analyzed. Eleven out of 13 arteries achieved acceptable image quality (mean score range: 3.9-5.0) for both readers. Fair to substantial interobserver agreement was reached on nine arteries. Free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography frequently yields diagnostic image quality for most abdominal arteries in young children.

Clinical performance of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced pediatric abdominal MR angiography

Energy Technology Data Exchange (ETDEWEB)

Zhang, Tao; Cheng, Joseph Y. [Stanford University, Department of Radiology, Stanford, CA (United States); Stanford University, Department of Electrical Engineering, Stanford, CA (United States); Yousaf, Ufra; Alley, Marcus T.; Vasanawala, Shreyas S. [Stanford University, Department of Radiology, Stanford, CA (United States); Hsiao, Albert [University of California, San Diego, Department of Radiology, San Diego, CA (United States); Lustig, Michael [Stanford University, Department of Electrical Engineering, Stanford, CA (United States); University of California, Berkeley, Department of Electrical Engineering and Computer Sciences, Berkeley, CA (United States); Pauly, John M. [Stanford University, Department of Electrical Engineering, Stanford, CA (United States)

2015-10-15

Pediatric contrast-enhanced MR angiography is often limited by respiration, other patient motion and compromised spatiotemporal resolution. To determine the reliability of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography method for depicting abdominal arterial anatomy in young children. With IRB approval and informed consent, we retrospectively identified 27 consecutive children (16 males and 11 females; mean age: 3.8 years, range: 14 days to 8.4 years) referred for contrast-enhanced MR angiography at our institution, who had undergone free-breathing spatiotemporally accelerated time-resolved contrast-enhanced MR angiography studies. A radio-frequency-spoiled gradient echo sequence with Cartesian variable density k-space sampling and radial view ordering, intrinsic motion navigation and intermittent fat suppression was developed. Images were reconstructed with soft-gated parallel imaging locally low-rank method to achieve both motion correction and high spatiotemporal resolution. Quality of delineation of 13 abdominal arteries in the reconstructed images was assessed independently by two radiologists on a five-point scale. Ninety-five percent confidence intervals of the proportion of diagnostically adequate cases were calculated. Interobserver agreements were also analyzed. Eleven out of 13 arteries achieved acceptable image quality (mean score range: 3.9-5.0) for both readers. Fair to substantial interobserver agreement was reached on nine arteries. Free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography frequently yields diagnostic image quality for most abdominal arteries in young children. (orig.)

Clinical performance of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced pediatric abdominal MR angiography

International Nuclear Information System (INIS)

Zhang, Tao; Cheng, Joseph Y.; Yousaf, Ufra; Alley, Marcus T.; Vasanawala, Shreyas S.; Hsiao, Albert; Lustig, Michael; Pauly, John M.

2015-01-01

Pediatric contrast-enhanced MR angiography is often limited by respiration, other patient motion and compromised spatiotemporal resolution. To determine the reliability of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography method for depicting abdominal arterial anatomy in young children. With IRB approval and informed consent, we retrospectively identified 27 consecutive children (16 males and 11 females; mean age: 3.8 years, range: 14 days to 8.4 years) referred for contrast-enhanced MR angiography at our institution, who had undergone free-breathing spatiotemporally accelerated time-resolved contrast-enhanced MR angiography studies. A radio-frequency-spoiled gradient echo sequence with Cartesian variable density k-space sampling and radial view ordering, intrinsic motion navigation and intermittent fat suppression was developed. Images were reconstructed with soft-gated parallel imaging locally low-rank method to achieve both motion correction and high spatiotemporal resolution. Quality of delineation of 13 abdominal arteries in the reconstructed images was assessed independently by two radiologists on a five-point scale. Ninety-five percent confidence intervals of the proportion of diagnostically adequate cases were calculated. Interobserver agreements were also analyzed. Eleven out of 13 arteries achieved acceptable image quality (mean score range: 3.9-5.0) for both readers. Fair to substantial interobserver agreement was reached on nine arteries. Free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography frequently yields diagnostic image quality for most abdominal arteries in young children. (orig.)

Spatio-temporal correlations in the Manna model in one, three and five dimensions

Science.gov (United States)

Willis, Gary; Pruessner, Gunnar

2018-02-01

Although the paradigm of criticality is centered around spatial correlations and their anomalous scaling, not many studies of self-organized criticality (SOC) focus on spatial correlations. Often, integrated observables, such as avalanche size and duration, are used, not least as to avoid complications due to the unavoidable lack of translational invariance. The present work is a survey of spatio-temporal correlation functions in the Manna Model of SOC, measured numerically in detail in d = 1,3 and 5 dimensions and compared to theoretical results, in particular relating them to “integrated” observables such as avalanche size and duration scaling, that measure them indirectly. Contrary to the notion held by some of SOC models organizing into a critical state by re-arranging their spatial structure avalanche by avalanche, which may be expected to result in large, nontrivial, system-spanning spatial correlations in the quiescent state (between avalanches), correlations of inactive particles in the quiescent state have a small amplitude that does not and cannot increase with the system size, although they display (noisy) power law scaling over a range linear in the system size. Self-organization, however, does take place as the (one-point) density of inactive particles organizes into a particular profile that is asymptotically independent of the driving location, also demonstrated analytically in one dimension. Activity and its correlations, on the other hand, display nontrivial long-ranged spatio-temporal scaling with exponents that can be related to established results, in particular avalanche size and duration exponents. The correlation length and amplitude are set by the system size (confirmed analytically for some observables), as expected in systems displaying finite size scaling. In one dimension, we find some surprising inconsistencies of the dynamical exponent. A (spatially extended) mean field theory (MFT) is recovered, with some corrections, in five

Spatiotemporal representation of cardiac vectorcardiogram (VCG signals

Directory of Open Access Journals (Sweden)

Yang Hui

2012-03-01

Full Text Available Abstract Background Vectorcardiogram (VCG signals monitor both spatial and temporal cardiac electrical activities along three orthogonal planes of the body. However, the absence of spatiotemporal resolution in conventional VCG representations is a major impediment for medical interpretation and clinical usage of VCG. This is especially so because time-domain features of 12-lead ECG, instead of both spatial and temporal characteristics of VCG, are widely used for the automatic assessment of cardiac pathological patterns. Materials and methods We present a novel representation approach that captures critical spatiotemporal heart dynamics by displaying the real time motion of VCG cardiac vectors in a 3D space. Such a dynamic display can also be realized with only one lead ECG signal (e.g., ambulatory ECG through an alternative lag-reconstructed ECG representation from nonlinear dynamics principles. Furthermore, the trajectories are color coded with additional dynamical properties of space-time VCG signals, e.g., the curvature, speed, octant and phase angles to enhance the information visibility. Results In this investigation, spatiotemporal VCG signal representation is used to characterize various spatiotemporal pathological patterns for healthy control (HC, myocardial infarction (MI, atrial fibrillation (AF and bundle branch block (BBB. The proposed color coding scheme revealed that the spatial locations of the peak of T waves are in the Octant 6 for the majority (i.e., 74 out of 80 of healthy recordings in the PhysioNet PTB database. In contrast, the peak of T waves from 31.79% (117/368 of MI subjects are found to remain in Octant 6 and the rest (68.21% spread over all other octants. The spatiotemporal VCG signal representation is shown to capture the same important heart characteristics as the 12-lead ECG plots and more. Conclusions Spatiotemporal VCG signal representation is shown to facilitate the characterization of space-time cardiac

A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data

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Dawen Xia

2018-01-01

Full Text Available Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR, CombineFileInputFormat (CFIF, and Sequence Files (SF, to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP algorithm in efficiency and scalability.

Spatiotemporal Variations of Reference Crop Evapotranspiration in Northern Xinjiang, China

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Jian Wang

2014-01-01

Full Text Available To set up a reasonable crop irrigation system in the context of global climate change in Northern Xinjiang, China, reference crop evapotranspiration (ET0 was analyzed by means of spatiotemporal variations. The ET0 values from 1962 to 2010 were calculated by Penman-Monteith formula, based on meteorological data of 22 meteorological observation stations in the study area. The spatiotemporal variations of ET0 were analyzed by Mann-Kendall test, Morlet wavelet analysis, and ArcGIS spatial analysis. The results showed that regional average ET0 had a decreasing trend and there was an abrupt change around 1983. The trend of regional average ET0 had a primary period about 28 years, in which there were five alternating stages (high-low-high-low-high. From the standpoint of spatial scale, ET0 gradually increased from the northeast and southwest toward the middle; the southeast and west had slightly greater variation, with significant regional differences. From April to October, the ET0 distribution significantly influenced the distribution characteristic of annual ET0. Among them sunshine hours and wind speed were two of principal climate factors affecting ET0.

Assessing the Defoliation of Pine Forests in a Long Time-Series and Spatiotemporal Prediction of the Defoliation Using Landsat Data

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Chenghao Zhu

2018-02-01

Full Text Available Pine forests (Pinus tabulaeformis have been in danger of defoliation by a caterpillar in the west Liaoning province of China for more than thirty years. This paper aims to assess and predict the degree of damage to pine forests by using remote sensing and ancillary data. Through regression analysis of the pine foliage remaining ratios of field plots with several vegetation indexes of Landsat data, a feasible inversion model was obtained to detect the degree of damage using the Normalized Difference Infrared Index of 5th band (NDII5. After comparing the inversion result of the degree of damage to the pine in 29 years and the historical damage record, quantized results of damage assessment in a long time-series were accurately obtained. Based on the correlation analysis between meteorological variables and the degree of damage from 1984 to 2015, the average degree of damage was predicted in temporal scale. By adding topographic and other variables, a linear prediction model in spatiotemporal scale was constructed. The spatiotemporal model was based on 5015 public pine points for 24 years and reached 0.6169 in the correlation coefficient. This paper provided a feasible and quantitative method in the spatiotemporal prediction of forest pest occurrence by remote sensing.

Joint level-set and spatio-temporal motion detection for cell segmentation.

Science.gov (United States)

Boukari, Fatima; Makrogiannis, Sokratis

2016-08-10

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images. In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result. We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan

Participatory Bluetooth Sensing: A Method for Acquiring Spatio-Temporal Data about Participant Mobility and Interactions at Large Scale Events

DEFF Research Database (Denmark)

Stopczynski, Arkadiusz; Larsen, Jakob Eg; Jørgensen, Sune Lehmann

2013-01-01

for collecting spatio-temporal data about participant mobility and social interactions uses the capabilities of Bluetooth capable smartphones carried by participants. As a proof-of-concept we present a field study with deployment of the method in a large music festival with 130 000 participants where a small...

A hybrid spatiotemporal drought forecasting model for operational use

Science.gov (United States)

Vasiliades, L.; Loukas, A.

2010-09-01

Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. This study develops a hybrid spatiotemporal scheme for integrated spatial and temporal forecasting. Temporal forecasting is achieved using feed-forward neural networks and the temporal forecasts are extended to the spatial dimension using a spatial recurrent neural network model. The methodology is demonstrated for an operational meteorological drought index the Standardized Precipitation Index (SPI) calculated at multiple timescales. 48 precipitation stations and 18 independent precipitation stations, located at Pinios river basin in Thessaly region, Greece, were used for the development and spatiotemporal validation of the hybrid spatiotemporal scheme. Several quantitative temporal and spatial statistical indices were considered for the performance evaluation of the models. Furthermore, qualitative statistical criteria based on contingency tables between observed and forecasted drought episodes were calculated. The results show that the lead time of forecasting for operational use depends on the SPI timescale. The hybrid spatiotemporal drought forecasting model could be operationally used for forecasting up to three months ahead for SPI short timescales (e.g. 3-6 months) up to six months ahead for large SPI timescales (e.g. 24 months). The above findings could be useful in developing a drought preparedness plan in the region.

Charging stations location model based on spatiotemporal electromobility use patterns

Science.gov (United States)

Pagany, Raphaela; Marquardt, Anna; Zink, Roland

2016-04-01

One of the major challenges for mainstream adoption of electric vehicles is the provision of infrastructure for charging the batteries of the vehicles. The charging stations must not only be located dense enough to allow users to complete their journeys, but the electric energy must also be provided from renewable sources in order to truly offer a transportation with less CO2 emissions. The examination of potential locations for the charging of electric vehicles can facilitate the adaption of electromobility and the integration of electronic vehicles in everyday life. A geographic information system (GIS) based model for optimal location of charging stations in a small and regional scale is presented. This considers parameters such as the forecast of electric vehicle use penetration, the relevant weight of diverse point of interests and the distance between parking area and destination for different vehicle users. In addition to the spatial scale the temporal modelling of the energy demand at the different charging locations has to be considerate. Depending on different user profiles (commuters, short haul drivers etc.) the frequency of charging vary during the day, the week and the year. In consequence, the spatiotemporal variability is a challenge for a reliable energy supply inside a decentralized renewable energy system. The presented model delivers on the one side the most adequate identified locations for charging stations and on the other side the interaction between energy supply and demand for electromobility under the consideration of temporal aspects. Using ESRI ArcGIS Desktop, first results for the case study region of Lower Bavaria are generated. The aim of the concept is to keep the model transferable to other regions and also open to integrate further and more detailed user profiles, derived from social studies about i.e. the daily behavior and the perception of electromobility in a next step.

Closing the scale gap between land surface parameterizations and GCMs with a new scheme, SiB3-Bins: SOIL MOISTURE SCALE GAP

International Nuclear Information System (INIS)

Baker, I. T.; Sellers, P. J.; Denning, A. S.; Medina, I.; Kraus, P.

2017-01-01

The interaction of land with the atmosphere is sensitive to soil moisture (W). Evapotranspiration (ET) reacts to soil moisture in a nonlinear way, f(W), as soils dry from saturation to wilt point. This nonlinear behavior and the fact that soil moisture varies on scales as small as 1–10 m in nature, while numerical general circulation models (GCMs) have grid cell sizes on the order of 1 to 100s of kilometers, makes the calculation of grid cell-average ET problematic. It is impractical to simulate the land in GCMs on the small scales seen in nature, so techniques have been developed to represent subgrid scale heterogeneity, including: (1) statistical-dynamical representations of grid subelements of varying wetness, (2) relaxation of f(W), (3) moderating f(W) with approximations of catchment hydrology, (4) “tiling” the landscape into vegetation types, and (5) hyperresolution. Here we present an alternative method for representing subgrid variability in W, one proven in a conceptual framework where landscape-scale W is represented as a series of “Bins” of increasing wetness from dry to saturated. The grid cell-level f(W) is defined by the integral of the fractional area of the wetness bins and the value of f(W) associated with each. This approach accounts for the spatiotemporal dynamics of W. We implemented this approach in the SiB3 land surface parameterization and then evaluated its performance against a control, which assumes a horizontally uniform field of W. We demonstrate that the Bins method, with a physical basis, attenuates unrealistic jumps in model state and ET seen in the control runs.

Spatio-temporal imaging of the hemoglobin in the compressed breast with diffuse optical tomography

Science.gov (United States)

Boverman, Gregory; Fang, Qianqian; Carp, Stefan A.; Miller, Eric L.; Brooks, Dana H.; Selb, Juliette; Moore, Richard H.; Kopans, Daniel B.; Boas, David A.

2007-07-01

We develop algorithms for imaging the time-varying optical absorption within the breast given diffuse optical tomographic data collected over a time span that is long compared to the dynamics of the medium. Multispectral measurements allow for the determination of the time-varying total hemoglobin concentration and of oxygen saturation. To facilitate the image reconstruction, we decompose the hemodynamics in time into a linear combination of spatio-temporal basis functions, the coefficients of which are estimated using all of the data simultaneously, making use of a Newton-based nonlinear optimization algorithm. The solution of the extremely large least-squares problem which arises in computing the Newton update is obtained iteratively using the LSQR algorithm. A Laplacian spatial regularization operator is applied, and, in addition, we make use of temporal regularization which tends to encourage similarity between the images of the spatio-temporal coefficients. Results are shown for an extensive simulation, in which we are able to image and quantify localized changes in both total hemoglobin concentration and oxygen saturation. Finally, a breast compression study has been performed for a normal breast cancer screening subject, using an instrument which allows for highly accurate co-registration of multispectral diffuse optical measurements with an x-ray tomosynthesis image of the breast. We are able to quantify the global return of blood to the breast following compression, and, in addition, localized changes are observed which correspond to the glandular region of the breast.

Parametric spatiotemporal oscillation in reaction-diffusion systems.

Science.gov (United States)

Ghosh, Shyamolina; Ray, Deb Shankar

2016-03-01

We consider a reaction-diffusion system in a homogeneous stable steady state. On perturbation by a time-dependent sinusoidal forcing of a suitable scaling parameter the system exhibits parametric spatiotemporal instability beyond a critical threshold frequency. We have formulated a general scheme to calculate the threshold condition for oscillation and the range of unstable spatial modes lying within a V-shaped region reminiscent of Arnold's tongue. Full numerical simulations show that depending on the specificity of nonlinearity of the models, the instability may result in time-periodic stationary patterns in the form of standing clusters or spatially localized breathing patterns with characteristic wavelengths. Our theoretical analysis of the parametric oscillation in reaction-diffusion system is corroborated by full numerical simulation of two well-known chemical dynamical models: chlorite-iodine-malonic acid and Briggs-Rauscher reactions.

Self-Organization in Coupled Map Scale-Free Networks

International Nuclear Information System (INIS)

Xiao-Ming, Liang; Zong-Hua, Liu; Hua-Ping, Lü

2008-01-01

We study the self-organization of phase synchronization in coupled map scale-free networks with chaotic logistic map at each node and find that a variety of ordered spatiotemporal patterns emerge spontaneously in a regime of coupling strength. These ordered behaviours will change with the increase of the average links and are robust to both the system size and parameter mismatch. A heuristic theory is given to explain the mechanism of self-organization and to figure out the regime of coupling for the ordered spatiotemporal patterns

Spatiotemporal drought forecasting using nonlinear models

Science.gov (United States)

Vasiliades, Lampros; Loukas, Athanasios

2010-05-01

Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatiotemporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension, respectively. Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Despite the widespread application of nonlinear mathematical models, comparative studies on spatiotemporal drought forecasting using different models are still a huge task for modellers. This study uses a promising approach, the Gamma Test (GT), to select the input variables and the training data length, so that the trial and error workload could be greatly reduced. The GT enables to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on any unseen data for a given selection of inputs, prior to model construction. The GT is applied to forecast droughts using monthly Standardized Precipitation Index (SPI) timeseries at multiple timescales in several precipitation stations at Pinios river basin in Thessaly region, Greece. Several nonlinear models have been developed efficiently, with the aid of the GT, for 1-month up to 12-month ahead forecasting. Several temporal and spatial statistical indices were considered for the performance evaluation of the models. The predicted results show reasonably good agreement with the actual data for short lead times, whereas the forecasting accuracy decreases with

Active sensing via movement shapes spatiotemporal patterns of sensory feedback.

Science.gov (United States)

Stamper, Sarah A; Roth, Eatai; Cowan, Noah J; Fortune, Eric S

2012-05-01

Previous work has shown that animals alter their locomotor behavior to increase sensing volumes. However, an animal's own movement also determines the spatial and temporal dynamics of sensory feedback. Because each sensory modality has unique spatiotemporal properties, movement has differential and potentially independent effects on each sensory system. Here we show that weakly electric fish dramatically adjust their locomotor behavior in relation to changes of modality-specific information in a task in which increasing sensory volume is irrelevant. We varied sensory information during a refuge-tracking task by changing illumination (vision) and conductivity (electroreception). The gain between refuge movement stimuli and fish tracking responses was functionally identical across all sensory conditions. However, there was a significant increase in the tracking error in the dark (no visual cues). This was a result of spontaneous whole-body oscillations (0.1 to 1 Hz) produced by the fish. These movements were costly: in the dark, fish swam over three times further when tracking and produced more net positive mechanical work. The magnitudes of these oscillations increased as electrosensory salience was degraded via increases in conductivity. In addition, tail bending (1.5 to 2.35 Hz), which has been reported to enhance electrosensory perception, occurred only during trials in the dark. These data show that both categories of movements - whole-body oscillations and tail bends - actively shape the spatiotemporal dynamics of electrosensory feedback.

A new framework to increase the efficiency of large-scale solar power plants.

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Alimohammadi, Shahrouz; Kleissl, Jan P.

2015-11-01

A new framework to estimate the spatio-temporal behavior of solar power is introduced, which predicts the statistical behavior of power output at utility scale Photo-Voltaic (PV) power plants. The framework is based on spatio-temporal Gaussian Processes Regression (Kriging) models, which incorporates satellite data with the UCSD version of the Weather and Research Forecasting model. This framework is designed to improve the efficiency of the large-scale solar power plants. The results are also validated from measurements of the local pyranometer sensors, and some improvements in different scenarios are observed. Solar energy.

Spatio-temporal transmission patterns of black-band disease in a coral community.

Directory of Open Access Journals (Sweden)

Assaf Zvuloni

Full Text Available BACKGROUND: Transmission mechanisms of black-band disease (BBD in coral reefs are poorly understood, although this disease is considered to be one of the most widespread and destructive coral infectious diseases. The major objective of this study was to assess transmission mechanisms of BBD in the field based on the spatio-temporal patterns of the disease. METHODOLOGY/PRINCIPAL FINDINGS: 3,175 susceptible and infected corals were mapped over an area of 10x10 m in Eilat (northern Gulf of Aqaba, Red Sea and the distribution of the disease was examined monthly throughout almost two full disease cycles (June 2006-December 2007. Spatial and spatio-temporal analyses were applied to infer the transmission pattern of the disease and to calculate key epidemiological parameters such as (basic reproduction number. We show that the prevalence of the disease is strongly associated with high water temperature. When water temperatures rise and disease prevalence increases, infected corals exhibit aggregated distributions on small spatial scales of up to 1.9 m. Additionally, newly-infected corals clearly appear in proximity to existing infected corals and in a few cases in direct contact with them. We also present and test a model of water-borne infection, indicating that the likelihood of a susceptible coral becoming infected is defined by its spatial location and by the relative spatial distribution of nearby infected corals found in the site. CONCLUSIONS/SIGNIFICANCE: Our results provide evidence that local transmission, but not necessarily by direct contact, is likely to be an important factor in the spread of the disease over the tested spatial scale. In the absence of potential disease vectors with limited mobility (e.g., snails, fireworms in the studied site, water-borne infection is likely to be a significant transmission mechanism of BBD. Our suggested model of water-borne transmission supports this hypothesis. The spatio-temporal analysis also points

Visual representation of spatiotemporal structure

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Schill, Kerstin; Zetzsche, Christoph; Brauer, Wilfried; Eisenkolb, A.; Musto, A.

1998-07-01

The processing and representation of motion information is addressed from an integrated perspective comprising low- level signal processing properties as well as higher-level cognitive aspects. For the low-level processing of motion information we argue that a fundamental requirement is the existence of a spatio-temporal memory. Its key feature, the provision of an orthogonal relation between external time and its internal representation, is achieved by a mapping of temporal structure into a locally distributed activity distribution accessible in parallel by higher-level processing stages. This leads to a reinterpretation of the classical concept of `iconic memory' and resolves inconsistencies on ultra-short-time processing and visual masking. The spatial-temporal memory is further investigated by experiments on the perception of spatio-temporal patterns. Results on the direction discrimination of motion paths provide evidence that information about direction and location are not processed and represented independent of each other. This suggests a unified representation on an early level, in the sense that motion information is internally available in form of a spatio-temporal compound. For the higher-level representation we have developed a formal framework for the qualitative description of courses of motion that may occur with moving objects.

A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting

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Shifen Cheng

2018-06-01

Full Text Available Short-term traffic forecasting plays an important part in intelligent transportation systems. Spatiotemporal k-nearest neighbor models (ST-KNNs have been widely adopted for short-term traffic forecasting in which spatiotemporal matrices are constructed to describe traffic conditions. The performance of the models is closely related to the spatial dependencies, the temporal dependencies, and the interaction of spatiotemporal dependencies. However, these models use distance functions and correlation coefficients to identify spatial neighbors and measure the temporal interaction by only considering the temporal closeness of traffic, which result in existing ST-KNNs that cannot fully reflect the essential features of road traffic. This study proposes an improved spatiotemporal k-nearest neighbor model for short-term traffic forecasting by utilizing a multi-view learning algorithm named MVL-STKNN that fully considers the spatiotemporal dependencies of traffic data. First, the spatial neighbors for each road segment are automatically determined using cross-correlation under different temporal dependencies. Three spatiotemporal views are built on the constructed spatiotemporal closeness, periodic, and trend matrices to represent spatially heterogeneous traffic states. Second, a spatiotemporal weighting matrix is introduced into the ST-KNN model to recognize similar traffic patterns in the three spatiotemporal views. Finally, the results of traffic pattern recognition under these three spatiotemporal views are aggregated by using a neural network algorithm to describe the interaction of spatiotemporal dependencies. Extensive experiments were conducted using real vehicular-speed datasets collected on city roads and expressways. In comparison with baseline methods, the results show that the MVL-STKNN model greatly improves short-term traffic forecasting by lowering the mean absolute percentage error between 28.24% and 46.86% for the city road dataset and

A Mixed Land Cover Spatio-temporal Data Model Based on Object-oriented and Snapshot

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LI Yinchao

2016-07-01

Full Text Available Spatio-temporal data model (STDM is one of the hot topics in the domains of spatio-temporal database and data analysis. There is a common view that a universal STDM is always of high complexity due to the various situation of spatio-temporal data. In this article, a mixed STDM is proposed based on object-oriented and snapshot models for modelling and analyzing landcover change (LCC. This model uses the object-oriented STDM to describe the spatio-temporal processes of land cover patches and organize their spatial and attributive properties. In the meantime, it uses the snapshot STDM to present the spatio-temporal distribution of LCC on the whole via snapshot images. The two types of models are spatially and temporally combined into a mixed version. In addition to presenting the spatio-temporal events themselves, this model could express the transformation events between different classes of spatio-temporal objects. It can be used to create database for historical data of LCC, do spatio-temporal statistics, simulation and data mining with the data. In this article, the LCC data in Heilongjiang province is used for case study to validate spatio-temporal data management and analysis abilities of mixed STDM, including creating database, spatio-temporal query, global evolution analysis and patches spatio-temporal process expression.

Quantile-based Bayesian maximum entropy approach for spatiotemporal modeling of ambient air quality levels.

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Yu, Hwa-Lung; Wang, Chih-Hsin

2013-02-05

Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.

How and Why Does Stream Water Temperature Vary at Small Spatial Scales in a Headwater Stream?

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Morgan, J. C.; Gannon, J. P.; Kelleher, C.

2017-12-01

The temperature of stream water is controlled by climatic variables, runoff/baseflow generation, and hyporheic exchange. Hydrologic conditions such as gaining/losing reaches and sources of inflow can vary dramatically along a stream on a small spatial scale. In this work, we attempt to discern the extent that the factors of air temperature, groundwater inflow, and precipitation influence stream temperature at small spatial scales along the length of a stream. To address this question, we measured stream temperature along the perennial stream network in a 43 ha catchment with a complex land use history in Cullowhee, NC. Two water temperature sensors were placed along the stream network on opposite sides of the stream at 100-meter intervals and at several locations of interest (i.e. stream junctions). The forty total sensors recorded the temperature every 10 minutes for one month in the spring and one month in the summer. A subset of sampling locations where stream temperature was consistent or varied from one side of the stream to the other were explored with a thermal imaging camera to obtain a more detailed representation of the spatial variation in temperature at those sites. These thermal surveys were compared with descriptions of the contributing area at the sample sites in an effort to discern specific causes of differing flow paths. Preliminary results suggest that on some branches of the stream stormflow has less influence than regular hyporheic exchange, while other tributaries can change dramatically with stormflow conditions. We anticipate this work will lead to a better understanding of temperature patterns in stream water networks. A better understanding of the importance of small-scale differences in flow paths to water temperature may be able to inform watershed management decisions in the future.

A stream cipher based on a spatiotemporal chaotic system

International Nuclear Information System (INIS)

Li Ping; Li Zhong; Halang, Wolfgang A.; Chen Guanrong

2007-01-01

A stream cipher based on a spatiotemporal chaotic system is proposed. A one-way coupled map lattice consisting of logistic maps is served as the spatiotemporal chaotic system. Multiple keystreams are generated from the coupled map lattice by using simple algebraic computations, and then are used to encrypt plaintext via bitwise XOR. These make the cipher rather simple and efficient. Numerical investigation shows that the cryptographic properties of the generated keystream are satisfactory. The cipher seems to have higher security, higher efficiency and lower computation expense than the stream cipher based on a spatiotemporal chaotic system proposed recently

Spatio-temporal Hotelling observer for signal detection from image sequences.

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Caucci, Luca; Barrett, Harrison H; Rodriguez, Jeffrey J

2009-06-22

Detection of signals in noisy images is necessary in many applications, including astronomy and medical imaging. The optimal linear observer for performing a detection task, called the Hotelling observer in the medical literature, can be regarded as a generalization of the familiar prewhitening matched filter. Performance on the detection task is limited by randomness in the image data, which stems from randomness in the object, randomness in the imaging system, and randomness in the detector outputs due to photon and readout noise, and the Hotelling observer accounts for all of these effects in an optimal way. If multiple temporal frames of images are acquired, the resulting data set is a spatio-temporal random process, and the Hotelling observer becomes a spatio-temporal linear operator. This paper discusses the theory of the spatio-temporal Hotelling observer and estimation of the required spatio-temporal covariance matrices. It also presents a parallel implementation of the observer on a cluster of Sony PLAYSTATION 3 gaming consoles. As an example, we consider the use of the spatio-temporal Hotelling observer for exoplanet detection.

Optimization of Spatiotemporal Apertures in Channel Sounding

DEFF Research Database (Denmark)

Pedersen, Troels; Pedersen, Claus; Yin, Xuefeng

2008-01-01

a spatiotemporal model which can describe parallel as well as switched sounding systems. The proposed model is applicable for arbitrary layouts of the spatial arrays. To simplify the derivations we investigate the special case of linear spatial arrays. However, the results obtained for linear arrays can......In this paper we investigate the impact of the spatio-temporal aperture of a channel sounding system equipped with antenna arrays at the transmitter and receiver on the accuracy of joint estimation of Doppler frequency and bi-direction. The contribution of this work is three-fold. Firstly, we state...... be generalized to arbitrary arrays. Secondly, we give the necessary and sufficient conditions for a spatio-temporal array to yield the minimum Cramér-Rao lower bound in the single-path case and Bayesian Cramér-Rao Lower Bound in the multipath case. The obtained conditions amount to an orthogonality condition...

Pollutant Dispersion in Boundary Layers Exposed to Rural-to-Urban Transitions: Varying the Spanwise Length Scale of the Roughness

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Tomas, J. M.; Eisma, H. E.; Pourquie, M. J. B. M.; Elsinga, G. E.; Jonker, H. J. J.; Westerweel, J.

2017-05-01

Both large-eddy simulations (LES) and water-tunnel experiments, using simultaneous stereoscopic particle image velocimetry and laser-induced fluorescence, have been used to investigate pollutant dispersion mechanisms in regions where the surface changes from rural to urban roughness. The urban roughness was characterized by an array of rectangular obstacles in an in-line arrangement. The streamwise length scale of the roughness was kept constant, while the spanwise length scale was varied by varying the obstacle aspect ratio l / h between 1 and 8, where l is the spanwise dimension of the obstacles and h is the height of the obstacles. Additionally, the case of two-dimensional roughness (riblets) was considered in LES. A smooth-wall turbulent boundary layer of depth 10 h was used as the approaching flow, and a line source of passive tracer was placed 2 h upstream of the urban canopy. The experimental and numerical results show good agreement, while minor discrepancies are readily explained. It is found that for l/h=2 the drag induced by the urban canopy is largest of all considered cases, and is caused by a large-scale secondary flow. In addition, due to the roughness transition the vertical advective pollutant flux is the main ventilation mechanism in the first three streets. Furthermore, by means of linear stochastic estimation the mean flow structure is identified that is responsible for street-canyon ventilation for the sixth street and onwards. Moreover, it is shown that the vertical length scale of this structure increases with increasing aspect ratio of the obstacles in the canopy, while the streamwise length scale does not show a similar trend.

Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

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Takeda, Yusuke; Hiroe, Nobuo; Yamashita, Okito; Sato, Masa-Aki

2016-06-01

Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Spatiotemporal Diffusive Evolution and Fractal Structure of Ground Motion

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Suwada, Tsuyoshi

2018-02-01

The spatiotemporal diffusive evolution and fractal structure of ground motion have been investigated at the in-ground tunnel of the KEK B-Factory (KEKB) injector linear accelerator (linac). The slow dynamic fluctuating displacements of the tunnel floor are measured in real time with a new remote-controllable sensing system based on a laser-based alignment system. Based on spatiotemporal analyses with linear-regression models, which were applied in both the time and frequency domains to time-series data recorded over a period of approximately 8 months, both coherent and stochastic components in the displacements of the tunnel floor were clearly observed along the entire length of the linac. In particular, it was clearly observed that the stochastic components exhibited characteristic spatiotemporal diffusive evolution with the fractal structure and fractional dimension. This report describes in detail the experimental techniques and analyses of the spatiotemporal diffusive evolution of ground motion observed at the in-ground tunnel of the injector linac using a real-time remote-controllable sensing system.

A graph-based approach to detect spatiotemporal dynamics in satellite image time series

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Guttler, Fabio; Ienco, Dino; Nin, Jordi; Teisseire, Maguelonne; Poncelet, Pascal

2017-08-01

Enhancing the frequency of satellite acquisitions represents a key issue for Earth Observation community nowadays. Repeated observations are crucial for monitoring purposes, particularly when intra-annual process should be taken into account. Time series of images constitute a valuable source of information in these cases. The goal of this paper is to propose a new methodological framework to automatically detect and extract spatiotemporal information from satellite image time series (SITS). Existing methods dealing with such kind of data are usually classification-oriented and cannot provide information about evolutions and temporal behaviors. In this paper we propose a graph-based strategy that combines object-based image analysis (OBIA) with data mining techniques. Image objects computed at each individual timestamp are connected across the time series and generates a set of evolution graphs. Each evolution graph is associated to a particular area within the study site and stores information about its temporal evolution. Such information can be deeply explored at the evolution graph scale or used to compare the graphs and supply a general picture at the study site scale. We validated our framework on two study sites located in the South of France and involving different types of natural, semi-natural and agricultural areas. The results obtained from a Landsat SITS support the quality of the methodological approach and illustrate how the framework can be employed to extract and characterize spatiotemporal dynamics.

Indeterminacy and Spatiotemporal Data

DEFF Research Database (Denmark)

Pfoser, D.; Tryfona, N.; Jensen, Christian Søndergaard

2005-01-01

For some spatiotemporal applications, it can be assumed that the modeled world is precise and bounded, and that our record of it is precise. While these simplifying assumptions are sufficient in applications like a land information system, they are unnecessarily crude for many other applications...

Spatio-temporal Characteristics of Land Use Land Cover Change Driven by Large Scale Land Transactions in Cambodia

Science.gov (United States)

Ghosh, A.; Smith, J. C.; Hijmans, R. J.

2017-12-01

Since mid-1990s, the Cambodian government granted nearly 300 `Economic Land Concessions' (ELCs), occupying approximately 2.3 million ha to foreign and domestic organizations (primarily agribusinesses). The majority of Cambodian ELC deals have been issued in areas of both relatively low population density and low agricultural productivity, dominated by smallholder production. These regions often contain highly biodiverse areas, thereby increasing the ecological cost associated with land clearing for extractive purposes. These large-scale land transactions have also resulted in substantial and rapid changes in land-use patterns and agriculture practices by smallholder farmers. In this study, we investigated the spatio-temporal characteristics of land use change associated with large-scale land transactions across Cambodia using multi-temporal multi-reolution remote sensing data. We identified major regions of deforestation during the last two decades using Landsat archive, global forest change data (2000-2014) and georeferenced database of ELC deals. We then mapped the deforestation and land clearing within ELC boundaries as well as areas bordering or near ELCs to quantify the impact of ELCs on local communities. Using time-series from MODIS Vegetation Indices products for the study period, we also estimated the time period over which any particular ELC deal initiated its proposed activity. We found evidence of similar patterns of land use change outside the boundaries of ELC deals which may be associated with i) illegal land encroachments by ELCs and/or ii) new agricultural practices adopted by local farmers near ELC boundaries. We also detected significant time gaps between ELC deal granting dates and initiation of land clearing for ELC purposes. Interestingly, we also found that not all designated areas for ELCs were put into effect indicating the possible proliferation of speculative land deals. This study demonstrates the potential of remote sensing techniques

Validating spatiotemporal predictions of an important pest of small grains.

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Merrill, Scott C; Holtzer, Thomas O; Peairs, Frank B; Lester, Philip J

2015-01-01

Arthropod pests are typically managed using tactics applied uniformly to the whole field. Precision pest management applies tactics under the assumption that within-field pest pressure differences exist. This approach allows for more precise and judicious use of scouting resources and management tactics. For example, a portion of a field delineated as attractive to pests may be selected to receive extra monitoring attention. Likely because of the high variability in pest dynamics, little attention has been given to developing precision pest prediction models. Here, multimodel synthesis was used to develop a spatiotemporal model predicting the density of a key pest of wheat, the Russian wheat aphid, Diuraphis noxia (Kurdjumov). Spatially implicit and spatially explicit models were synthesized to generate spatiotemporal pest pressure predictions. Cross-validation and field validation were used to confirm model efficacy. A strong within-field signal depicting aphid density was confirmed with low prediction errors. Results show that the within-field model predictions will provide higher-quality information than would be provided by traditional field scouting. With improvements to the broad-scale model component, the model synthesis approach and resulting tool could improve pest management strategy and provide a template for the development of spatially explicit pest pressure models. © 2014 Society of Chemical Industry.

Dynamical topology and statistical properties of spatiotemporal chaos.

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Zhuang, Quntao; Gao, Xun; Ouyang, Qi; Wang, Hongli

2012-12-01

For spatiotemporal chaos described by partial differential equations, there are generally locations where the dynamical variable achieves its local extremum or where the time partial derivative of the variable vanishes instantaneously. To a large extent, the location and movement of these topologically special points determine the qualitative structure of the disordered states. We analyze numerically statistical properties of the topologically special points in one-dimensional spatiotemporal chaos. The probability distribution functions for the number of point, the lifespan, and the distance covered during their lifetime are obtained from numerical simulations. Mathematically, we establish a probabilistic model to describe the dynamics of these topologically special points. In spite of the different definitions in different spatiotemporal chaos, the dynamics of these special points can be described in a uniform approach.

Spatiotemporal Psychopathology II: How does a psychopathology of the brain's resting state look like? Spatiotemporal approach and the history of psychopathology.

Science.gov (United States)

Northoff, Georg

2016-01-15

Psychopathology as the investigation and classification of experience, behavior and symptoms in psychiatric patients is an old discipline that ranges back to the end of the 19th century. Since then different approaches to psychopathology have been suggested. Recent investigations showing abnormalities in the brain on different levels raise the question how the gap between brain and psyche, between neural abnormalities and alteration in experience and behavior can be bridged. Historical approaches like descriptive (Jaspers) and structural (Minkoswki) psychopathology as well as the more current phenomenological psychopathology (Paarnas, Fuchs, Sass, Stanghellini) remain on the side of the psyche giving detailed description of the phenomenal level of experience while leaving open the link to the brain. In contrast, the recently introduced Research Domain Classification (RDoC) aims at explicitly linking brain and psyche by starting from so-called 'neuro-behavioral constructs'. How does Spatiotemporal Psychopathology, as demonstrated in the first paper on depression, stand in relation to these approaches? In a nutshell, Spatiotemporal Psychopathology aims to bridge the gap between brain and psyche. Specifically, as demonstrated in depression in the first paper, the focus is on the spatiotemporal features of the brain's intrinsic activity and how they are transformed into corresponding spatiotemporal features in experience on the phenomenal level and behavioral changes, which can well account for the symptoms in these patients. This second paper focuses on some of the theoretical background assumptions in Spatiotemporal Psychopathology by directly comparing it to descriptive, structural, and phenomenological psychopathology as well as to RDoC. Copyright © 2015 Elsevier B.V. All rights reserved.

Building spatio-temporal database model based on ontological approach using relational database environment

International Nuclear Information System (INIS)

Mahmood, N.; Burney, S.M.A.

2017-01-01

Everything in this world is encapsulated by space and time fence. Our daily life activities are utterly linked and related with other objects in vicinity. Therefore, a strong relationship exist with our current location, time (including past, present and future) and event through with we are moving as an object also affect our activities in life. Ontology development and its integration with database are vital for the true understanding of the complex systems involving both spatial and temporal dimensions. In this paper we propose a conceptual framework for building spatio-temporal database model based on ontological approach. We have used relational data model for modelling spatio-temporal data content and present our methodology with spatio-temporal ontological accepts and its transformation into spatio-temporal database model. We illustrate the implementation of our conceptual model through a case study related to cultivated land parcel used for agriculture to exhibit the spatio-temporal behaviour of agricultural land and related entities. Moreover, it provides a generic approach for designing spatiotemporal databases based on ontology. The proposed model is capable to understand the ontological and somehow epistemological commitments and to build spatio-temporal ontology and transform it into a spatio-temporal data model. Finally, we highlight the existing and future research challenges. (author)

A multi-scale approach of fluvial biogeomorphic dynamics using photogrammetry.

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Hortobágyi, Borbála; Corenblit, Dov; Vautier, Franck; Steiger, Johannes; Roussel, Erwan; Burkart, Andreas; Peiry, Jean-Luc

2017-11-01

Over the last twenty years, significant technical advances turned photogrammetry into a relevant tool for the integrated analysis of biogeomorphic cross-scale interactions within vegetated fluvial corridors, which will largely contribute to the development and improvement of self-sustainable river restoration efforts. Here, we propose a cost-effective, easily reproducible approach based on stereophotogrammetry and Structure from Motion (SfM) technique to study feedbacks between fluvial geomorphology and riparian vegetation at different nested spatiotemporal scales. We combined different photogrammetric methods and thus were able to investigate biogeomorphic feedbacks at all three spatial scales (i.e., corridor, alluvial bar and micro-site) and at three different temporal scales, i.e., present, recent past and long term evolution on a diversified riparian landscape mosaic. We evaluate the performance and the limits of photogrammetric methods by targeting a set of fundamental parameters necessary to study biogeomorphic feedbacks at each of the three nested spatial scales and, when possible, propose appropriate solutions. The RMSE varies between 0.01 and 2 m depending on spatial scale and photogrammetric methods. Despite some remaining difficulties to properly apply them with current technologies under all circumstances in fluvial biogeomorphic studies, e.g. the detection of vegetation density or landform topography under a dense vegetation canopy, we suggest that photogrammetry is a promising instrument for the quantification of biogeomorphic feedbacks at nested spatial scales within river systems and for developing appropriate river management tools and strategies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Length scale hierarchy and spatiotemporal change of alluvial morphologies over the Selenga River delta, Russia

Science.gov (United States)

Dong, T. Y.; Nittrouer, J.; McElroy, B. J.; Ma, H.; Czapiga, M. J.; Il'icheva, E.; Pavlov, M.; Parker, G.

2017-12-01

The movement of water and sediment in natural channels creates various types of alluvial morphologies that span length scales from dunes to deltas. The behavior of these morphologies is controlled microscopically by hydrodynamic conditions and bed material size, and macroscopically by hydrologic and geological settings. Alluvial morphologies can be modeled as either diffusive or kinematic waves, in accordance with their respective boundary conditions. Recently, it has been shown that the difference between these two dynamic behaviors of alluvial morphologies can be characterized by the backwater number, which is a dimensionless value normalizing the length scale of a morphological feature to its local hydrodynamic condition. Application of the backwater number has proven useful for evaluating the size of morphologies, including deltas (e.g., by assessing the preferential avulsion location of a lobe), and for comparing bedform types across different fluvial systems. Yet two critical questions emerge when applying the backwater number: First, how do different types of alluvial morphologies compare within a single deltaic system, where there is a hydrodynamic transition from uniform to non-uniform flow? Second, how do different types of morphologies evolve temporally within a system as a function of changing water discharge? This study addresses these questions by compiling and analyzing field data from the Selenga River delta, Russia, which include measurements of flow velocity, channel geometry, bed material grain size, and channel slope, as well as length scales of various morphologies, including dunes, island bars, meanders, bifurcations, and delta lobes. Data analyses reveal that the length scale of morphologies decrease and the backwater number increases as flow transitions from uniform to non-uniform conditions progressing downstream. It is shown that the evaluated length scale hierarchy and planform distribution of different morphologies can be used to

Regulation of Spatiotemporal Patterns by Biological Variability: General Principles and Applications to Dictyostelium discoideum.

Directory of Open Access Journals (Sweden)

Miriam Grace

2015-11-01

Full Text Available Spatiotemporal patterns often emerge from local interactions in a self-organizing fashion. In biology, the resulting patterns are also subject to the influence of the systematic differences between the system's constituents (biological variability. This regulation of spatiotemporal patterns by biological variability is the topic of our review. We discuss several examples of correlations between cell properties and the self-organized spatiotemporal patterns, together with their relevance for biology. Our guiding, illustrative example will be spiral waves of cAMP in a colony of Dictyostelium discoideum cells. Analogous processes take place in diverse situations (such as cardiac tissue, where spiral waves occur in potentially fatal ventricular fibrillation so a deeper understanding of this additional layer of self-organized pattern formation would be beneficial to a wide range of applications. One of the most striking differences between pattern-forming systems in physics or chemistry and those in biology is the potential importance of variability. In the former, system components are essentially identical with random fluctuations determining the details of the self-organization process and the resulting patterns. In biology, due to variability, the properties of potentially very few cells can have a driving influence on the resulting asymptotic collective state of the colony. Variability is one means of implementing a few-element control on the collective mode. Regulatory architectures, parameters of signaling cascades, and properties of structure formation processes can be "reverse-engineered" from observed spatiotemporal patterns, as different types of regulation and forms of interactions between the constituents can lead to markedly different correlations. The power of this biology-inspired view of pattern formation lies in building a bridge between two scales: the patterns as a collective state of a very large number of cells on the one hand

Integrating movement ecology with biodiversity research - exploring new avenues to address spatiotemporal biodiversity dynamics.

Science.gov (United States)

Jeltsch, Florian; Bonte, Dries; Pe'er, Guy; Reineking, Björn; Leimgruber, Peter; Balkenhol, Niko; Schröder, Boris; Buchmann, Carsten M; Mueller, Thomas; Blaum, Niels; Zurell, Damaris; Böhning-Gaese, Katrin; Wiegand, Thorsten; Eccard, Jana A; Hofer, Heribert; Reeg, Jette; Eggers, Ute; Bauer, Silke

2013-01-01

Movement of organisms is one of the key mechanisms shaping biodiversity, e.g. the distribution of genes, individuals and species in space and time. Recent technological and conceptual advances have improved our ability to assess the causes and consequences of individual movement, and led to the emergence of the new field of 'movement ecology'. Here, we outline how movement ecology can contribute to the broad field of biodiversity research, i.e. the study of processes and patterns of life among and across different scales, from genes to ecosystems, and we propose a conceptual framework linking these hitherto largely separated fields of research. Our framework builds on the concept of movement ecology for individuals, and demonstrates its importance for linking individual organismal movement with biodiversity. First, organismal movements can provide 'mobile links' between habitats or ecosystems, thereby connecting resources, genes, and processes among otherwise separate locations. Understanding these mobile links and their impact on biodiversity will be facilitated by movement ecology, because mobile links can be created by different modes of movement (i.e., foraging, dispersal, migration) that relate to different spatiotemporal scales and have differential effects on biodiversity. Second, organismal movements can also mediate coexistence in communities, through 'equalizing' and 'stabilizing' mechanisms. This novel integrated framework provides a conceptual starting point for a better understanding of biodiversity dynamics in light of individual movement and space-use behavior across spatiotemporal scales. By illustrating this framework with examples, we argue that the integration of movement ecology and biodiversity research will also enhance our ability to conserve diversity at the genetic, species, and ecosystem levels.

Cartography in the Age of Spatio-temporal Big Data

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WANG Jiayao

2017-10-01

Full Text Available Cartography is an ancient science with almost the same long history as the world's oldest culture.Since ancient times,the movement and change of anything and any phenomena,including human activities,have been carried out in a certain time and space.The development of science and technology and the progress of social civilization have made social management and governance more and more dependent on time and space.The information source,theme,content,carrier,form,production methods and application methods of map are different in different historical periods,so that its all-round value is different. With the arrival of the big data age,the scientific paradigm has now entered the era of "data-intensive" paradigm,so is the cartography,with obvious characteristics of big data science.All big data are caused by movement and change of all things and phenomena in the geographic world,so they have space and time characteristics and thus cannot be separated from the spatial reference and time reference.Therefore,big data is big spatio-temporal data essentially.Since the late 1950s and early 1960s,modern cartography,that is,the cartography in the information age,takes spatio-temporal data as the object,and focuses on the processing and expression of spatio-temporal data,but not in the face of the large scale multi-source heterogeneous and multi-dimensional dynamic data flow(or flow datafrom sky to the sea.The real-time dynamic nature,the theme pertinence,the content complexity,the carrier diversification,the expression form personalization,the production method modernization,the application ubiquity of the map,is incomparable in the past period,which leads to the great changes of the theory,technology and application system of cartography.And all these changes happen to occur in the 60 years since the late 1950s and early 1960s,so this article was written to commemorate the 60th anniversary of the "Acta Geodaetica et Cartographica Sinica".

Modelling spatio-temporal variability of Mytilus edulis (L.) growth by forcing a dynamic energy budget model with satellite-derived environmental data

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Thomas, Yoann; Mazurié, Joseph; Alunno-Bruscia, Marianne; Bacher, Cédric; Bouget, Jean-François; Gohin, Francis; Pouvreau, Stéphane; Struski, Caroline

2011-11-01

In order to assess the potential of various marine ecosystems for shellfish aquaculture and to evaluate their carrying capacities, there is a need to clarify the response of exploited species to environmental variations using robust ecophysiological models and available environmental data. For a large range of applications and comparison purposes, a non-specific approach based on 'generic' individual growth models offers many advantages. In this context, we simulated the response of blue mussel ( Mytilus edulis L.) to the spatio-temporal fluctuations of the environment in Mont Saint-Michel Bay (North Brittany) by forcing a generic growth model based on Dynamic Energy Budgets with satellite-derived environmental data (i.e. temperature and food). After a calibration step based on data from mussel growth surveys, the model was applied over nine years on a large area covering the entire bay. These simulations provide an evaluation of the spatio-temporal variability in mussel growth and also show the ability of the DEB model to integrate satellite-derived data and to predict spatial and temporal growth variability of mussels. Observed seasonal, inter-annual and spatial growth variations are well simulated. The large-scale application highlights the strong link between food and mussel growth. The methodology described in this study may be considered as a suitable approach to account for environmental effects (food and temperature variations) on physiological responses (growth and reproduction) of filter feeders in varying environments. Such physiological responses may then be useful for evaluating the suitability of coastal ecosystems for shellfish aquaculture.

Exploring the Spatiotemporal Organization of Membrane Proteins in Living Plant Cells.

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Wang, Li; Xue, Yiqun; Xing, Jingjing; Song, Kai; Lin, Jinxing

2018-04-29

Plasma membrane proteins have important roles in transport and signal transduction. Deciphering the spatiotemporal organization of these proteins provides crucial information for elucidating the links between the behaviors of different molecules. However, monitoring membrane proteins without disrupting their membrane environment remains difficult. Over the past decade, many studies have developed single-molecule techniques, opening avenues for probing the stoichiometry and interactions of membrane proteins in their native environment by providing nanometer-scale spatial information and nanosecond-scale temporal information. In this review, we assess recent progress in the development of labeling and imaging technology for membrane protein analysis. We focus in particular on several single-molecule techniques for quantifying the dynamics and assembly of membrane proteins. Finally, we provide examples of how these new techniques are advancing our understanding of the complex biological functions of membrane proteins.

Spatio-temporal databases complex motion pattern queries

CERN Document Server

Vieira, Marcos R

2013-01-01

This brief presents several new query processing techniques, called complex motion pattern queries, specifically designed for very large spatio-temporal databases of moving objects. The brief begins with the definition of flexible pattern queries, which are powerful because of the integration of variables and motion patterns. This is followed by a summary of the expressive power of patterns and flexibility of pattern queries. The brief then present the Spatio-Temporal Pattern System (STPS) and density-based pattern queries. STPS databases contain millions of records with information about mobi

Spatio-Temporal Multiway Data Decomposition Using Principal Tensor Analysis on k-Modes: The R Package PTAk

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Didier G. Leibovici

2010-10-01

Full Text Available The purpose of this paper is to describe the R package {PTAk and how the spatio-temporal context can be taken into account in the analyses. Essentially PTAk( is a multiway multidimensional method to decompose a multi-entries data-array, seen mathematically as a tensor of any order. This PTAk-modes method proposes a way of generalizing SVD (singular value decomposition, as well as some other well known methods included in the R package, such as PARAFAC or CANDECOMP and the PCAn-modes or Tucker-n model. The example datasets cover different domains with various spatio-temporal characteristics and issues: (i~medical imaging in neuropsychology with a functional MRI (magnetic resonance imaging study, (ii~pharmaceutical research with a pharmacodynamic study with EEG (electro-encephaloegraphic data for a central nervous system (CNS drug, and (iii~geographical information system (GIS with a climatic dataset that characterizes arid and semi-arid variations. All the methods implemented in the R package PTAk also support non-identity metrics, as well as penalizations during the optimization process. As a result of these flexibilities, together with pre-processing facilities, PTAk constitutes a framework for devising extensions of multidimensional methods such ascorrespondence analysis, discriminant analysis, and multidimensional scaling, also enabling spatio-temporal constraints.

Challenges in modelling spatiotemporally varying phytoplankton blooms in the Northwestern Arabian Sea and Gulf of Oman

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Sedigh Marvasti, S.; Gnanadesikan, A.; Bidokhti, A. A.; Dunne, J. P.; Ghader, S.

2015-07-01

We examine interannual variability of phytoplankton blooms in northwestern Arabian Sea and Gulf of Oman. Satellite data (SeaWIFS ocean color) shows two climatological blooms in this region, a wintertime bloom peaking in February and a summertime bloom peaking in September. A pronounced anti-correlation between the AVISO sea surface height anomaly (SSHA) and chlorophyll is found during the wintertime bloom. On a regional scale, interannual variability of the wintertime bloom is thus dominated by cyclonic eddies which vary in location from one year to another. These results were compared against the outputs from three different 3-D Earth System models. We show that two coarse (1°) models with the relatively complex biogeochemistry (TOPAZ) capture the annual cycle but neither eddies nor the interannual variability. An eddy-resolving model (GFDL CM2.6) with a simpler biogeochemistry (miniBLING) displays larger interannual variability, but overestimates the wintertime bloom and captures eddy-bloom coupling in the south but not in the north. The southern part of the domain is a region with a much sharper thermocline and nutricline relatively close to the surface, in which eddies modulate diffusive nutrient supply to the surface (a mechanism not previously emphasized in the literature). We suggest that for the model to simulate the observed wintertime blooms within cyclones, it will be necessary to represent this relatively unusual nutrient structure as well as the cyclonic eddies. This is a challenge in the Northern Arabian Sea as it requires capturing the details of the outflow from the Persian Gulf.

Hierarchical Distributed-Lag Models: Exploring Varying Geographic Scale and Magnitude in Associations Between the Built Environment and Health.

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Baek, Jonggyu; Sanchez-Vaznaugh, Emma V; Sánchez, Brisa N

2016-03-15

It is well known that associations between features of the built environment and health depend on the geographic scale used to construct environmental attributes. In the built environment literature, it has long been argued that geographic scales may vary across study locations. However, this hypothesized variation has not been systematically examined due to a lack of available statistical methods. We propose a hierarchical distributed-lag model (HDLM) for estimating the underlying overall shape of food environment-health associations as a function of distance from locations of interest. This method enables indirect assessment of relevant geographic scales and captures area-level heterogeneity in the magnitudes of associations, along with relevant distances within areas. The proposed model was used to systematically examine area-level variation in the association between availability of convenience stores around schools and children's weights. For this case study, body mass index (weight kg)/height (m)2) z scores (BMIz) for 7th grade children collected via California's 2001-2009 FitnessGram testing program were linked to a commercial database that contained locations of food outlets statewide. Findings suggested that convenience store availability may influence BMIz only in some places and at varying distances from schools. Future research should examine localized environmental or policy differences that may explain the heterogeneity in convenience store-BMIz associations. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Onset of meso-scale turbulence in active nematics

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Doostmohammadi, A.; Shendruk, T.N.; Thijssen, K.; Yeomans, J.M.

2017-01-01

Meso-scale turbulence is an innate phenomenon, distinct from inertial turbulence, that spontaneously occurs at low Reynolds number in fluidized biological systems. This spatiotemporal disordered flow radically changes nutrient and molecular transport in living fluids and can strongly affect the

Comparison of Spatiotemporal Mapping Techniques for Enormous Etl and Exploitation Patterns

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Deiotte, R.; La Valley, R.

2017-10-01

The need to extract, transform, and exploit enormous volumes of spatiotemporal data has exploded with the rise of social media, advanced military sensors, wearables, automotive tracking, etc. However, current methods of spatiotemporal encoding and exploitation simultaneously limit the use of that information and increase computing complexity. Current spatiotemporal encoding methods from Niemeyer and Usher rely on a Z-order space filling curve, a relative of Peano's 1890 space filling curve, for spatial hashing and interleaving temporal hashes to generate a spatiotemporal encoding. However, there exist other space-filling curves, and that provide different manifold coverings that could promote better hashing techniques for spatial data and have the potential to map spatiotemporal data without interleaving. The concatenation of Niemeyer's and Usher's techniques provide a highly efficient space-time index. However, other methods have advantages and disadvantages regarding computational cost, efficiency, and utility. This paper explores the several methods using a range of sizes of data sets from 1K to 10M observations and provides a comparison of the methods.

COMPARISON OF SPATIOTEMPORAL MAPPING TECHNIQUES FOR ENORMOUS ETL AND EXPLOITATION PATTERNS

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R. Deiotte

2017-10-01

Full Text Available The need to extract, transform, and exploit enormous volumes of spatiotemporal data has exploded with the rise of social media, advanced military sensors, wearables, automotive tracking, etc. However, current methods of spatiotemporal encoding and exploitation simultaneously limit the use of that information and increase computing complexity. Current spatiotemporal encoding methods from Niemeyer and Usher rely on a Z-order space filling curve, a relative of Peano’s 1890 space filling curve, for spatial hashing and interleaving temporal hashes to generate a spatiotemporal encoding. However, there exist other space-filling curves, and that provide different manifold coverings that could promote better hashing techniques for spatial data and have the potential to map spatiotemporal data without interleaving. The concatenation of Niemeyer’s and Usher’s techniques provide a highly efficient space-time index. However, other methods have advantages and disadvantages regarding computational cost, efficiency, and utility. This paper explores the several methods using a range of sizes of data sets from 1K to 10M observations and provides a comparison of the methods.

A spatiotemporal mixed model to assess the influence of environmental and socioeconomic factors on the incidence of hand, foot and mouth disease

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Lianfa Li

2018-02-01

Full Text Available Abstract Background As a common infectious disease, hand, foot and mouth disease (HFMD is affected by multiple environmental and socioeconomic factors, and its pathogenesis is complex. Furthermore, the transmission of HFMD is characterized by strong spatial clustering and autocorrelation, and the classical statistical approach may be biased without consideration of spatial autocorrelation. In this paper, we propose to embed spatial characteristics into a spatiotemporal additive model to improve HFMD incidence assessment. Methods Using incidence data (6439 samples from 137 monitoring district for Shandong Province, China, along with meteorological, environmental and socioeconomic spatial and spatiotemporal covariate data, we proposed a spatiotemporal mixed model to estimate HFMD incidence. Geo-additive regression was used to model the non-linear effects of the covariates on the incidence risk of HFMD in univariate and multivariate models. Furthermore, the spatial effect was constructed to capture spatial autocorrelation at the sub-regional scale, and clusters (hotspots of high risk were generated using spatiotemporal scanning statistics as a predictor. Linear and non-linear effects were compared to illustrate the usefulness of non-linear associations. Patterns of spatial effects and clusters were explored to illustrate the variation of the HFMD incidence across geographical sub-regions. To validate our approach, 10-fold cross-validation was conducted. Results The results showed that there were significant non-linear associations of the temporal index, spatiotemporal meteorological factors and spatial environmental and socioeconomic factors with HFMD incidence. Furthermore, there were strong spatial autocorrelation and clusters for the HFMD incidence. Spatiotemporal meteorological parameters, the normalized difference vegetation index (NDVI, the temporal index, spatiotemporal clustering and spatial effects played important roles as predictors in

Research on spatio-temporal database techniques for spatial information service

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Zhao, Rong; Wang, Liang; Li, Yuxiang; Fan, Rongshuang; Liu, Ping; Li, Qingyuan

2007-06-01

Geographic data should be described by spatial, temporal and attribute components, but the spatio-temporal queries are difficult to be answered within current GIS. This paper describes research into the development and application of spatio-temporal data management system based upon GeoWindows GIS software platform which was developed by Chinese Academy of Surveying and Mapping (CASM). Faced the current and practical requirements of spatial information application, and based on existing GIS platform, one kind of spatio-temporal data model which integrates vector and grid data together was established firstly. Secondly, we solved out the key technique of building temporal data topology, successfully developed a suit of spatio-temporal database management system adopting object-oriented methods. The system provides the temporal data collection, data storage, data management and data display and query functions. Finally, as a case study, we explored the application of spatio-temporal data management system with the administrative region data of multi-history periods of China as the basic data. With all the efforts above, the GIS capacity of management and manipulation in aspect of time and attribute of GIS has been enhanced, and technical reference has been provided for the further development of temporal geographic information system (TGIS).

An evaluation of space time cube representation of spatiotemporal patterns.

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Kristensson, Per Ola; Dahlbäck, Nils; Anundi, Daniel; Björnstad, Marius; Gillberg, Hanna; Haraldsson, Jonas; Mårtensson, Ingrid; Nordvall, Mathias; Ståhl, Josefine

2009-01-01

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.

Simulating smoke transport from wildland fires with a regional-scale air quality model: sensitivity to spatiotemporal allocation of fire emissions.

Science.gov (United States)

Garcia-Menendez, Fernando; Hu, Yongtao; Odman, Mehmet T

2014-09-15

Air quality forecasts generated with chemical transport models can provide valuable information about the potential impacts of fires on pollutant levels. However, significant uncertainties are associated with fire-related emission estimates as well as their distribution on gridded modeling domains. In this study, we explore the sensitivity of fine particulate matter concentrations predicted by a regional-scale air quality model to the spatial and temporal allocation of fire emissions. The assessment was completed by simulating a fire-related smoke episode in which air quality throughout the Atlanta metropolitan area was affected on February 28, 2007. Sensitivity analyses were carried out to evaluate the significance of emission distribution among the model's vertical layers, along the horizontal plane, and into hourly inputs. Predicted PM2.5 concentrations were highly sensitive to emission injection altitude relative to planetary boundary layer height. Simulations were also responsive to the horizontal allocation of fire emissions and their distribution into single or multiple grid cells. Additionally, modeled concentrations were greatly sensitive to the temporal distribution of fire-related emissions. The analyses demonstrate that, in addition to adequate estimates of emitted mass, successfully modeling the impacts of fires on air quality depends on an accurate spatiotemporal allocation of emissions. Copyright © 2014 Elsevier B.V. All rights reserved.

Towards large scale stochastic rainfall models for flood risk assessment in trans-national basins

Science.gov (United States)

Serinaldi, F.; Kilsby, C. G.

2012-04-01

While extensive research has been devoted to rainfall-runoff modelling for risk assessment in small and medium size watersheds, less attention has been paid, so far, to large scale trans-national basins, where flood events have severe societal and economic impacts with magnitudes quantified in billions of Euros. As an example, in the April 2006 flood events along the Danube basin at least 10 people lost their lives and up to 30 000 people were displaced, with overall damages estimated at more than half a billion Euros. In this context, refined analytical methods are fundamental to improve the risk assessment and, then, the design of structural and non structural measures of protection, such as hydraulic works and insurance/reinsurance policies. Since flood events are mainly driven by exceptional rainfall events, suitable characterization and modelling of space-time properties of rainfall fields is a key issue to perform a reliable flood risk analysis based on alternative precipitation scenarios to be fed in a new generation of large scale rainfall-runoff models. Ultimately, this approach should be extended to a global flood risk model. However, as the need of rainfall models able to account for and simulate spatio-temporal properties of rainfall fields over large areas is rather new, the development of new rainfall simulation frameworks is a challenging task involving that faces with the problem of overcoming the drawbacks of the existing modelling schemes (devised for smaller spatial scales), but keeping the desirable properties. In this study, we critically summarize the most widely used approaches for rainfall simulation. Focusing on stochastic approaches, we stress the importance of introducing suitable climate forcings in these simulation schemes in order to account for the physical coherence of rainfall fields over wide areas. Based on preliminary considerations, we suggest a modelling framework relying on the Generalized Additive Models for Location, Scale

Rainfall spatiotemporal variability relation to wetlands hydroperiods

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Serrano-Hidalgo, Carmen; Guardiola-Albert, Carolina; Fernandez-Naranjo, Nuria

2017-04-01

Doñana natural space (Southwestern Spain) is one of the largest protected wetlands in Europe. The wide marshes present in this natural space have such ecological value that this wetland has been declared a Ramsar reserve in 1982. Apart from the extensive marsh, there are also small lagoons and seasonally flooded areas which are likewise essential to maintain a wide variety of valuable habitats. Hydroperiod, the length of time each point remains flooded along an annual cycle, is a critical ecological parameter that shapes aquatic plants and animals distribution and determines available habitat for many of the living organisms in the marshes. Recently, there have been published two different works estimating the hydroperiod of Doñana lagoons with Landsat Time Series images (Cifuentes et al., 2015; Díaz-Delgado et al., 2016). In both works the flooding cycle hydroperiod in Doñana marshes reveals a flooding regime mainly driven by rainfall, evapotranspiration, topography and local hydrological management actions. The correlation found between rainfall and hydroperiod is studied differently in both works. While in one the rainfall is taken from one raingauge (Cifuentes et al., 2015), the one performed by Díaz-Delgado (2016) uses annual rainfall maps interpolated with the inverse of the distance method. The rainfall spatiotemporal variability in this area can be highly significant; however the amount of this importance has not been quantified at the moment. In the present work the geostatistical tool known as spatiotemporal variogram is used to study the rainfall spatiotemporal variability. The spacetime package implemented in R (Pebesma, 2012) facilities its computation from a high rainfall data base of more than 100 raingauges from 1950 to 2016. With the aid of these variograms the rainfall spatiotemporal variability is quantified. The principal aim of the present work is the study of the relation between the rainfall spatiotemporal variability and the

Stability Switches, Hopf Bifurcations, and Spatio-temporal Patterns in a Delayed Neural Model with Bidirectional Coupling

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Song, Yongli; Zhang, Tonghua; Tadé, Moses O.

2009-12-01

The dynamical behavior of a delayed neural network with bi-directional coupling is investigated by taking the delay as the bifurcating parameter. Some parameter regions are given for conditional/absolute stability and Hopf bifurcations by using the theory of functional differential equations. As the propagation time delay in the coupling varies, stability switches for the trivial solution are found. Conditions ensuring the stability and direction of the Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. We also discuss the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay differential equations combined with representation theory of Lie groups. In particular, we obtain that the spatio-temporal patterns of bifurcating periodic oscillations will alternate according to the change of the propagation time delay in the coupling, i.e., different ranges of delays correspond to different patterns of neural activities. Numerical simulations are given to illustrate the obtained results and show the existence of bursts in some interval of the time for large enough delay.

High Spatio-Temporal Resolution Bathymetry Estimation and Morphology

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Bergsma, E. W. J.; Conley, D. C.; Davidson, M. A.; O'Hare, T. J.

2015-12-01

In recent years, bathymetry estimates using video images have become increasingly accurate. With the cBathy code (Holman et al., 2013) fully operational, bathymetry results with 0.5 metres accuracy have been regularly obtained at Duck, USA. cBathy is based on observations of the dominant frequencies and wavelengths of surface wave motions and estimates the depth (and hence allows inference of bathymetry profiles) based on linear wave theory. Despite the good performance at Duck, large discrepancies were found related to tidal elevation and camera height (Bergsma et al., 2014) and on the camera boundaries. A tide dependent floating pixel and camera boundary solution have been proposed to overcome these issues (Bergsma et al., under review). The video-data collection is set estimate depths hourly on a grid with resolution in the order of 10x25 meters. Here, the application of the cBathy at Porthtowan in the South-West of England is presented. Hourly depth estimates are combined and analysed over a period of 1.5 years (2013-2014). In this work the focus is on the sub-tidal region, where the best cBathy results are achieved. The morphology of the sub-tidal bar is tracked with high spatio-temporal resolution on short and longer time scales. Furthermore, the impact of the storm and reset (sudden and large changes in bathymetry) of the sub-tidal area is clearly captured with the depth estimations. This application shows that the high spatio-temporal resolution of cBathy makes it a powerful tool for coastal research and coastal zone management.

Spatiotemporal Data Mining, Analysis, and Visualization of Human Activity Data

Science.gov (United States)

Li, Xun

2012-01-01

This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data…

Markovian Limit of a Spatio-Temporal Correlated Open Systems

Science.gov (United States)

Monnai, T.

Large fluctuation of Brownian particles is affected by the finiteness of the correlation length of the background noise field. Indeed a Fokker—Planck equation is derived in a Markovian limit of a spatio-temporal short correlated noise. Corresponding kinetic quantities are renormalized due to the spatio-temporal memory. We also investigate the case of open system by connecting a thermostat to the system.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Directory of Open Access Journals (Sweden)

Haiyang Yu

2017-06-01

Full Text Available Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs, for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs and long short-term memory (LSTM neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

Science.gov (United States)

Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

2017-06-26

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Spatio-Temporal Variation and Prediction of Ischemic Heart Disease Hospitalizations in Shenzhen, China

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Yanxia Wang

2014-05-01

Full Text Available Ischemic heart disease (IHD is a leading cause of death worldwide. Urban public health and medical management in Shenzhen, an international city in the developing country of China, is challenged by an increasing burden of IHD. This study analyzed the spatio-temporal variation of IHD hospital admissions from 2003 to 2012 utilizing spatial statistics, spatial analysis, and space-time scan statistics. The spatial statistics and spatial analysis measured the incidence rate (hospital admissions per 1,000 residents and the standardized rate (the observed cases standardized by the expected cases of IHD at the district level to determine the spatio-temporal distribution and identify patterns of change. The space-time scan statistics was used to identify spatio-temporal clusters of IHD hospital admissions at the district level. The other objective of this study was to forecast the IHD hospital admissions over the next three years (2013–2015 to predict the IHD incidence rates and the varying burdens of IHD-related medical services among the districts in Shenzhen. The results show that the highest hospital admissions, incidence rates, and standardized rates of IHD are in Futian. From 2003 to 2012, the IHD hospital admissions exhibited similar mean centers and directional distributions, with a slight increase in admissions toward the north in accordance with the movement of the total population. The incidence rates of IHD exhibited a gradual increase from 2003 to 2012 for all districts in Shenzhen, which may be the result of the rapid development of the economy and the increasing traffic pollution. In addition, some neighboring areas exhibited similar temporal change patterns, which were also detected by the spatio-temporal cluster analysis. Futian and Dapeng would have the highest and the lowest hospital admissions, respectively, although these districts have the highest incidence rates among all of the districts from 2013 to 2015 based on the prediction

Spatio-temporal dynamics of ocean conditions and forage taxa reveal regional structuring of seabird–prey relationships.

Science.gov (United States)

Santora, Jarrod A; Schroeder, Isaac D; Field, John C; Wells, Brian K; Sydeman, William J

Studies of predator–prey demographic responses and the physical drivers of such relationships are rare, yet essential for predicting future changes in the structure and dynamics of marine ecosystems. Here, we hypothesize that predator–prey relationships vary spatially in association with underlying physical ocean conditions, leading to observable changes in demographic rates, such as reproduction. To test this hypothesis, we quantified spatio-temporal variability in hydrographic conditions, krill, and forage fish to model predator (seabird) demographic responses over 18 years (1990–2007). We used principal component analysis and spatial correlation maps to assess coherence among ocean conditions, krill, and forage fish, and generalized additive models to quantify interannual variability in seabird breeding success relative to prey abundance. The first principal component of four hydrographic measurements yielded an index that partitioned “warm/weak upwelling” and “cool/strong upwelling” years. Partitioning of krill and forage fish time series among shelf and oceanic regions yielded spatially explicit indicators of prey availability. Krill abundance within the oceanic region was remarkably consistent between years, whereas krill over the shelf showed marked interannual fluctuations in relation to ocean conditions. Anchovy abundance varied on the shelf, and was greater in years of strong stratification, weak upwelling and warmer temperatures. Spatio-temporal variability of juvenile forage fish co-varied strongly with each other and with krill, but was weakly correlated with hydrographic conditions. Demographic responses between seabirds and prey availability revealed spatially variable associations indicative of the dynamic nature of “predator–habitat” relationships. Quantification of spatially explicit demographic responses, and their variability through time, demonstrate the possibility of delineating specific critical areas where the

Spatiotemporal analysis of tropical disease research combining Europe PMC and affiliation mapping web services.

Science.gov (United States)

Palmblad, Magnus; Torvik, Vetle I

2017-01-01

Tropical medicine appeared as a distinct sub-discipline in the late nineteenth century, during a period of rapid European colonial expansion in Africa and Asia. After a dramatic drop after World War II, research on tropical diseases have received more attention and research funding in the twenty-first century. We used Apache Taverna to integrate Europe PMC and MapAffil web services, containing the spatiotemporal analysis workflow from a list of PubMed queries to a list of publication years and author affiliations geoparsed to latitudes and longitudes. The results could then be visualized in the Quantum Geographic Information System (QGIS). Our workflows automatically matched 253,277 affiliations to geographical coordinates for the first authors of 379,728 papers on tropical diseases in a single execution. The bibliometric analyses show how research output in tropical diseases follow major historical shifts in the twentieth century and renewed interest in and funding for tropical disease research in the twenty-first century. They show the effects of disease outbreaks, WHO eradication programs, vaccine developments, wars, refugee migrations, and peace treaties. Literature search and geoparsing web services can be combined in scientific workflows performing a complete spatiotemporal bibliometric analyses of research in tropical medicine. The workflows and datasets are freely available and can be used to reproduce or refine the analyses and test specific hypotheses or look into particular diseases or geographic regions. This work exceeds all previously published bibliometric analyses on tropical diseases in both scale and spatiotemporal range.

Spatial and spatio-temporal analysis of malaria in the state of Acre, western Amazon, Brazil

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Leonardo Augusto Kohara Melchior

2016-11-01

Full Text Available Since 2005, the State of Acre, western Amazon, Brazil, has reported the highest annual parasite incidence (API of malaria among the Brazilian states. This study examines malaria incidence in Acre using spatial and spatio-temporal analysis based on an ecological time series study analyzing malaria cases and deaths for the time period 1992- 2014 and using secondary data. API indexes were calculated by age, sex, parasite species, ratio of Plasmodium vivax to P. falciparum malaria, malaria mortality rate and case fatality rate. SaTScan was used to detect spatial and spatio-temporal clusters of malaria cases and data were represented in the form of choropleth maps. A high-risk cluster of malaria was detected in Vale do Juruá and three low-risk clusters in Vale do Acre for both parasite species. Those younger than 19 years of age and females showed a high incidence of malaria in Vale do Juruá, but working-age males were the most affected in Vale do Acre. The malaria mortality rate showed a decreasing trend across the state, while the case fatality rate increased only in the micro-region of Rio Branco during the study period. We conclude that malaria is a focal disease in Acre showing different spatial and spatio-temporal patterns of cases and deaths that vary by age, sex, and parasite species. Malaria incidence is thought to be influenced by factors related to regional characteristics; therefore, appropriate disease and vector control strategies must be implemented at each locality.

Integrating Future Land Use Scenarios to Evaluate the Spatio-Temporal Dynamics of Landscape Ecological Security

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Yi Lu

2016-11-01

Full Text Available Urban ecological security is the basic principle of national ecological security. However, analyses of the spatial and temporal dynamics of ecological security remain limited, especially those that consider different scenarios of urban development. In this study, an integrated method is proposed that combines the Conversion of Land Use and its Effects (CLUE-S model with the Pressure–State–Response (P-S-R framework to assess landscape ecological security (LES in Huangshan City, China under two scenarios. Our results suggest the following conclusions: (1 the spatial and temporal dynamics of ecological security are closely related to the urbanization process; (2 although the average values of landscape ecological security are similar under different scenarios, the areas of relatively high security levels vary considerably; and (3 spatial heterogeneity in ecological security exists between different districts and counties, and the city center and its vicinity may face relatively serious declines in ecological security in the future. Overall, the proposed method not only illustrates the spatio-temporal dynamics of landscape ecological security under different scenarios but also reveals the anthropogenic effects on ecosystems by differentiating between causes, effects, and human responses at the landscape scale. This information is of great significance to decision-makers for future urban planning and management.

Spatiotemporal dynamics of a digital phase-locked loop based coupled map lattice system

Energy Technology Data Exchange (ETDEWEB)

Banerjee, Tanmoy, E-mail: tbanerjee@phys.buruniv.ac.in; Paul, Bishwajit; Sarkar, B. C. [Department of Physics, University of Burdwan, Burdwan, West Bengal 713 104 (India)

2014-03-15

We explore the spatiotemporal dynamics of a coupled map lattice (CML) system, which is realized with a one dimensional array of locally coupled digital phase-locked loops (DPLLs). DPLL is a nonlinear feedback-controlled system widely used as an important building block of electronic communication systems. We derive the phase-error equation of the spatially extended system of coupled DPLLs, which resembles a form of the equation of a CML system. We carry out stability analysis for the synchronized homogeneous solutions using the circulant matrix formalism. It is shown through extensive numerical simulations that with the variation of nonlinearity parameter and coupling strength the system shows transitions among several generic features of spatiotemporal dynamics, viz., synchronized fixed point solution, frozen random pattern, pattern selection, spatiotemporal intermittency, and fully developed spatiotemporal chaos. We quantify the spatiotemporal dynamics using quantitative measures like average quadratic deviation and spatial correlation function. We emphasize that instead of using an idealized model of CML, which is usually employed to observe the spatiotemporal behaviors, we consider a real world physical system and establish the existence of spatiotemporal chaos and other patterns in this system. We also discuss the importance of the present study in engineering application like removal of clock-skew in parallel processors.

5D Modelling: An Efficient Approach for Creating Spatiotemporal Predictive 3D Maps of Large-Scale Cultural Resources

Science.gov (United States)

Doulamis, A.; Doulamis, N.; Ioannidis, C.; Chrysouli, C.; Grammalidis, N.; Dimitropoulos, K.; Potsiou, C.; Stathopoulou, E.-K.; Ioannides, M.

2015-08-01

Outdoor large-scale cultural sites are mostly sensitive to environmental, natural and human made factors, implying an imminent need for a spatio-temporal assessment to identify regions of potential cultural interest (material degradation, structuring, conservation). On the other hand, in Cultural Heritage research quite different actors are involved (archaeologists, curators, conservators, simple users) each of diverse needs. All these statements advocate that a 5D modelling (3D geometry plus time plus levels of details) is ideally required for preservation and assessment of outdoor large scale cultural sites, which is currently implemented as a simple aggregation of 3D digital models at different time and levels of details. The main bottleneck of such an approach is its complexity, making 5D modelling impossible to be validated in real life conditions. In this paper, a cost effective and affordable framework for 5D modelling is proposed based on a spatial-temporal dependent aggregation of 3D digital models, by incorporating a predictive assessment procedure to indicate which regions (surfaces) of an object should be reconstructed at higher levels of details at next time instances and which at lower ones. In this way, dynamic change history maps are created, indicating spatial probabilities of regions needed further 3D modelling at forthcoming instances. Using these maps, predictive assessment can be made, that is, to localize surfaces within the objects where a high accuracy reconstruction process needs to be activated at the forthcoming time instances. The proposed 5D Digital Cultural Heritage Model (5D-DCHM) is implemented using open interoperable standards based on the CityGML framework, which also allows the description of additional semantic metadata information. Visualization aspects are also supported to allow easy manipulation, interaction and representation of the 5D-DCHM geometry and the respective semantic information. The open source 3DCity

Synthesizing spatiotemporally sparse smartphone sensor data for bridge modal identification

Science.gov (United States)

Ozer, Ekin; Feng, Maria Q.

2016-08-01

Smartphones as vibration measurement instruments form a large-scale, citizen-induced, and mobile wireless sensor network (WSN) for system identification and structural health monitoring (SHM) applications. Crowdsourcing-based SHM is possible with a decentralized system granting citizens with operational responsibility and control. Yet, citizen initiatives introduce device mobility, drastically changing SHM results due to uncertainties in the time and the space domains. This paper proposes a modal identification strategy that fuses spatiotemporally sparse SHM data collected by smartphone-based WSNs. Multichannel data sampled with the time and the space independence is used to compose the modal identification parameters such as frequencies and mode shapes. Structural response time history can be gathered by smartphone accelerometers and converted into Fourier spectra by the processor units. Timestamp, data length, energy to power conversion address temporal variation, whereas spatial uncertainties are reduced by geolocation services or determining node identity via QR code labels. Then, parameters collected from each distributed network component can be extended to global behavior to deduce modal parameters without the need of a centralized and synchronous data acquisition system. The proposed method is tested on a pedestrian bridge and compared with a conventional reference monitoring system. The results show that the spatiotemporally sparse mobile WSN data can be used to infer modal parameters despite non-overlapping sensor operation schedule.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

Science.gov (United States)

Aghamohammadi, Amirhossein; Ang, Mei Choo; A Sundararajan, Elankovan; Weng, Ng Kok; Mogharrebi, Marzieh; Banihashem, Seyed Yashar

2018-01-01

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

Spatio-temporal variability of aerosols in the tropics relationship with atmospheric and oceanic environments

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Zuluaga-Arias, Manuel D.

2011-12-01

Earth's radiation budget is directly influenced by aerosols through the absorption of solar radiation and subsequent heating of the atmosphere. Aerosols modulate the hydrological cycle indirectly by modifying cloud properties, precipitation and ocean heat storage. In addition, polluting aerosols impose health risks in local, regional and global scales. In spite of recent advances in the study of aerosols variability, uncertainty in their spatio-temporal distributions still presents a challenge in the understanding of climate variability. For example, aerosol loading varies not only from year to year but also on higher frequency intraseasonal time scales producing strong variability on local and regional scales. An assessment of the impact of aerosol variability requires long period measurements of aerosols at both regional and global scales. The present dissertation compiles a large database of remotely sensed aerosol loading in order to analyze its spatio-temporal variability, and how this load interacts with different variables that characterize the dynamic and thermodynamic states of the environment. Aerosol Index (AI) and Aerosol Optical Depth (AOD) were used as measures of the atmospheric aerosol load. In addition, atmospheric and oceanic satellite observations, and reanalysis datasets is used in the analysis to investigate aerosol-environment interactions. A diagnostic study is conducted to produce global and regional aerosol satellite climatologies, and to analyze and compare the validity of aerosol retrievals. We find similarities and differences between the aerosol distributions over various regions of the globe when comparing the different satellite retrievals. A nonparametric approach is also used to examine the spatial distribution of the recent trends in aerosol concentration. A significant positive trend was found over the Middle East, Arabian Sea and South Asian regions strongly influenced by increases in dust events. Spectral and composite analyses

Contaminant exposure in relation to spatio-temporal variation in diet composition: A case study of the little owl (Athene noctua)

International Nuclear Information System (INIS)

Schipper, Aafke M.; Wijnhoven, Sander; Baveco, Hans; Brink, Nico W. van den

2012-01-01

We assessed dietary exposure of the little owl Athene noctua to trace metal contamination in a Dutch Rhine River floodplain area. Diet composition was calculated per month for three habitat types, based on the population densities of six prey types (earthworms, ground beetles and four small mammal species) combined with the little owl’s functional response to these prey types. Exposure levels showed a strong positive relationship with the dietary fraction of earthworms, but also depended on the dietary fraction of common voles, with higher common vole fractions resulting in decreasing exposure levels. Spatio-temporal changes in the availability of earthworms and common voles in particular resulted in considerable variation in exposure, with peaks in exposure exceeding a tentative toxicity threshold. These findings imply that wildlife exposure assessments based on a predefined, average diet composition may considerably underestimate local or intermittent peaks in exposure. - Highlights: ► We assessed dietary cadmium exposure of the little owl in a Dutch floodplain. ► Exposure levels were related mainly to the availability of earthworms and voles. ► Diet composition and exposure levels showed large spatio-temporal variation. ► Seasonal peaks in exposure exceeded a tentative toxicity threshold. - Dietary contaminant exposure of opportunistic predators may vary considerably due to spatio-temporal variation in diet.

Spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013

Science.gov (United States)

Wang, Lizhe; Chen, Lajiao

2016-07-01

Spatio-temporal data on human population and its driving factors is critical to understanding and responding to population problems. Unfortunately, such spatio-temporal data on a large scale and over the long term are often difficult to obtain. Here, we present a dataset on Chinese population distribution and its driving factors over a remarkably long period, from 1949 to 2013. Driving factors of population distribution were selected according to the push-pull migration laws, which were summarized into four categories: natural environment, natural resources, economic factors and social factors. Natural environment and natural resources indicators were calculated using Geographic Information System (GIS) and Remote Sensing (RS) techniques, whereas economic and social factors from 1949 to 2013 were collected from the China Statistical Yearbook and China Compendium of Statistics from 1949 to 2008. All of the data were quality controlled and unified into an identical dataset with the same spatial scope and time period. The dataset is expected to be useful for understanding how population responds to and impacts environmental change.

Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement.

Science.gov (United States)

Malik, Abish; Maciejewski, Ross; Towers, Sherry; McCullough, Sean; Ebert, David S

2014-12-01

In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.

A Hierarchical and Dynamic Seascape Framework for Scaling and Comparing Ocean Biodiversity Observations

Science.gov (United States)

Kavanaugh, M.; Muller-Karger, F. E.; Montes, E.; Santora, J. A.; Chavez, F.; Messié, M.; Doney, S. C.

2016-02-01

The pelagic ocean is a complex system in which physical, chemical and biological processes interact to shape patterns on multiple spatial and temporal scales and levels of ecological organization. Monitoring and management of marine seascapes must consider a hierarchical and dynamic mosaic, where the boundaries, extent, and location of features change with time. As part of a Marine Biodiversity Observing Network demonstration project, we conducted a multiscale classification of dynamic coastal seascapes in the northeastern Pacific and Gulf of Mexico using multivariate satellite and modeled data. Synoptic patterns were validated using mooring and ship-based observations that spanned multiple trophic levels and were collected as part of several long-term monitoring programs, including the Monterey Bay and Florida Keys National Marine Sanctuaries. Seascape extent and habitat diversity varied as a function of both seasonal and interannual forcing. We discuss the patterns of in situ observations in the context of seascape dynamics and the effect on rarefaction, spatial patchiness, and tracking and comparing ecosystems through time. A seascape framework presents an effective means to translate local biodiversity measurements to broader spatiotemporal scales, scales relevant for modeling the effects of global change and enabling whole-ecosystem management in the dynamic ocean.

Spatiotemporal Analysis of the 2014 Ebola Epidemic in West Africa.

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Jantien A Backer

2016-12-01

Full Text Available In 2014-2016, Guinea, Sierra Leone and Liberia in West Africa experienced the largest and longest Ebola epidemic since the discovery of the virus in 1976. During the epidemic, incidence data were collected and published at increasing resolution. To monitor the epidemic as it spread within and between districts, we develop an analysis method that exploits the full spatiotemporal resolution of the data by combining a local model for time-varying effective reproduction numbers with a gravity-type model for spatial dispersion of the infection. We test this method in simulations and apply it to the weekly incidences of confirmed and probable cases per district up to June 2015, as reported by the World Health Organization. Our results indicate that, of the newly infected cases, only a small percentage, between 4% and 10%, migrates to another district, and a minority of these migrants, between 0% and 23%, leave their country. The epidemics in the three countries are found to be similar in estimated effective reproduction numbers, and in the probability of importing infection into a district. The countries might have played different roles in cross-border transmissions, although a sensitivity analysis suggests that this could also be related to underreporting. The spatiotemporal analysis method can exploit available longitudinal incidence data at different geographical locations to monitor local epidemics, determine the extent of spatial spread, reveal the contribution of local and imported cases, and identify sources of introductions in uninfected areas. With good quality data on incidence, this data-driven method can help to effectively control emerging infections.

Modeling and Statistical Analysis of the Spatio-Temporal Patterns of Seasonal Influenza in Israel

Science.gov (United States)

Katriel, Guy; Yaari, Rami; Roll, Uri; Stone, Lewi

2012-01-01

Background Seasonal influenza outbreaks are a serious burden for public health worldwide and cause morbidity to millions of people each year. In the temperate zone influenza is predominantly seasonal, with epidemics occurring every winter, but the severity of the outbreaks vary substantially between years. In this study we used a highly detailed database, which gave us both temporal and spatial information of influenza dynamics in Israel in the years 1998–2009. We use a discrete-time stochastic epidemic SIR model to find estimates and credible confidence intervals of key epidemiological parameters. Findings Despite the biological complexity of the disease we found that a simple SIR-type model can be fitted successfully to the seasonal influenza data. This was true at both the national levels and at the scale of single cities.The effective reproductive number Re varies between the different years both nationally and among Israeli cities. However, we did not find differences in Re between different Israeli cities within a year. R e was positively correlated to the strength of the spatial synchronization in Israel. For those years in which the disease was more “infectious”, then outbreaks in different cities tended to occur with smaller time lags. Our spatial analysis demonstrates that both the timing and the strength of the outbreak within a year are highly synchronized between the Israeli cities. We extend the spatial analysis to demonstrate the existence of high synchrony between Israeli and French influenza outbreaks. Conclusions The data analysis combined with mathematical modeling provided a better understanding of the spatio-temporal and synchronization dynamics of influenza in Israel and between Israel and France. Altogether, we show that despite major differences in demography and weather conditions intra-annual influenza epidemics are tightly synchronized in both their timing and magnitude, while they may vary greatly between years. The predominance of

Spatio-temporal data analytics for wind energy integration

CERN Document Server

Yang, Lei; Zhang, Junshan

2014-01-01

This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic di

Spatiotemporal Propagation of the Cortical Atrophy: Population and Individual Patterns

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Igor Koval

2018-05-01

Full Text Available Repeated failures in clinical trials for Alzheimer’s disease (AD have raised a strong interest for the prodromal phase of the disease. A better understanding of the brain alterations during this early phase is crucial to diagnose patients sooner, to estimate an accurate disease stage, and to give a reliable prognosis. According to recent evidence, structural alterations in the brain are likely to be sensitive markers of the disease progression. Neuronal loss translates in specific spatiotemporal patterns of cortical atrophy, starting in the enthorinal cortex and spreading over other cortical regions according to specific propagation pathways. We developed a digital model of the cortical atrophy in the left hemisphere from prodromal to diseased phases, which is built on the temporal alignment and combination of several short-term observation data to reconstruct the long-term history of the disease. The model not only provides a description of the spatiotemporal patterns of cortical atrophy at the group level but also shows the variability of these patterns at the individual level in terms of difference in propagation pathways, speed of propagation, and age at propagation onset. Longitudinal MRI datasets of patients with mild cognitive impairments who converted to AD are used to reconstruct the cortical atrophy propagation across all disease stages. Each observation is considered as a signal spatially distributed on a network, such as the cortical mesh, each cortex location being associated to a node. We consider how the temporal profile of the signal varies across the network nodes. We introduce a statistical mixed-effect model to describe the evolution of the cortex alterations. To ensure a spatiotemporal smooth propagation of the alterations, we introduce a constrain on the propagation signal in the model such that neighboring nodes have similar profiles of the signal changes. Our generative model enables the reconstruction of personalized

Research of Cadastral Data Modelling and Database Updating Based on Spatio-temporal Process

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ZHANG Feng

2016-02-01

Full Text Available The core of modern cadastre management is to renew the cadastre database and keep its currentness,topology consistency and integrity.This paper analyzed the changes and their linkage of various cadastral objects in the update process.Combined object-oriented modeling technique with spatio-temporal objects' evolution express,the paper proposed a cadastral data updating model based on the spatio-temporal process according to people's thought.Change rules based on the spatio-temporal topological relations of evolution cadastral spatio-temporal objects are drafted and further more cascade updating and history back trace of cadastral features,land use and buildings are realized.This model implemented in cadastral management system-ReGIS.Achieved cascade changes are triggered by the direct driving force or perceived external events.The system records spatio-temporal objects' evolution process to facilitate the reconstruction of history,change tracking,analysis and forecasting future changes.

An implicit spatiotemporal shape model for human activity localization and recognition

NARCIS (Netherlands)

Oikonomopoulos, A.; Patras, I.; Pantic, Maja

2009-01-01

In this paper we address the problem of localisation and recognition of human activities in unsegmented image sequences. The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization

Spatio-temporal modeling of nonlinear distributed parameter systems

CERN Document Server

Li, Han-Xiong

2011-01-01

The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s

Challenges in modeling spatiotemporally varying phytoplankton blooms in the Northwestern Arabian Sea and Gulf of Oman

Science.gov (United States)

Sedigh Marvasti, S.; Gnanadesikan, A.; Bidokhti, A. A.; Dunne, J. P.; Ghader, S.

2016-02-01

Recent years have shown an increase in harmful algal blooms in the Northwest Arabian Sea and Gulf of Oman, raising the question of whether climate change will accelerate this trend. This has led us to examine whether the Earth System Models used to simulate phytoplankton productivity accurately capture bloom dynamics in this region - both in terms of the annual cycle and interannual variability. Satellite data (SeaWIFS ocean color) show two climatological blooms in this region, a wintertime bloom peaking in February and a summertime bloom peaking in September. On a regional scale, interannual variability of the wintertime bloom is dominated by cyclonic eddies which vary in location from one year to another. Two coarse (1°) models with the relatively complex biogeochemistry (TOPAZ) capture the annual cycle but neither eddies nor the interannual variability. An eddy-resolving model (GFDL CM2.6) with a simpler biogeochemistry (miniBLING) displays larger interannual variability, but overestimates the wintertime bloom and captures eddy-bloom coupling in the south but not in the north. The models fail to capture both the magnitude of the wintertime bloom and its modulation by eddies in part because of their failure to capture the observed sharp thermocline and/or nutricline in this region. When CM2.6 is able to capture such features in the Southern part of the basin, eddies modulate diffusive nutrient supply to the surface (a mechanism not previously emphasized in the literature). For the model to simulate the observed wintertime blooms within cyclones, it will be necessary to represent this relatively unusual nutrient structure as well as the cyclonic eddies. This is a challenge in the Northern Arabian Sea as it requires capturing the details of the outflow from the Persian Gulf - something that is poorly done in global models.

Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices

Science.gov (United States)

El Harti, Abderrazak; Lhissou, Rachid; Chokmani, Karem; Ouzemou, Jamal-eddine; Hassouna, Mohamed; Bachaoui, El Mostafa; El Ghmari, Abderrahmene

2016-08-01

Soil salinization is major environmental issue in irrigated agricultural production. Conventional methods for salinization monitoring are time and money consuming and limited by the high spatiotemporal variability of this phenomenon. This work aims to propose a spatiotemporal monitoring method of soil salinization in the Tadla plain in central Morocco using spectral indices derived from Thematic Mapper (TM) and Operational Land Imager (OLI) data. Six Landsat TM/OLI satellite images acquired during 13 years period (2000-2013) coupled with in-situ electrical conductivity (EC) measurements were used to develop the proposed method. After radiometric and atmospheric correction of TM/OLI images, a new soil salinity index (OLI-SI) is proposed for soil EC estimation. Validation shows that this index allowed a satisfactory EC estimation in the Tadla irrigated perimeter with coefficient of determination R2 varying from 0.55 to 0.77 and a Root Mean Square Error (RMSE) ranging between 1.02 dS/m and 2.35 dS/m. The times-series of salinity maps produced over the Tadla plain using the proposed method show that salinity is decreasing in intensity and progressively increasing in spatial extent, over the 2000-2013 period. This trend resulted in a decrease in agricultural activities in the southwestern part of the perimeter, located in the hydraulic downstream.

Growing magma chambers control the distribution of small-scale flood basalts.

Science.gov (United States)

Yu, Xun; Chen, Li-Hui; Zeng, Gang

2015-11-19

Small-scale continental flood basalts are a global phenomenon characterized by regular spatio-temporal distributions. However, no genetic mechanism has been proposed to explain the visible but overlooked distribution patterns of these continental basaltic volcanism. Here we present a case study from eastern China, combining major and trace element analyses with Ar-Ar and K-Ar dating to show that the spatio-temporal distribution of small-scale flood basalts is controlled by the growth of long-lived magma chambers. Evolved basalts (SiO2 > 47.5 wt.%) from Xinchang-Shengzhou, a small-scale Cenozoic flood basalt field in Zhejiang province, eastern China, show a northward younging trend over the period 9.4-3.0 Ma. With northward migration, the magmas evolved only slightly ((Na2O + K2O)/MgO = 0.40-0.66; TiO2/MgO = 0.23-0.35) during about 6 Myr (9.4-3.3 Ma). When the flood basalts reached the northern end of the province, the magmas evolved rapidly (3.3-3.0 Ma) through a broad range of compositions ((Na2O + K2O)/MgO = 0.60-1.28; TiO2/MgO = 0.30-0.57). The distribution and two-stage compositional evolution of the migrating flood basalts record continuous magma replenishment that buffered against magmatic evolution and induced magma chamber growth. Our results demonstrate that the magma replenishment-magma chamber growth model explains the spatio-temporal distribution of small-scale flood basalts.

Spatio-temporal modelling of zero-inflated deep-sea shrimp data by Tweedie generalized additive

Directory of Open Access Journals (Sweden)

Simona Arcuti

2013-10-01

Full Text Available In theMediterrean Sea the population features of demersal resources fluctuate over spatial and temporal scales due to the variability of abiotic and biotic factors as well as to human activities. The two shrimps Parapenaeus longirostris and Aristaeomorpha foliacea are among the most important deep-sea demersal resources in the North-Western Ionian Sea. Their changes in terms of density, biomass andmedian length induced by anthropogenic and environmental variables (fishing effort, sea surface temperature, precipitations, Winter North Atlantic Oscillation (NAO and Annual MediterraneanOscillation (MO indices were investigated. Biological data were collected during trawl surveys carried out from 1995 to 2006 as part of the international program MEDITS (International Bottom Trawl Survey in the Mediterranean. Generalized AdditiveModels were used to evaluate the spatio-temporal variation of both species, together with the possible nonlinear effects of biotic and abiotic factors. Density and biomass were assumed to be distributed according to a member of the Tweedie family in order to account for zero-inflation in the relative data. Spacetime interaction was consideredwithin a non-separablemodel with smooth spatio-temporal component based on tensor product splines. The results show significant spatio-temporal and depth effects in the three population parameters of these resources. Winter NAO index significantly influenced the density, biomass and length of P. longirostris. Sea surface temperature significantly influenced the size of this species and the three population features of A. foliacea. The size of this shrimp resulted also influenced negatively by fishing effort and positively by the MO index.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos

Science.gov (United States)

2018-01-01

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods. PMID:29438421

Bayesian spatio-temporal modeling of particulate matter concentrations in Peninsular Malaysia

Science.gov (United States)

Manga, Edna; Awang, Norhashidah

2016-06-01

This article presents an application of a Bayesian spatio-temporal Gaussian process (GP) model on particulate matter concentrations from Peninsular Malaysia. We analyze daily PM10 concentration levels from 35 monitoring sites in June and July 2011. The spatiotemporal model set in a Bayesian hierarchical framework allows for inclusion of informative covariates, meteorological variables and spatiotemporal interactions. Posterior density estimates of the model parameters are obtained by Markov chain Monte Carlo methods. Preliminary data analysis indicate information on PM10 levels at sites classified as industrial locations could explain part of the space time variations. We include the site-type indicator in our modeling efforts. Results of the parameter estimates for the fitted GP model show significant spatio-temporal structure and positive effect of the location-type explanatory variable. We also compute some validation criteria for the out of sample sites that show the adequacy of the model for predicting PM10 at unmonitored sites.

Limited mobility of target pests crucially lowers controllability when sterile insect releases are spatiotemporally biased.

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Ikegawa, Yusuke; Himuro, Chihiro

2017-05-21

The sterile insect technique (SIT) is a genetic pest control method wherein mass-reared sterile insects are periodically released into the wild, thereby impeding the successful reproduction of fertile pests. In Okinawa Prefecture, Japan, the SIT has been implemented to eradicate the West Indian sweet potato weevil Euscepes postfasciatus (Fairmaire), which is a flightless agricultural pest of sweet potatoes. It is known that E. postfasciatus is much less mobile than other insects to which the SIT has been applied. However, previous theoretical studies have rarely examined effects of low mobility of target pests and variation in the spatiotemporal evenness of sterile insect releases. To theoretically examine the effects of spatiotemporal evenness on the regional eradication of less mobile pests, we constructed a simple two-patch population model comprised of a pest and sterile insect moving between two habitats, and numerically simulated different release strategies (varying the number of released sterile insects and release intervals). We found that spatially biased releases allowed the pest to spatially escape from the sterile insect, and thus intensively lowered its controllability. However, we showed that the temporally counterbalancing spatially biased releases by swapping the number of released insects in the two habitats at every release (called temporal balancing) could greatly mitigate this negative effect and promote the controllability. We also showed that the negative effect of spatiotemporally biased releases was a result of the limited mobility of the target insect. Although directed dispersal of the insects in response to habitats of differing quality could lower the controllability in the more productive habitat, the temporal balancing could promote and eventually maximize the controllability as released insects increased. Copyright © 2017 Elsevier Ltd. All rights reserved.

Wave fronts and spatiotemporal chaos in an array of coupled Lorenz oscillators

International Nuclear Information System (INIS)

Pazo, Diego; Montejo, Noelia; Perez-Munuzuri, Vicente

2001-01-01

The effects of coupling strength and single-cell dynamics (SCD) on spatiotemporal pattern formation are studied in an array of Lorenz oscillators. Different spatiotemporal structures (stationary patterns, propagating wave fronts, short wavelength bifurcation) arise for bistable SCD, and two well differentiated types of spatiotemporal chaos for chaotic SCD (in correspondence with the transition from stationary patterns to propagating fronts). Wave-front propagation in the bistable regime is studied in terms of global bifurcation theory, while a short wavelength pattern region emerges through a pitchfork bifurcation

Characterizing the intra-urban spatiotemporal dynamics of High Heat Stress Zones (Hotspots)

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Shreevastava, A.; Rao, P. S.; McGrath, G. S.

2017-12-01

In this study, we present an innovative framework to characterize the spatio-temporal dynamics of High Heat Stress Zones (Hot spots) created within an Urban area in the event of a Heat Wave. Heat waves are one of the leading causes of weather-related human mortality in many countries, and cities receive its worst brunt. The extreme heat stress within urban areas is often a synergistic combination of large-scale meteorological events, and the locally exacerbated impacts due to Urban Heat Islands (UHI). UHI is typically characterized as the difference between mean temperature of the urban and rural area. As a result, it fails to capture the significant variability that exists within the city itself. This variability arises from the diverse and complex spatial geometries of cities. Previous studies that have attempted to quantify the heat stress at an intra-urban scale are labor intensive, expensive, and difficult to emulate globally as they rely on availability of extensive data and their assimilation. The proposed study takes advantage of the well-established notion of fractal properties of cities to make the methods scalable to other cities where in-situ observational data might not be available. As an input, land surface temperatures are estimated using Landsat data. Using clustering analysis, we probe the emergence of thermal hotspots. The probability distributions (PD) of these hotspots are found to follow a power-law distribution in agreement with fractal characteristics of the city. PDs of several archetypical cities are then investigated to compare the effect of different spatial structures (e.g. monocentric v/s polycentric, sprawl v/s compact). Further, the temporal variability of the distributions on a diurnal as well as a seasonal scale is discussed. Finally, the spatiotemporal dynamics of the urban hotspots under a heat-wave (E.g. Delhi Heat wave, 2015) are compared against the non-heat wave scenarios. In summary, a technique that is globally adaptive and

A flexible spatiotemporal method for fusing satellite images with different resolutions

Science.gov (United States)

Xiaolin Zhu; Eileen H. Helmer; Feng Gao; Desheng Liu; Jin Chen; Michael A. Lefsky

2016-01-01

Studies of land surface dynamics in heterogeneous landscapes often require remote sensing datawith high acquisition frequency and high spatial resolution. However, no single sensor meets this requirement. This study presents a new spatiotemporal data fusion method, the Flexible Spatiotemporal DAta Fusion (FSDAF) method, to generate synthesized frequent high spatial...

Evolution of predator dispersal in relation to spatio-temporal prey dynamics: how not to get stuck in the wrong place!

Directory of Open Access Journals (Sweden)

Justin M J Travis

Full Text Available The eco-evolutionary dynamics of dispersal are recognised as key in determining the responses of populations to environmental changes. Here, by developing a novel modelling approach, we show that predators are likely to have evolved to emigrate more often and become more selective over their destination patch when their prey species exhibit spatio-temporally complex dynamics. We additionally demonstrate that the cost of dispersal can vary substantially across space and time. Perhaps as a consequence of current environmental change, many key prey species are currently exhibiting major shifts in their spatio-temporal dynamics. By exploring similar shifts in silico, we predict that predator populations will be most vulnerable when prey dynamics shift from stable to complex. The more sophisticated dispersal rules, and greater variance therein, that evolve under complex dynamics will enable persistence across a broader range of prey dynamics than the rules which evolve under relatively stable prey conditions.

Spatiotemporal Variation of Driving Forces for Settlement Expansion in Different Types of Counties

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Guanglong Dong

2015-12-01

Full Text Available Understanding the process of settlement expansion and the spatiotemporal variation of driving forces is the foundation of rational and specific planning for sustainable development. However, little attention has been paid to the spatiotemporal differences of driving forces among different counties, especially when they are representatives of different development types. This study used Guanyun, Kunshan and Changshu as case studies, and binary logistic regression was employed. The results showed that the expansion rates of Kunshan and Changshu were 5.55 and 3.93 times higher than that of Guanyun. The combinations and relative importance of drivers varied with counties and periods. The change in the number of driving forces can be divided into three stages: increasing stage, decreasing stage, and stable stage. In the relatively developed counties, Kunshan and Changshu, the importance of population is decreased, while it remain an important factor in the less developed county, Guanyun. In addition, the effect of GDP stays the same in Kunshan while it becomes the most important factor in Changshu. The distance to the main road and the distance to town are increasingly important in Kunshan and Guanyun, and distance to town has been the only common factor in the last period, indicating the discrepancy is increased. The relative importance of distance to a lake in Kunshan and Changshu increased, reflecting the role of increasing tourism in accelerating settlement expansion.

ELASTIC CLOUD COMPUTING ARCHITECTURE AND SYSTEM FOR HETEROGENEOUS SPATIOTEMPORAL COMPUTING

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X. Shi

2017-10-01

Full Text Available Spatiotemporal computation implements a variety of different algorithms. When big data are involved, desktop computer or standalone application may not be able to complete the computation task due to limited memory and computing power. Now that a variety of hardware accelerators and computing platforms are available to improve the performance of geocomputation, different algorithms may have different behavior on different computing infrastructure and platforms. Some are perfect for implementation on a cluster of graphics processing units (GPUs, while GPUs may not be useful on certain kind of spatiotemporal computation. This is the same situation in utilizing a cluster of Intel's many-integrated-core (MIC or Xeon Phi, as well as Hadoop or Spark platforms, to handle big spatiotemporal data. Furthermore, considering the energy efficiency requirement in general computation, Field Programmable Gate Array (FPGA may be a better solution for better energy efficiency when the performance of computation could be similar or better than GPUs and MICs. It is expected that an elastic cloud computing architecture and system that integrates all of GPUs, MICs, and FPGAs could be developed and deployed to support spatiotemporal computing over heterogeneous data types and computational problems.

Elastic Cloud Computing Architecture and System for Heterogeneous Spatiotemporal Computing

Science.gov (United States)

Shi, X.

2017-10-01

Spatiotemporal computation implements a variety of different algorithms. When big data are involved, desktop computer or standalone application may not be able to complete the computation task due to limited memory and computing power. Now that a variety of hardware accelerators and computing platforms are available to improve the performance of geocomputation, different algorithms may have different behavior on different computing infrastructure and platforms. Some are perfect for implementation on a cluster of graphics processing units (GPUs), while GPUs may not be useful on certain kind of spatiotemporal computation. This is the same situation in utilizing a cluster of Intel's many-integrated-core (MIC) or Xeon Phi, as well as Hadoop or Spark platforms, to handle big spatiotemporal data. Furthermore, considering the energy efficiency requirement in general computation, Field Programmable Gate Array (FPGA) may be a better solution for better energy efficiency when the performance of computation could be similar or better than GPUs and MICs. It is expected that an elastic cloud computing architecture and system that integrates all of GPUs, MICs, and FPGAs could be developed and deployed to support spatiotemporal computing over heterogeneous data types and computational problems.

Multivariate Spatio-Temporal Clustering: A Framework for Integrating Disparate Data to Understand Network Representativeness and Scaling Up Sparse Ecosystem Measurements

Science.gov (United States)

Hoffman, F. M.; Kumar, J.; Maddalena, D. M.; Langford, Z.; Hargrove, W. W.

2014-12-01

Disparate in situ and remote sensing time series data are being collected to understand the structure and function of ecosystems and how they may be affected by climate change. However, resource and logistical constraints limit the frequency and extent of observations, particularly in the harsh environments of the arctic and the tropics, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent variability at desired scales. These regions host large areas of potentially vulnerable ecosystems that are poorly represented in Earth system models (ESMs), motivating two new field campaigns, called Next Generation Ecosystem Experiments (NGEE) for the Arctic and Tropics, funded by the U.S. Department of Energy. Multivariate Spatio-Temporal Clustering (MSTC) provides a quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks. We applied MSTC to down-scaled general circulation model results and data for the State of Alaska at a 4 km2 resolution to define maps of ecoregions for the present (2000-2009) and future (2090-2099), showing how combinations of 37 bioclimatic characteristics are distributed and how they may shift in the future. Optimal representative sampling locations were identified on present and future ecoregion maps, and representativeness maps for candidate sampling locations were produced. We also applied MSTC to remotely sensed LiDAR measurements and multi-spectral imagery from the WorldView-2 satellite at a resolution of about 5 m2 within the Barrow Environmental Observatory (BEO) in Alaska. At this resolution, polygonal ground features—such as centers, edges, rims, and troughs—can be distinguished. Using these remote sensing data, we up-scaled vegetation distribution data collected on these polygonal ground features to a large area of the BEO to provide distributions of plant functional types that can

Spatiotemporal dynamics of surface water networks across a global biodiversity hotspot—implications for conservation

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Tulbure, Mirela G.; Kininmonth, Stuart; Broich, Mark

2014-11-01

The concept of habitat networks represents an important tool for landscape conservation and management at regional scales. Previous studies simulated degradation of temporally fixed networks but few quantified the change in network connectivity from disintegration of key features that undergo naturally occurring spatiotemporal dynamics. This is particularly of concern for aquatic systems, which typically show high natural spatiotemporal variability. Here we focused on the Swan Coastal Plain, a bioregion that encompasses a global biodiversity hotspot in Australia with over 1500 water bodies of high biodiversity. Using graph theory, we conducted a temporal analysis of water body connectivity over 13 years of variable climate. We derived large networks of surface water bodies using Landsat data (1999-2011). We generated an ensemble of 278 potential networks at three dispersal distances approximating the maximum dispersal distance of different water dependent organisms. We assessed network connectivity through several network topology metrics and quantified the resilience of the network topology during wet and dry phases. We identified ‘stepping stone’ water bodies across time and compared our networks with theoretical network models with known properties. Results showed a highly dynamic seasonal pattern of variability in network topology metrics. A decline in connectivity over the 13 years was noted with potential negative consequences for species with limited dispersal capacity. The networks described here resemble theoretical scale-free models, also known as ‘rich get richer’ algorithm. The ‘stepping stone’ water bodies are located in the area around the Peel-Harvey Estuary, a Ramsar listed site, and some are located in a national park. Our results describe a powerful approach that can be implemented when assessing the connectivity for a particular organism with known dispersal distance. The approach of identifying the surface water bodies that act as �

Artificial neural network does better spatiotemporal compressive sampling

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Lee, Soo-Young; Hsu, Charles; Szu, Harold

2012-06-01

Spatiotemporal sparseness is generated naturally by human visual system based on artificial neural network modeling of associative memory. Sparseness means nothing more and nothing less than the compressive sensing achieves merely the information concentration. To concentrate the information, one uses the spatial correlation or spatial FFT or DWT or the best of all adaptive wavelet transform (cf. NUS, Shen Shawei). However, higher dimensional spatiotemporal information concentration, the mathematics can not do as flexible as a living human sensory system. The reason is obviously for survival reasons. The rest of the story is given in the paper.

The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow

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Yin Tian

2018-04-01

Full Text Available Using the scalp time-varying network method, the present study is the first to investigate the temporal influence of the reference on N170, a negative event-related potential component (ERP appeared about 170 ms that is elicited by facial recognition, in the network levels. Two kinds of scalp electroencephalogram (EEG references, namely, AR (average of all recording channels and reference electrode standardization technique (REST, were comparatively investigated via the time-varying processing of N170. Results showed that the latency and amplitude of N170 were significantly different between REST and AR, with the former being earlier and smaller. In particular, the information flow from right temporal-parietal P8 to left P7 in the time-varying network was earlier in REST than that in AR, and this phenomenon was reproduced by simulation, in which the performance of REST was closer to the true case at source level. These findings indicate that reference plays a crucial role in ERP data interpretation, and importantly, the newly developed approximate zero-reference REST would be a superior choice for precise evaluation of the scalp spatio-temporal changes relating to various cognitive events.

Spiking neural network for recognizing spatiotemporal sequences of spikes

International Nuclear Information System (INIS)

Jin, Dezhe Z.

2004-01-01

Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons

Ranking businesses and municipal locations by spatiotemporal cardiac arrest risk to guide public defibrillator placement

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Sun, Christopher L. F.; Brooks, Steven C.; Morrison, Laurie J.; Chan, Timothy C.Y.

2017-01-01

Background Efforts to guide automated external defibrillator (AED) placement for out-of-hospital cardiac arrest (OHCA) treatment have focused on identifying broadly defined location categories without considering hours of operation. Broad location categories may be composed of many businesses with varying accessibility. Identifying specific locations for AED deployment incorporating operating hours and time of OHCA occurrence may improve AED accessibility. We aim to identify specific businesses and municipal locations that maximize OHCA coverage based on spatiotemporal assessment of OHCA risk in the immediate vicinity of franchise locations. Methods This study was a retrospective population-based cohort study using data from the Toronto Regional RescuNET Epistry cardiac arrest database. We identified all non-traumatic public OHCAs occurring in Toronto, Canada from Jan. 2007–Dec. 2015. We identified 41 unique businesses and municipal location types with 20 or more locations in Toronto from the YellowPages, Canadian Franchise Association, and the City of Toronto Open Data Portal. We obtained their geographic coordinates and hours of operation from websites, phone, or in-person. We determined the number of OHCAs that occurred within 100 m of each location when it was open (spatiotemporal coverage) for Toronto overall and downtown. The businesses and municipal locations were then ranked by spatiotemporal OHCA coverage. To evaluate temporal stability of the rankings, we calculated intra-class correlation (ICC) of the annual coverage values. Results There were 2,654 non-traumatic public OHCAs. Tim Hortons ranked first in Toronto covering 286 OHCAs. Starbucks ranked first in downtown covering 110 OHCAs. Coffee shops and bank machines from the five largest Canadian banks occupied eight of the top 10 spots in both Toronto and downtown. The rankings exhibited high temporal stability with ICC values of 0.88 (95% CI, 0.83–0.93) in Toronto and 0.79 (95% CI, 0.71–0.86) in

Ranking Businesses and Municipal Locations by Spatiotemporal Cardiac Arrest Risk to Guide Public Defibrillator Placement.

Science.gov (United States)

Sun, Christopher L F; Brooks, Steven C; Morrison, Laurie J; Chan, Timothy C Y

2017-03-21

Efforts to guide automated external defibrillator placement for out-of-hospital cardiac arrest (OHCA) treatment have focused on identifying broadly defined location categories without considering hours of operation. Broad location categories may be composed of many businesses with varying accessibility. Identifying specific locations for automated external defibrillator deployment incorporating operating hours and time of OHCA occurrence may improve automated external defibrillator accessibility. We aim to identify specific businesses and municipal locations that maximize OHCA coverage on the basis of spatiotemporal assessment of OHCA risk in the immediate vicinity of franchise locations. This study was a retrospective population-based cohort study using data from the Toronto Regional RescuNET Epistry cardiac arrest database. We identified all nontraumatic public OHCAs occurring in Toronto, ON, Canada, from January 2007 through December 2015. We identified 41 unique businesses and municipal location types with ≥20 locations in Toronto from the YellowPages, Canadian Franchise Association, and the City of Toronto Open Data Portal. We obtained their geographic coordinates and hours of operation from Web sites, by phone, or in person. We determined the number of OHCAs that occurred within 100 m of each location when it was open (spatiotemporal coverage) for Toronto overall and downtown. The businesses and municipal locations were then ranked by spatiotemporal OHCA coverage. To evaluate temporal stability of the rankings, we calculated intraclass correlation of the annual coverage values. There were 2654 nontraumatic public OHCAs. Tim Hortons ranked first in Toronto, covering 286 OHCAs. Starbucks ranked first in downtown, covering 110 OHCAs. Coffee shops and bank machines from the 5 largest Canadian banks occupied 8 of the top 10 spots in both Toronto and downtown. The rankings exhibited high temporal stability with intraclass correlation values of 0.88 (95

Estimating the state of large spatio-temporally chaotic systems

International Nuclear Information System (INIS)

Ott, E.; Hunt, B.R.; Szunyogh, I.; Zimin, A.V.; Kostelich, E.J.; Corazza, M.; Kalnay, E.; Patil, D.J.; Yorke, J.A.

2004-01-01

We consider the estimation of the state of a large spatio-temporally chaotic system from noisy observations and knowledge of a system model. Standard state estimation techniques using the Kalman filter approach are not computationally feasible for systems with very many effective degrees of freedom. We present and test a new technique (called a Local Ensemble Kalman Filter), generally applicable to large spatio-temporally chaotic systems for which correlations between system variables evaluated at different points become small at large separation between the points

Statistical methods for spatio-temporal systems

CERN Document Server

Finkenstadt, Barbel

2006-01-01

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities.Contributed by leading researchers in the field, each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis, gastroenteric disease, and the U.K. foot-and-mouth outbreak, the first chapter uses stochastic models, such as point process models, to provide the probabilistic backbone that facilitates statistical inference from data. The next chapter discusses the critical issue of modeling random growth objects in diverse biological systems, such as bacteria colonies, tumors, and plant populations. The subsequent chapter examines data transformation tools using examples from ecology and air quality data, followed by a chapter on space-time co...

Assessment of spatio-temporal gait parameters from trunk accelerations during human walking

NARCIS (Netherlands)

Zijlstra, W; Hof, AL

2003-01-01

This paper studies the feasibility of an analysis of spatio-temporal gait parameters based upon accelerometry. To this purpose, acceleration patterns of the trunk and their relationships with spatio-temporal gait parameters were analysed in healthy subjects. Based on model predictions of the body's

A spatiotemporal analysis of hydrological patterns based on a wireless sensor network system

Science.gov (United States)

Plaza, F.; Slater, T. A.; Zhong, X.; Li, Y.; Liang, Y.; Liang, X.

2017-12-01

Understanding complicated spatiotemporal patterns of eco-hydrological variables at a small scale plays a profound role in improving predictability of high resolution distributed hydrological models. However, accurate and continuous monitoring of these complex patterns has become one of the main challenges in the environmental sciences. Wireless sensor networks (WSNs) have emerged as one of the most widespread potential solutions to achieve this. This study presents a spatiotemporal analysis of hydrological patterns (e.g., soil moisture, soil water potential, soil temperature and transpiration) based on observational data collected from a dense multi-hop wireless sensor network (WSN) in a steep-forested testbed located in Southwestern Pennsylvania, USA. At this WSN testbed with an approximate area of 3000 m2, environmental variables are collected from over 240 sensors that are connected to more than 100 heterogeneous motes. The sensors include the soil moisture of EC-5, soil temperature and soil water potential of MPS-1 and MPS-2, and sap flow sensors constructed in house. The motes consist of MICAz, IRIS and TelosB. In addition, several data loggers have been installed along the site to provide a comparative reference to the WSN measurements for the purpose of checking the WSN data quality. The edaphic properties monitored by the WSN sensors show strong agreement with the data logger measurements. Moreover, sap flow measurements, scaled to tree stand transpiration, are found to be reasonable. This study also investigates the feasibility and roles that these sensor measurements play in improving the performance of high-resolution distributed hydrological models. In particular, we explore this using a modified version of the Distributed Hydrological Soil Vegetation Model (DHSVM).

Spatio-Temporal Saliency Perception via Hypercomplex Frequency Spectral Contrast

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Zhiqiang Tian

2013-03-01

Full Text Available Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor. In this paper, we propose a novel spatio-temporal saliency perception method based on spatio-temporal hypercomplex spectral contrast (HSC. Firstly, the proposed HSC algorithm represent the features in the HSV (hue, saturation and value color space and features of motion by a hypercomplex number. Secondly, the spatio-temporal salient objects are efficiently detected by hypercomplex Fourier spectral contrast in parallel. Finally, our saliency perception model also incorporates with the non-uniform sampling, which is a common phenomenon of human vision that directs visual attention to the logarithmic center of the image/video in natural scenes. The experimental results on the public saliency perception datasets demonstrate the effectiveness of the proposed approach compared to eleven state-of-the-art approaches. In addition, we extend the proposed model to moving object extraction in dynamic scenes, and the proposed algorithm is superior to the traditional algorithms.

Spatio-Temporal Data Mining for Location-Based Services

DEFF Research Database (Denmark)

Gidofalvi, Gyozo

. The objectives of the presented thesis are three-fold. First, to extend popular data mining methods to the spatio-temporal domain. Second, to demonstrate the usefulness of the extended methods and the derived knowledge in promising LBS examples. Finally, to eliminate privacy concerns in connection with spatio......-temporal data mining by devising systems for privacy-preserving location data collection and mining.......Location-Based Services (LBS) are continuously gaining popularity. Innovative LBSes integrate knowledge about the users into the service. Such knowledge can be derived by analyzing the location data of users. Such data contain two unique dimensions, space and time, which need to be analyzed...

Spatio-temporal modeling for residential burglary

NARCIS (Netherlands)

Mahfoud, M.; Bhulai, Sandjai; van der Mei, R.D.; Bhulai, Sandjai; Kardaras, Dimitris

2017-01-01

Spatio-temporal modeling is widely recognized as a promising means for predicting crime patterns. Despite their enormous potential, the available methods are still in their infancy. A lot of research focuses on crime hotspot detection and geographic crime clusters, while a systematic approach to

INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

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A. Bhushan

2015-07-01

Full Text Available In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

Assessment on spatiotemporal relationship between rainfall and cloud top temperature from new generation weather satellite imagery

Science.gov (United States)

Wei, Chiang; Yeh, Hui-Chung; Chen, Yen-Chang

2017-04-01

This study addressed the relationship between rainfall and cloud top temperature (CCT) from new generation satellite Himawari-8 imagery at different spatiotemporal scale. This satellite provides higher band, more bits for data format, spatial and temporal resolution compared with previous GMS series. The multi-infrared channels with 10-minute and 1-2 km resolution make it possible for rainfall estimating/forecasting in small/medium watershed. The preliminary result investigated at Chenyulan watershed (443.6 square kilometer) of Central Taiwan in 2016 Typhoon Megi shows the regression coefficient fitted by negative exponential equation of largest rainfall vs. CCT (B8 band) at pixel scale increases as time scales enlarges and reach 0.462 for 120-minute accumulative rainfall; the value (CTT of B15 band) decreases from 0.635 for 10-minute to 0.423 for 120-minute accumulative rainfall at basin-wide scale. More rainfall events for different regime are yet to evaluate to get solid results.

Spatio-temporal correlations in models of collective motion ruled by different dynamical laws.

Science.gov (United States)

Cavagna, Andrea; Conti, Daniele; Giardina, Irene; Grigera, Tomas S; Melillo, Stefania; Viale, Massimiliano

2016-11-15

Information transfer is an essential factor in determining the robustness of biological systems with distributed control. The most direct way to study the mechanisms ruling information transfer is to experimentally observe the propagation across the system of a signal triggered by some perturbation. However, this method may be inefficient for experiments in the field, as the possibilities to perturb the system are limited and empirical observations must rely on natural events. An alternative approach is to use spatio-temporal correlations to probe the information transfer mechanism directly from the spontaneous fluctuations of the system, without the need to have an actual propagating signal on record. Here we test this method on models of collective behaviour in their deeply ordered phase by using ground truth data provided by numerical simulations in three dimensions. We compare two models characterized by very different dynamical equations and information transfer mechanisms: the classic Vicsek model, describing an overdamped noninertial dynamics and the inertial spin model, characterized by an underdamped inertial dynamics. By using dynamic finite-size scaling, we show that spatio-temporal correlations are able to distinguish unambiguously the diffusive information transfer mechanism of the Vicsek model from the linear mechanism of the inertial spin model.

Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques.

Science.gov (United States)

Chang, Fi-John; Chen, Pin-An; Chang, Li-Chiu; Tsai, Yu-Hsuan

2016-08-15

This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling process equipped with a dynamic neural network and three refined statistical methods, for reliably predicting the TP concentrations along a river simultaneously. Two different types of artificial neural network (BPNN-static neural network; NARX network-dynamic neural network) are constructed in modeling the dynamic system. The Dahan River in Taiwan is used as a study case, where ten-year seasonal water quality data collected at seven monitoring stations along the river are used for model training and validation. Results demonstrate that the NARX network can suitably capture the important dynamic features and remarkably outperforms the BPNN model, and the SMS can effectively identify key input factors, suitably overcome data scarcity, significantly increase model reliability, satisfactorily estimate site-specific TP concentration at seven monitoring stations simultaneously, and adequately reconstruct seasonal TP data into a monthly scale. The proposed SMS can reliably model the dynamic spatio-temporal water pollution variation in a river system for missing, hazardous or costly data of interest. Copyright © 2016 Elsevier B.V. All rights reserved.

Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation

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Hui Luan

2016-09-01

Full Text Available This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA. Temporal patterns for two-hour time periods, spatial patterns at the small-area scale, and space-time interaction (i.e., unusual departures from overall spatial and temporal patterns were estimated. Temporally, calls-for-service were found to be lowest in the early morning (02:00–03:59 and highest in the evening (20:00–21:59, while high levels of calls-for-service were spatially located in central business areas and in areas characterized by major roadways, universities, and shopping centres. Space-time interaction was observed to be geographically dispersed during daytime hours but concentrated in central business areas during evening hours. Interpreted through the routine activity theory, results are discussed with respect to law enforcement resource demand and allocation, and the advantages of modeling spatio-temporal datasets with Bayesian INLA methods are highlighted.

Tensor analysis methods for activity characterization in spatiotemporal data

Energy Technology Data Exchange (ETDEWEB)

Haass, Michael Joseph; Van Benthem, Mark Hilary; Ochoa, Edward M

2014-03-01

Tensor (multiway array) factorization and decomposition offers unique advantages for activity characterization in spatio-temporal datasets because these methods are compatible with sparse matrices and maintain multiway structure that is otherwise lost in collapsing for regular matrix factorization. This report describes our research as part of the PANTHER LDRD Grand Challenge to develop a foundational basis of mathematical techniques and visualizations that enable unsophisticated users (e.g. users who are not steeped in the mathematical details of matrix algebra and mulitway computations) to discover hidden patterns in large spatiotemporal data sets.

Routes to spatiotemporal chaos in Kerr optical frequency combs.

Science.gov (United States)

Coillet, Aurélien; Chembo, Yanne K

2014-03-01

We investigate the various routes to spatiotemporal chaos in Kerr optical frequency combs, obtained through pumping an ultra-high Q-factor whispering-gallery mode resonator with a continuous-wave laser. The Lugiato-Lefever model is used to build bifurcation diagrams with regards to the parameters that are externally controllable, namely, the frequency and the power of the pumping laser. We show that the spatiotemporal chaos emerging from Turing patterns and solitons display distinctive dynamical features. Experimental spectra of chaotic Kerr combs are also presented for both cases, in excellent agreement with theoretical spectra.

Size-dependent diffusion promotes the emergence of spatiotemporal patterns

DEFF Research Database (Denmark)

Zhang, Lai; Thygesen, Uffe Høgsbro; Banerjee, Malay

2014-01-01

intraspecific physiological variations at the individual level. Here we explore the impacts of size variation within species resulting from individual ontogeny, on the emergence of spatiotemporal patterns in a fully size-structured population model. We found that size dependency of animal's diffusivity greatly......, we found that the single-generation cycle is more likely to drive spatiotemporal patterns compared to predator-prey cycles, meaning that the mechanism of Hopf bifurcation might be more common than hitherto appreciated since the former cycle is more widespread than the latter in case of interacting...

How about a Bayesian M/EEG imaging method correcting for incomplete spatio-temporal priors

DEFF Research Database (Denmark)

Stahlhut, Carsten; Attias, Hagai T.; Sekihara, Kensuke

2013-01-01

previous spatio-temporal inverse M/EEG models, the proposed model benefits of consisting of two source terms, namely, a spatio-temporal pattern term limiting the source configuration to a spatio-temporal subspace and a source correcting term to pick up source activity not covered by the spatio......-temporal prior belief. We have tested the model on both artificial data and real EEG data in order to demonstrate the efficacy of the model. The model was tested at different SNRs (-10.0,-5.2, -3.0, -1.0, 0, 0.8, 3.0 dB) using white noise. At all SNRs the sAquavit performs best in AUC measure, e.g. at SNR=0d...

Imaging collagen type I fibrillogenesis with high spatiotemporal resolution

International Nuclear Information System (INIS)

Stamov, Dimitar R; Stock, Erik; Franz, Clemens M; Jähnke, Torsten; Haschke, Heiko

2015-01-01

Fibrillar collagens, such as collagen type I, belong to the most abundant extracellular matrix proteins and they have received much attention over the last five decades due to their large interactome, complex hierarchical structure and high mechanical stability. Nevertheless, the collagen self-assembly process is still incompletely understood. Determining the real-time kinetics of collagen type I formation is therefore pivotal for better understanding of collagen type I structure and function, but visualising the dynamic self-assembly process of collagen I on the molecular scale requires imaging techniques offering high spatiotemporal resolution. Fast and high-speed scanning atomic force microscopes (AFM) provide the means to study such processes on the timescale of seconds under near-physiological conditions. In this study we have applied fast AFM tip scanning to study the assembly kinetics of fibrillar collagen type I nanomatrices with a temporal resolution reaching eight seconds for a frame size of 500 nm. By modifying the buffer composition and pH value, the kinetics of collagen fibrillogenesis can be adjusted for optimal analysis by fast AFM scanning. We furthermore show that amplitude-modulation imaging can be successfully applied to extract additional structural information from collagen samples even at high scan rates. Fast AFM scanning with controlled amplitude modulation therefore provides a versatile platform for studying dynamic collagen self-assembly processes at high resolution. - Highlights: • Continuous non-invasive time-lapse investigation of collagen I fibrillogenesis in situ. • Imaging of collagen I self-assembly with high spatiotemporal resolution. • Application of setpoint modulation to study the hierarchical structure of collagen I. • Observing real-time formation of the D-banding pattern in collagen I

Imaging collagen type I fibrillogenesis with high spatiotemporal resolution

Energy Technology Data Exchange (ETDEWEB)

Stamov, Dimitar R, E-mail: stamov@jpk.com [JPK Instruments AG, Bouchéstrasse 12, 12435 Berlin (Germany); Stock, Erik [JPK Instruments AG, Bouchéstrasse 12, 12435 Berlin (Germany); Franz, Clemens M [DFG-Center for Functional Nanostructures (CFN), Karlsruhe Institute of Technology (KIT), Wolfgang-Gaede-Strasse 1a, 76131 Karlsruhe (Germany); Jähnke, Torsten; Haschke, Heiko [JPK Instruments AG, Bouchéstrasse 12, 12435 Berlin (Germany)

2015-02-15

Fibrillar collagens, such as collagen type I, belong to the most abundant extracellular matrix proteins and they have received much attention over the last five decades due to their large interactome, complex hierarchical structure and high mechanical stability. Nevertheless, the collagen self-assembly process is still incompletely understood. Determining the real-time kinetics of collagen type I formation is therefore pivotal for better understanding of collagen type I structure and function, but visualising the dynamic self-assembly process of collagen I on the molecular scale requires imaging techniques offering high spatiotemporal resolution. Fast and high-speed scanning atomic force microscopes (AFM) provide the means to study such processes on the timescale of seconds under near-physiological conditions. In this study we have applied fast AFM tip scanning to study the assembly kinetics of fibrillar collagen type I nanomatrices with a temporal resolution reaching eight seconds for a frame size of 500 nm. By modifying the buffer composition and pH value, the kinetics of collagen fibrillogenesis can be adjusted for optimal analysis by fast AFM scanning. We furthermore show that amplitude-modulation imaging can be successfully applied to extract additional structural information from collagen samples even at high scan rates. Fast AFM scanning with controlled amplitude modulation therefore provides a versatile platform for studying dynamic collagen self-assembly processes at high resolution. - Highlights: • Continuous non-invasive time-lapse investigation of collagen I fibrillogenesis in situ. • Imaging of collagen I self-assembly with high spatiotemporal resolution. • Application of setpoint modulation to study the hierarchical structure of collagen I. • Observing real-time formation of the D-banding pattern in collagen I.

The relative importance of intrinsic and extrinsic drivers to population growth vary among local populations of Greater Sage-Grouse: An integrated population modeling approach

Science.gov (United States)

Coates, Peter S.; Prochazka, Brian G.; Ricca, Mark A.; Halstead, Brian J.; Casazza, Michael L.; Blomberg, Erik J.; Brussee, Brianne E.; Wiechman, Lief; Tebbenkamp, Joel; Gardner, Scott C.; Reese, Kerry P.

2018-01-01

Consideration of ecological scale is fundamental to understanding and managing avian population growth and decline. Empirically driven models for population dynamics and demographic processes across multiple spatial scales can be powerful tools to help guide conservation actions. Integrated population models (IPMs) provide a framework for better parameter estimation by unifying multiple sources of data (e.g., count and demographic data). Hierarchical structure within such models that include random effects allow for varying degrees of data sharing across different spatiotemporal scales. We developed an IPM to investigate Greater Sage-Grouse (Centrocercus urophasianus) on the border of California and Nevada, known as the Bi-State Distinct Population Segment. Our analysis integrated 13 years of lek count data (n > 2,000) and intensive telemetry (VHF and GPS; n > 350 individuals) data across 6 subpopulations. Specifically, we identified the most parsimonious models among varying random effects and density-dependent terms for each population vital rate (e.g., nest survival). Using a joint likelihood process, we integrated the lek count data with the demographic models to estimate apparent abundance and refine vital rate parameter estimates. To investigate effects of climatic conditions, we extended the model to fit a precipitation covariate for instantaneous rate of change (r). At a metapopulation extent (i.e. Bi-State), annual population rate of change λ (er) did not favor an overall increasing or decreasing trend through the time series. However, annual changes in λ were driven by changes in precipitation (one-year lag effect). At subpopulation extents, we identified substantial variation in λ and demographic rates. One subpopulation clearly decoupled from the trend at the metapopulation extent and exhibited relatively high risk of extinction as a result of low egg fertility. These findings can inform localized, targeted management actions for specific areas

Spatiotemporal and plantar pressure patterns of 1000 healthy individuals aged 3-101 years.

Science.gov (United States)

McKay, Marnee J; Baldwin, Jennifer N; Ferreira, Paulo; Simic, Milena; Vanicek, Natalie; Wojciechowski, Elizabeth; Mudge, Anita; Burns, Joshua

2017-10-01

The purpose of this study was to establish normative reference values for spatiotemporal and plantar pressure parameters, and to investigate the influence of demographic, anthropometric and physical characteristics. In 1000 healthy males and females aged 3-101 years, spatiotemporal and plantar pressure data were collected barefoot with the Zeno™ walkway and Emed ® platform. Correlograms were developed to visualise the relationships between widely reported spatiotemporal and pressure variables with demographic (age, gender), anthropometric (height, mass, waist circumference) and physical characteristics (ankle strength, ankle range of motion, vibration perception) in children aged 3-9 years, adolescents aged 10-19 years, adults aged 20-59 years and older adults aged over 60 years. A comprehensive catalogue of 31 spatiotemporal and pressure variables were generated from 1000 healthy individuals. The key findings were that gait velocity was stable during adolescence and adulthood, while children and older adults walked at a comparable slower speed. Peak pressures increased during childhood to older adulthood. Children demonstrated highest peak pressures beneath the rearfoot whilst adolescents, adults and older adults demonstrated highest pressures at the forefoot. Main factors influencing spatiotemporal and pressure parameters were: increased age, height, body mass and waist circumference, as well as ankle dorsiflexion and plantarflexion strength. This study has established whole of life normative reference values of widely used spatiotemporal and plantar pressure parameters, and revealed changes to be expected across the lifespan. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatiotemporal mapping of ground water pollution in a Greek lignite basin, using geostatistics

Energy Technology Data Exchange (ETDEWEB)

Modis, K. [National Technical Univ. of Athens, Athens (Greece)

2010-07-01

An issue of significant interest in the mining industry in Greece is the occurrence of chemical pollutants in ground water. Ammonium, nitrites and nitrates concentrations have been monitored through an extensive sampling network in the Ptolemais lignite opencast mining area in Greece. Due to intensive mining efforts in the area, the surface topology is continuously altered, affecting the life span of the water boreholes and resulting in messy spatiotemporal distribution of data. This paper discussed the spatiotemporal mapping of ground water pollution in the Ptolemais lignite basin, using geostatistics. More specifically, the spatiotemporal distribution of ground water contamination was examined by the application of the bayesian maximum entropy theory which allows merging spatial and temporal estimations in a single model. The paper provided a description of the site and discussed the materials and methods, including samples and statistics; variography; and spatiotemporal mapping. It was concluded that in the case of the Ptolemais mining area, results revealed an underlying average yearly variation pattern of pollutant concentrations. Inspection of the produced spatiotemporal maps demonstrated a continuous increase in the risk of ammonium contamination, while risk for the other two pollutants appeared in hot spots. 18 refs., 1 tab., 7 figs.

Spatiotemporal mapping of ground water pollution in a Greek lignite basin, using geostatistics

International Nuclear Information System (INIS)

Modis, K.

2010-01-01

An issue of significant interest in the mining industry in Greece is the occurrence of chemical pollutants in ground water. Ammonium, nitrites and nitrates concentrations have been monitored through an extensive sampling network in the Ptolemais lignite opencast mining area in Greece. Due to intensive mining efforts in the area, the surface topology is continuously altered, affecting the life span of the water boreholes and resulting in messy spatiotemporal distribution of data. This paper discussed the spatiotemporal mapping of ground water pollution in the Ptolemais lignite basin, using geostatistics. More specifically, the spatiotemporal distribution of ground water contamination was examined by the application of the bayesian maximum entropy theory which allows merging spatial and temporal estimations in a single model. The paper provided a description of the site and discussed the materials and methods, including samples and statistics; variography; and spatiotemporal mapping. It was concluded that in the case of the Ptolemais mining area, results revealed an underlying average yearly variation pattern of pollutant concentrations. Inspection of the produced spatiotemporal maps demonstrated a continuous increase in the risk of ammonium contamination, while risk for the other two pollutants appeared in hot spots. 18 refs., 1 tab., 7 figs.

Evaluation of spatial and spatiotemporal estimation methods in simulation of precipitation variability patterns

Science.gov (United States)

Bayat, Bardia; Zahraie, Banafsheh; Taghavi, Farahnaz; Nasseri, Mohsen

2013-08-01

Identification of spatial and spatiotemporal precipitation variations plays an important role in different hydrological applications such as missing data estimation. In this paper, the results of Bayesian maximum entropy (BME) and ordinary kriging (OK) are compared for modeling spatial and spatiotemporal variations of annual precipitation with and without incorporating elevation variations. The study area of this research is Namak Lake watershed located in the central part of Iran with an area of approximately 90,000 km2. The BME and OK methods have been used to model the spatial and spatiotemporal variations of precipitation in this watershed, and their performances have been evaluated using cross-validation statistics. The results of the case study have shown the superiority of BME over OK in both spatial and spatiotemporal modes. The results have shown that BME estimates are less biased and more accurate than OK. The improvements in the BME estimates are mostly related to incorporating hard and soft data in the estimation process, which resulted in more detailed and reliable results. Estimation error variance for BME results is less than OK estimations in the study area in both spatial and spatiotemporal modes.

Compressing spatio-temporal trajectories

DEFF Research Database (Denmark)

Gudmundsson, Joachim; Katajainen, Jyrki; Merrick, Damian

2009-01-01

such that the most common spatio-temporal queries can still be answered approximately after the compression has taken place. In the process, we develop an implementation of the Douglas–Peucker path-simplification algorithm which works efficiently even in the case where the polygonal path given as input is allowed...... to self-intersect. For a polygonal path of size n, the processing time is O(nlogkn) for k=2 or k=3 depending on the type of simplification....

A Hybrid Approach Combining the Multi-Temporal Scale Spatio-Temporal Network with the Continuous Triangular Model for Exploring Dynamic Interactions in Movement Data: A Case Study of Football

Directory of Open Access Journals (Sweden)

Pengdong Zhang

2018-01-01

Full Text Available Benefiting from recent advantages in location-aware technologies, movement data are becoming ubiquitous. Hence, numerous research topics with respect to movement data have been undertaken. Yet, the research of dynamic interactions in movement data is still in its infancy. In this paper, we propose a hybrid approach combining the multi-temporal scale spatio-temporal network (MTSSTN and the continuous triangular model (CTM for exploring dynamic interactions in movement data. The approach mainly includes four steps: first, the relative trajectory calculus (RTC is used to derive three types of interaction patterns; second, for each interaction pattern, a corresponding MTSSTN is generated; third, for each MTSSTN, the interaction intensity measures and three centrality measures (i.e., degree, betweenness and closeness are calculated; finally, the results are visualized at multiple temporal scales using the CTM and analyzed based on the generated CTM diagrams. Based on the proposed approach, three distinctive aims can be achieved for each interaction pattern at multiple temporal scales: (1 exploring the interaction intensities between any two individuals; (2 exploring the interaction intensities among multiple individuals, and (3 exploring the importance of each individual and identifying the most important individuals. The movement data obtained from a real football match are used as a case study to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach is useful in exploring dynamic interactions in football movement data and discovering insightful information.

Energy prediction using spatiotemporal pattern networks

Energy Technology Data Exchange (ETDEWEB)

Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun; Henze, Gregor P.; Sarkar, Soumik

2017-11-01

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.

Spatiotemporal variation in the reproductive ecology of two parapatric subspecies of Oenothera cespitosa (Onagraceae).

Science.gov (United States)

Artz, Derek R; Villagra, Cristian A; Raguso, Robert A

2010-09-01

• Flowering plants that rely on pollinators for most of their reproduction may experience unpredictable and inconsistent availability of effective pollinators throughout their reproductive lifetime. We investigated the reproductive ecology of two subspecies of the tufted evening primrose, Oenothera cespitosa, which occupy geographically and edaphically distinct habitats in western North America: O. cespitosa subsp. navajoensis inhabits sandstone soils on open sites or rocky slopes in the Colorado Plateau and O. cespitosa subsp. cespitosa grows in clay soils on talus slopes and exposed rocky ridges in the western Great Plains and northern Rocky Mountains of the United States. • Pollen augmentation and selfing experiments, floral visitor observations, and single-visit effectiveness experiments were conducted over 4 years to examine the breeding system and spatiotemporal variation in pollinator behavior, assemblage, and abundance at different populations for each subspecies. • Both subspecies of O. cespitosa were self-incompatible and pollen-limited, suggesting that the relative abundance, effectiveness, and movement patterns of different insects as pollinators influenced the quality and quantity of seed production in these plants. Medium-sized vespertine hawkmoths (Hyles lineata, Sphinx vashti) were effective pollinators when present, as were large matinal bees (Anthophora affabilis, A. dammersi, Xylocopa tabaniformis androleuca), whereas small oligolectic Lasioglossum bees primarily functioned as pollen thieves in the evening and morning. • These findings highlight the importance of variability of pollinator composition and abundance in the evolution of plant breeding systems and reproductive success at varying spatial and temporal scales.

SPAN: spike pattern association neuron for learning spatio-temporal sequences

OpenAIRE

Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

2012-01-01

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN — a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the prec...

Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan

Science.gov (United States)

Hussain, Ijaz; Spöck, Gunter; Pilz, Jürgen; Yu, Hwa-Lung

2010-08-01

Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space-time heterogeneity of rainfall observations make space-time estimation of precipitation a challenging task. In this paper we propose a Box-Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space-time monthly precipitation in the monsoon periods during 1974-2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space-time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.

Visual memory performance for color depends on spatiotemporal context.

Science.gov (United States)

Olivers, Christian N L; Schreij, Daniel

2014-10-01

Performance on visual short-term memory for features has been known to depend on stimulus complexity, spatial layout, and feature context. However, with few exceptions, memory capacity has been measured for abruptly appearing, single-instance displays. In everyday life, objects often have a spatiotemporal history as they or the observer move around. In three experiments, we investigated the effect of spatiotemporal history on explicit memory for color. Observers saw a memory display emerge from behind a wall, after which it disappeared again. The test display then emerged from either the same side as the memory display or the opposite side. In the first two experiments, memory improved for intermediate set sizes when the test display emerged in the same way as the memory display. A third experiment then showed that the benefit was tied to the original motion trajectory and not to the display object per se. The results indicate that memory for color is embedded in a richer episodic context that includes the spatiotemporal history of the display.

Selecting salient frames for spatiotemporal video modeling and segmentation.

Science.gov (United States)

Song, Xiaomu; Fan, Guoliang

2007-12-01

We propose a new statistical generative model for spatiotemporal video segmentation. The objective is to partition a video sequence into homogeneous segments that can be used as "building blocks" for semantic video segmentation. The baseline framework is a Gaussian mixture model (GMM)-based video modeling approach that involves a six-dimensional spatiotemporal feature space. Specifically, we introduce the concept of frame saliency to quantify the relevancy of a video frame to the GMM-based spatiotemporal video modeling. This helps us use a small set of salient frames to facilitate the model training by reducing data redundancy and irrelevance. A modified expectation maximization algorithm is developed for simultaneous GMM training and frame saliency estimation, and the frames with the highest saliency values are extracted to refine the GMM estimation for video segmentation. Moreover, it is interesting to find that frame saliency can imply some object behaviors. This makes the proposed method also applicable to other frame-related video analysis tasks, such as key-frame extraction, video skimming, etc. Experiments on real videos demonstrate the effectiveness and efficiency of the proposed method.

A Theoretical Basis for Entropy-Scaling Effects in Human Mobility Patterns.

Science.gov (United States)

Osgood, Nathaniel D; Paul, Tuhin; Stanley, Kevin G; Qian, Weicheng

2016-01-01

Characterizing how people move through space has been an important component of many disciplines. With the advent of automated data collection through GPS and other location sensing systems, researchers have the opportunity to examine human mobility at spatio-temporal resolution heretofore impossible. However, the copious and complex data collected through these logging systems can be difficult for humans to fully exploit, leading many researchers to propose novel metrics for encapsulating movement patterns in succinct and useful ways. A particularly salient proposed metric is the mobility entropy rate of the string representing the sequence of locations visited by an individual. However, mobility entropy rate is not scale invariant: entropy rate calculations based on measurements of the same trajectory at varying spatial or temporal granularity do not yield the same value, limiting the utility of mobility entropy rate as a metric by confounding inter-experimental comparisons. In this paper, we derive a scaling relationship for mobility entropy rate of non-repeating straight line paths from the definition of Lempel-Ziv compression. We show that the resulting formulation predicts the scaling behavior of simulated mobility traces, and provides an upper bound on mobility entropy rate under certain assumptions. We further show that this formulation has a maximum value for a particular sampling rate, implying that optimal sampling rates for particular movement patterns exist.

Bayesian spatio-temporal analysis and geospatial risk factors of human monocytic ehrlichiosis.

Directory of Open Access Journals (Sweden)

Ram K Raghavan

Full Text Available Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005-2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER], and socio-economic conditions (US Census Bureau were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005-2012, and identified poverty status, relative humidity, and an interactive factor, 'diurnal temperature range x mixed forest area' as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases.

Plant diversity increases spatio?temporal niche complementarity in plant?pollinator interactions

OpenAIRE

Venjakob, Christine; Klein, Alexandra?Maria; Ebeling, Anne; Tscharntke, Teja; Scherber, Christoph

2016-01-01

Ongoing biodiversity decline impairs ecosystem processes, including pollination. Flower visitation, an important indicator of pollination services, is influenced by plant species richness. However, the spatio-temporal responses of different pollinator groups to plant species richness have not yet been analyzed experimentally. Here, we used an experimental plant species richness gradient to analyze plant-pollinator interactions with an unprecedented spatio-temporal resolution. We observed four...

MULTI-WAVELENGTH OBSERVATIONS OF THE SPATIO-TEMPORAL EVOLUTION OF SOLAR FLARES WITH AIA/SDO. I. UNIVERSAL SCALING LAWS OF SPACE AND TIME PARAMETERS

International Nuclear Information System (INIS)

Aschwanden, Markus J.; Zhang, Jie; Liu, Kai

2013-01-01

We extend a previous statistical solar flare study of 155 GOES M- and X-class flares observed with AIA/SDO to all seven coronal wavelengths (94, 131, 171, 193, 211, 304, and 335 Å) to test the wavelength dependence of scaling laws and statistical distributions. Except for the 171 and 193 Å wavelengths, which are affected by EUV dimming caused by coronal mass ejections (CMEs), we find near-identical size distributions of geometric (lengths L, flare areas A, volumes V, and fractal dimension D 2 ), temporal (flare durations T), and spatio-temporal parameters (diffusion coefficient κ, spreading exponent β, and maximum expansion velocities v max ) in different wavelengths, which are consistent with the universal predictions of the fractal-diffusive avalanche model of a slowly driven, self-organized criticality (FD-SOC) system, i.e., N(L)∝L –3 , N(A)∝A –2 , N(V)∝V –5/3 , N(T)∝T –2 , and D 2 = 3/2, for a Euclidean dimension d = 3. Empirically, we find also a new strong correlation κ∝L 0.94±0.01 and the three-parameter scaling law L∝κ T 0.1 , which is more consistent with the logistic-growth model than with classical diffusion. The findings suggest long-range correlation lengths in the FD-SOC system that operate in the vicinity of a critical state, which could be used for predictions of individual extreme events. We find also that eruptive flares (with accompanying CMEs) have larger volumes V, longer flare durations T, higher EUV and soft X-ray fluxes, and somewhat larger diffusion coefficients κ than confined flares (without CMEs)

Reliable Collaborative Filtering on Spatio-Temporal Privacy Data

Directory of Open Access Journals (Sweden)

Zhen Liu

2017-01-01

Full Text Available Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN. When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately.

Stochastic resonance based on modulation instability in spatiotemporal chaos.

Science.gov (United States)

Han, Jing; Liu, Hongjun; Huang, Nan; Wang, Zhaolu

2017-04-03

A novel dynamic of stochastic resonance in spatiotemporal chaos is presented, which is based on modulation instability of perturbed partially coherent wave. The noise immunity of chaos can be reinforced through this effect and used to restore the coherent signal information buried in chaotic perturbation. A theoretical model with fluctuations term is derived from the complex Ginzburg-Landau equation via Wigner transform. It shows that through weakening the nonlinear threshold and triggering energy redistribution, the coherent component dominates the instability damped by incoherent component. The spatiotemporal output showing the properties of stochastic resonance may provide a potential application of signal encryption and restoration.

Secondary Instabilities and Spatiotemporal Chaos in Parametric Surface Waves

International Nuclear Information System (INIS)

Zhang, W.; Vinals, J.

1995-01-01

A 2D model is introduced to study the onset of parametric surface waves, their secondary instabilities, and the transition to spatiotemporal chaos. We obtain the stability boundary of a periodic standing wave above onset against Eckhaus, zigzag, and transverse amplitude modulations (TAM), as a function of the control parameter var-epsilon and the wavelength of the pattern. The Eckhaus and TAM boundaries cross at a finite value of var-epsilon, thus explaining the finite threshold for the TAM observed experimentally. At larger values of var-epsilon, a numerical solution reveals a transition to spatiotemporal chaotic states mediated by the TAM instability

Spatial and spatio-temporal bayesian models with R - INLA

CERN Document Server

Blangiardo, Marta

2015-01-01

Dedication iiiPreface ix1 Introduction 11.1 Why spatial and spatio-temporal statistics? 11.2 Why do we use Bayesian methods for modelling spatial and spatio-temporal structures? 21.3 Why INLA? 31.4 Datasets 32 Introduction to 212.1 The language 212.2 objects 222.3 Data and session management 342.4 Packages 352.5 Programming in 362.6 Basic statistical analysis with 393 Introduction to Bayesian Methods 533.1 Bayesian Philosophy 533.2 Basic Probability Elements 573.3 Bayes Theorem 623.4 Prior and Posterior Distributions 643.5 Working with the Posterior Distribution 663.6 Choosing the Prior Distr

Spatiotemporal variability in carbon exchange fluxes across the Sahel

DEFF Research Database (Denmark)

Tagesson, Håkan Torbern; Fensholt, Rasmus; Cappelaere, Bernard

2016-01-01

for semi-arid ecosystems. We have synthesized data on the land-atmosphere exchange of CO2 measured with the eddy covariance technique from the six existing sites across the Sahel, one of the largest semi-arid regions in the world. The overall aim of the study is to analyse and quantify the spatiotemporal...... variability in these fluxes and to analyse to which degree spatiotemporal variation can be explained by hydrological, climatic, edaphic and vegetation variables. All ecosystems were C sinks (average ± total error -162 ± 48 g C m-2 y-1), but were smaller when strongly impacted by anthropogenic influences...

Mobile technologies and the spatiotemporal configurations of institutional practice

DEFF Research Database (Denmark)

Shklovski, Irina; Troshynski, Emily; Dourish, Paul

2015-01-01

are specifically concerned with what happens to institutional roles, power relationships, and decision-making processes when a particular type of information—that of spatiotemporal location of people—is made into a technologically tradable object through the use of location-based systems. We examine...... in which broad adoption of location-based and mobile technologies has the capacity to radically reconfigure the spatiotemporal arrangement of institutional processes. The presence of digital location traces creates new forms of institutional accountability, facilitates a shift in the understood relation...... between location and action, and necessitates new models of interpretation and sense making in practice....

Spatiotemporal chaos and two-dimensional dissipative rogue waves in Lugiato-Lefever model

Science.gov (United States)

Panajotov, Krassimir; Clerc, Marcel G.; Tlidi, Mustapha

2017-06-01

Driven nonlinear optical cavities can exhibit complex spatiotemporal dynamics. We consider the paradigmatic Lugiato-Lefever model describing driven nonlinear optical resonator. This model is one of the most-studied nonlinear equations in optics. It describes a large spectrum of nonlinear phenomena from bistability, to periodic patterns, localized structures, self-pulsating localized structures and to a complex spatiotemporal behavior. The model is considered also as prototype model to describe several optical nonlinear devices such as Kerr media, liquid crystals, left handed materials, nonlinear fiber cavity, and frequency comb generation. We focus our analysis on a spatiotemporal chaotic dynamics in one-dimension. We identify a route to spatiotemporal chaos through an extended quasiperiodicity. We have estimated the Kaplan-Yorke dimension that provides a measure of the strange attractor complexity. Likewise, we show that the Lugiato-Leferver equation supports rogues waves in two-dimensional settings. We characterize rogue-wave formation by computing the probability distribution of the pulse height. Contribution to the Topical Issue "Theory and Applications of the Lugiato-Lefever Equation", edited by Yanne K. Chembo, Damia Gomila, Mustapha Tlidi, Curtis R. Menyuk.

Approximate spatio-temporal top-k publish/subscribe

KAUST Repository

Chen, Lisi

2018-04-26

Location-based publish/subscribe plays a significant role in mobile information disseminations. In this light, we propose and study a novel problem of processing location-based top-k subscriptions over spatio-temporal data streams. We define a new type of approximate location-based top-k subscription, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription, that continuously feeds users with relevant spatio-temporal messages by considering textual similarity, spatial proximity, and information freshness. Different from existing location-based top-k subscriptions, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription can automatically adjust the triggering condition by taking the triggering score of other subscriptions into account. The group filtering efficacy can be substantially improved by sacrificing the publishing result quality with a bounded guarantee. We conduct extensive experiments on two real datasets to demonstrate the performance of the developed solutions.

Approximate spatio-temporal top-k publish/subscribe

KAUST Repository

Chen, Lisi; Shang, Shuo

2018-01-01

Location-based publish/subscribe plays a significant role in mobile information disseminations. In this light, we propose and study a novel problem of processing location-based top-k subscriptions over spatio-temporal data streams. We define a new type of approximate location-based top-k subscription, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription, that continuously feeds users with relevant spatio-temporal messages by considering textual similarity, spatial proximity, and information freshness. Different from existing location-based top-k subscriptions, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription can automatically adjust the triggering condition by taking the triggering score of other subscriptions into account. The group filtering efficacy can be substantially improved by sacrificing the publishing result quality with a bounded guarantee. We conduct extensive experiments on two real datasets to demonstrate the performance of the developed solutions.

Spatio-temporal modeling of 210Pb transportation in lake environments

International Nuclear Information System (INIS)

Kuelahci, Fatih; Sen, Zekai

2009-01-01

Radioactive particle movement analysis in any environment gives valuable information about the effects of the concerned environment on the particle and the transportation phenomenon. In this study, the spatio-temporal point cumulative semivariogram (STPCSV) approach is proposed for the analysis of the spatio-temporal changes in the radioactive particle movement within a surface water body. This methodology is applied to the 210 Pb radioactive isotope measurements at 44 stations, which are determined beforehand in order to characterize the Keban Dam water environment on the Euphrates River in the southeastern part of Turkey. It considers the contributions coming from all the stations and provides information about the spatio-temporal behavior of 210 Pb in the water environment. After having identified the radii of influences at each station it is possible to draw maps for further interpretations. In order to see holistically the spatial changes of the radioisotope after 1st, 3rd and 5th hours, the radius of influence maps are prepared and interpreted accordingly.

Spatio-temporal analyses of impacts of multiple climatic hazards in a savannah ecosystem of Ghana

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Gerald A.B. Yiran, PhD

2016-01-01

Full Text Available Ghana’s savannah ecosystem has been subjected to a number of climatic hazards of varying severity. This paper presents a spatial, time-series analysis of the impacts of multiple hazards on the ecosystem and human livelihoods over the period 1983–2012, using the Upper East Region of Ghana as a case study. Our aim is to understand the nature of hazards (their frequency, magnitude and duration and how they cumulatively affect humans. Primary data were collected using questionnaires, focus group discussions, in-depth interviews and personal observations. Secondary data were collected from documents and reports. Calculations of the standard precipitation index (SPI and crop failure index used rainfall data from 4 weather stations (Manga, Binduri, Vea and Navrongo and crop yield data of 5 major crops (maize, sorghum, millet, rice and groundnuts respectively. Temperature and windstorms were analysed from the observed weather data. We found that temperatures were consistently high and increasing. From the SPI, drought frequency varied spatially from 9 at Binduri to 13 occurrences at Vea; dry spells occurred at least twice every year and floods occurred about 6 times on average, with slight spatial variations, during 1988–2012, a period with consistent data from all stations. Impacts from each hazard varied spatio-temporally. Within the study period, more 70% of years recorded severe crop losses with greater impacts when droughts and floods occur in the same year, especially in low lying areas. The effects of crop losses were higher in districts with no/little irrigation (Talensi, Nabdam, Garu-Tempane, Kassena-Nankana East. Frequency and severity of diseases and sicknesses such as cerebrospinal meningitis, heat rashes, headaches and malaria related to both dry and wet conditions have increased steadily over time. Other impacts recorded with spatio-temporal variations included destruction to housing, displacement, injury and death of people. These

Spatiotemporal electrochemical measurements across an electric double layer capacitor electrode with application to aqueous sodium hybrid batteries

Science.gov (United States)

Tully, Katherine C.; Whitacre, Jay F.; Litster, Shawn

2014-02-01

This paper presents in-situ spatiotemporal measurements of the electrolyte phase potential within an electric double layer capacitor (EDLC) negative electrode as envisaged for use in an aqueous hybrid battery for grid-scale energy storage. The ultra-thick electrodes used in these batteries to reduce non-functional material costs require sufficiently fast through-plane mass and charge transport to attain suitable charging and discharging rates. To better evaluate the through-plane transport, we have developed an electrode scaffold (ES) for making in situ electrolyte potential distribution measurements at discrete known distances across the thickness of an uninterrupted EDLC negative electrode. Using finite difference methods, we calculate local current, volumetric charging current and charge storage distributions from the spatiotemporal electrolyte potential measurements. These potential distributions provide insight into complex phenomena that cannot be directly observed using other existing methods. Herein, we use the distributions to identify areas of the electrode that are underutilized, assess the effects of various parameters on the cumulative charge storage distribution, and evaluate an effectiveness factor for charge storage in EDLC electrodes.

Associations of dragonflies (Odonata) to habitat variables within the Maltese Islands: a spatio-temporal approach.

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Balzan, Mario V

2012-01-01

Relatively little information is available on environmental associations and the conservation of Odonata in the Maltese Islands. Aquatic habitats are normally spatio-temporally restricted, often located within predominantly rural landscapes, and are thereby susceptible to farmland water management practices, which may create additional pressure on water resources. This study investigates how odonate assemblage structure and diversity are associated with habitat variables of local breeding habitats and the surrounding agricultural landscapes. Standardized survey methodology for adult Odonata involved periodical counts over selected water-bodies (valley systems, semi-natural ponds, constructed agricultural reservoirs). Habitat variables relating to the type of water body, the floristic and physiognomic characteristics of vegetation, and the composition of the surrounding landscape, were studied and analyzed through a multivariate approach. Overall, odonate diversity was associated with a range of factors across multiple spatial scales, and was found to vary with time. Lentic water-bodies are probably of high conservation value, given that larval stages were mainly associated with this habitat category, and that all species were recorded in the adult stage in this habitat type. Comparatively, lentic and lotic seminatural waterbodies were more diverse than agricultural reservoirs and brackish habitats. Overall, different odonate groups were associated with different vegetation life-forms and height categories. The presence of the great reed, Arundo donax L., an invasive alien species that forms dense stands along several water-bodies within the Islands, seems to influence the abundance and/or occurrence of a number of species. At the landscape scale, roads and other ecologically disturbed ground, surface water-bodies, and landscape diversity were associated with particular components of the odonate assemblages. Findings from this study have several implications for the

Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal

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Zhiqu Liu

2017-11-01

Full Text Available The spatiotemporal pattern of soil moisture is of great significance for the understanding of the water exchange between the land surface and the atmosphere. The two-satellite constellation of the Sentinel-1 mission provides C-band synthetic aperture radar (SAR observations with high spatial and temporal resolutions, which are suitable for soil moisture monitoring. In this paper, we aim to assess the capability of pattern analysis based on the soil moisture retrieved from Sentinel-1 time-series data of Dahra in Senegal. The look-up table (LUT method is used in the retrieval with the backscattering coefficients that are simulated by the advanced integrated equation Model (AIEM for the soil layer and the Michigan microwave canopy scattering (MIMICS model for the vegetation layer. The temporal trend of Sentinel-1A soil moisture is evaluated by the ground measurements from the site at Dahra, with an unbiased root-mean-squared deviation (ubRMSD of 0.053 m3/m3, a mean average deviation (MAD of 0.034 m3/m3, and an R value of 0.62. The spatial variation is also compared with the existing microwave products at a coarse scale, which confirms the reliability of the Sentinel-1A soil moisture. The spatiotemporal patterns are analyzed by empirical orthogonal functions (EOF, and the geophysical factors that are affecting soil moisture are discussed. The first four EOFs of soil moisture explain 77.2% of the variance in total and the primary EOF explains 66.2%, which shows the dominant pattern at the study site. Soil texture and the normalized difference vegetation index are more closely correlated with the primary pattern than the topography and temperature in the study area. The investigation confirms the potential for soil moisture retrieval and spatiotemporal pattern analysis using Sentinel-1 images.

Spatio-temporal resolved diagnostics of the single filament barrier discharge in air

International Nuclear Information System (INIS)

Wagner, H.E.; Brandenburg, R.; Michel, P.; Kozlov, K.V.

2001-01-01

First experimental results on the spatio-temporal development of single filaments of DBDs in dry air at atmospheric pressure are presented. The measurements allow a detailed visualisation and interpretation of the streamer development. In combination with the kinetic model they are used to get information on the spatiotemporal development of the reduced field-strength E/n, too

Wave-processing of long-scale information by neuronal chains.

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José Antonio Villacorta-Atienza

Full Text Available Investigation of mechanisms of information handling in neural assemblies involved in computational and cognitive tasks is a challenging problem. Synergetic cooperation of neurons in time domain, through synchronization of firing of multiple spatially distant neurons, has been widely spread as the main paradigm. Complementary, the brain may also employ information coding and processing in spatial dimension. Then, the result of computation depends also on the spatial distribution of long-scale information. The latter bi-dimensional alternative is notably less explored in the literature. Here, we propose and theoretically illustrate a concept of spatiotemporal representation and processing of long-scale information in laminar neural structures. We argue that relevant information may be hidden in self-sustained traveling waves of neuronal activity and then their nonlinear interaction yields efficient wave-processing of spatiotemporal information. Using as a testbed a chain of FitzHugh-Nagumo neurons, we show that the wave-processing can be achieved by incorporating into the single-neuron dynamics an additional voltage-gated membrane current. This local mechanism provides a chain of such neurons with new emergent network properties. In particular, nonlinear waves as a carrier of long-scale information exhibit a variety of functionally different regimes of interaction: from complete or asymmetric annihilation to transparent crossing. Thus neuronal chains can work as computational units performing different operations over spatiotemporal information. Exploiting complexity resonance these composite units can discard stimuli of too high or too low frequencies, while selectively compress those in the natural frequency range. We also show how neuronal chains can contextually interpret raw wave information. The same stimulus can be processed differently or identically according to the context set by a periodic wave train injected at the opposite end of the

Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

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Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Devers, Alexandre; Graff, Benjamin

2017-06-01

The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

The spatiotemporal variation in evapotranspiration of terrestrial ecosystems in China between 1982-2015

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Lian, X.; Piao, S.; Li, X.

2017-12-01

Evapotranspiration (ET) is one of the most important fluxes in the terrestrial ecosystem, and play a vital role in regulating atmosphere-hydrosphere-biosphere interaction. Several studies have suggested that global ET has significantly increased in the past several decades, and that such increase has exhibited big spatial variability, but there are few detailed studies on the spatio-temporal change in ET over China. Combining remote-sensing and ground-based observations with a machine learning approach (model tree ensemble, MTE), this study investigate the spatiotemporal variation in ET in China during 1982 and 2015. Our results showed that mean annual ET in China is 552±14mm year-1, which is within range of estimates by previous studies (from 430 mm year-1 to 555 mm year-1). ET spatially decreases from southeast to northwest, with highest value appeared in humidity regions (more than 1400 mm year-1) and lowest value in arid regions (less than 200 mm year-1). Over the past three decades, ET in China significantly increased by 1.07 mm year-2 with remarkable spatial heterogeneity. The largest increase in ET appears in the eastern periphery of SiChuan Basin, which may be related to increase in temperature, solar radiation as well as enhancing vegetation productivity. Only 20% of study area show decrease in ET, which is mainly located in parts of the southeast, southwest and northeast of China. The regional decrease in ET is likely to be contributed by decrease in solar radiation and relative humidity. Although our finding of the significant increase in China's ET at the country scale is supported by five different ET products, there are still less agreement on the change in ET at the regional scale among different ET products.

Neural avalanches at the critical point between replay and non-replay of spatiotemporal patterns.

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Silvia Scarpetta

Full Text Available We model spontaneous cortical activity with a network of coupled spiking units, in which multiple spatio-temporal patterns are stored as dynamical attractors. We introduce an order parameter, which measures the overlap (similarity between the activity of the network and the stored patterns. We find that, depending on the excitability of the network, different working regimes are possible. For high excitability, the dynamical attractors are stable, and a collective activity that replays one of the stored patterns emerges spontaneously, while for low excitability, no replay is induced. Between these two regimes, there is a critical region in which the dynamical attractors are unstable, and intermittent short replays are induced by noise. At the critical spiking threshold, the order parameter goes from zero to one, and its fluctuations are maximized, as expected for a phase transition (and as observed in recent experimental results in the brain. Notably, in this critical region, the avalanche size and duration distributions follow power laws. Critical exponents are consistent with a scaling relationship observed recently in neural avalanches measurements. In conclusion, our simple model suggests that avalanche power laws in cortical spontaneous activity may be the effect of a network at the critical point between the replay and non-replay of spatio-temporal patterns.

Parallel optical control of spatiotemporal neuronal spike activity using high-frequency digital light processingtechnology

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Jason eJerome

2011-08-01

Full Text Available Neurons in the mammalian neocortex receive inputs from and communicate back to thousands of other neurons, creating complex spatiotemporal activity patterns. The experimental investigation of these parallel dynamic interactions has been limited due to the technical challenges of monitoring or manipulating neuronal activity at that level of complexity. Here we describe a new massively parallel photostimulation system that can be used to control action potential firing in in vitro brain slices with high spatial and temporal resolution while performing extracellular or intracellular electrophysiological measurements. The system uses Digital-Light-Processing (DLP technology to generate 2-dimensional (2D stimulus patterns with >780,000 independently controlled photostimulation sites that operate at high spatial (5.4 µm and temporal (>13kHz resolution. Light is projected through the quartz-glass bottom of the perfusion chamber providing access to a large area (2.76 x 2.07 mm2 of the slice preparation. This system has the unique capability to induce temporally precise action potential firing in large groups of neurons distributed over a wide area covering several cortical columns. Parallel photostimulation opens up new opportunities for the in vitro experimental investigation of spatiotemporal neuronal interactions at a broad range of anatomical scales.

Dynamical Properties of Transient Spatio-Temporal Patterns in Bacterial Colony of Proteus mirabilis

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Watanabe, Kazuhiko; Wakita, Jun-ichi; Itoh, Hiroto; Shimada, Hirotoshi; Kurosu, Sayuri; Ikeda, Takemasa; Yamazaki, Yoshihiro; Matsuyama, Tohey; Matsushita, Mitsugu

2002-02-01

Spatio-temporal patterns emerged inside a colony of bacterial species Proteus mirabilis on the surface of nutrient-rich semisolid agar medium have been investigated. We observed various patterns composed of the following basic types: propagating stripe, propagating stripe with fixed dislocation, expanding and shrinking target, and rotating spiral. The remarkable point is that the pattern changes immediately when we alter the position for observation, but it returns to the original if we restore the observing position within a few minutes. We further investigated mesoscopic and microscopic properties of the spatio-temporal patterns. It turned out that whenever the spatio-temporal patterns are observed in a colony, the areas are composed of two superimposed monolayers of elongated bacterial cells. In each area they are aligned almost parallel with each other like a two-dimensional nematic liquid crystal, and move collectively and independently of another layer. It has been found that the observed spatio-temporal patterns are explained as the moiré effect.

Spatiotemporal dynamics of surface water networks across a global biodiversity hotspot—implications for conservation

International Nuclear Information System (INIS)

Tulbure, Mirela G; Broich, Mark; Kininmonth, Stuart

2014-01-01

The concept of habitat networks represents an important tool for landscape conservation and management at regional scales. Previous studies simulated degradation of temporally fixed networks but few quantified the change in network connectivity from disintegration of key features that undergo naturally occurring spatiotemporal dynamics. This is particularly of concern for aquatic systems, which typically show high natural spatiotemporal variability. Here we focused on the Swan Coastal Plain, a bioregion that encompasses a global biodiversity hotspot in Australia with over 1500 water bodies of high biodiversity. Using graph theory, we conducted a temporal analysis of water body connectivity over 13 years of variable climate. We derived large networks of surface water bodies using Landsat data (1999–2011). We generated an ensemble of 278 potential networks at three dispersal distances approximating the maximum dispersal distance of different water dependent organisms. We assessed network connectivity through several network topology metrics and quantified the resilience of the network topology during wet and dry phases. We identified ‘stepping stone’ water bodies across time and compared our networks with theoretical network models with known properties. Results showed a highly dynamic seasonal pattern of variability in network topology metrics. A decline in connectivity over the 13 years was noted with potential negative consequences for species with limited dispersal capacity. The networks described here resemble theoretical scale-free models, also known as ‘rich get richer’ algorithm. The ‘stepping stone’ water bodies are located in the area around the Peel-Harvey Estuary, a Ramsar listed site, and some are located in a national park. Our results describe a powerful approach that can be implemented when assessing the connectivity for a particular organism with known dispersal distance. The approach of identifying the surface water bodies that act as

Quantifying small-scale spatio-temporal variability of snow stratigraphy in forests based on high-resolution snow penetrometry

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Teich, M.; Hagenmuller, P.; Bebi, P.; Jenkins, M. J.; Giunta, A. D.; Schneebeli, M.

2017-12-01

Snow stratigraphy, the characteristic layering within a seasonal snowpack, has important implications for snow remote sensing, hydrology and avalanches. Forests modify snowpack properties through interception, wind speed reduction, and changes to the energy balance. The lack of snowpack observations in forests limits our ability to understand the evolution of snow stratigraphy and its spatio-temporal variability as a function of forest structure and to observe snowpack response to changes in forest cover. We examined the snowpack under canopies of a spruce forest in the central Rocky Mountains, USA, using the SnowMicroPen (SMP), a high resolution digital penetrometer. Weekly-repeated penetration force measurements were recorded along 10 m transects every 0.3 m in winter 2015 and bi-weekly along 20 m transects every 0.5 m in 2016 in three study plots beneath canopies of undisturbed, bark beetle-disturbed and harvested forest stands, and an open meadow. To disentangle information about layer hardness and depth variabilities, and to quantitatively compare the different SMP profiles, we applied a matching algorithm to our dataset, which combines several profiles by automatically adjusting their layer thicknesses. We linked spatial and temporal variabilities of penetration force and depth, and thus snow stratigraphy to forest and meteorological conditions. Throughout the season, snow stratigraphy was more heterogeneous in undisturbed but also beneath bark beetle-disturbed forests. In contrast, and despite remaining small diameter trees and woody debris, snow stratigraphy was rather homogenous at the harvested plot. As expected, layering at the non-forested plot varied only slightly over the small spatial extent sampled. At the open and harvested plots, persistent crusts and ice lenses were clearly present in the snowpack, while such hard layers barely occurred beneath undisturbed and disturbed canopies. Due to settling, hardness significantly increased with depth at

A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data

KAUST Repository

Houborg, Rasmus

2018-03-19

Satellite sensing in the visible to near-infrared (VNIR) domain has been the backbone of land surface monitoring and characterization for more than four decades. However, a limitation of conventional single-sensor satellite missions is their limited capacity to observe land surface dynamics at the very high spatial and temporal resolutions demanded by a wide range of applications. One solution to this spatio-temporal divide is an observation strategy based on the CubeSat standard, which facilitates constellations of small, inexpensive satellites. Repeatable near-daily image capture in RGB and near-infrared (NIR) bands at 3–4 m resolution has recently become available via a constellation of >130 CubeSats operated commercially by Planet. While the observing capacity afforded by this system is unprecedented, the relatively low radiometric quality and cross-sensor inconsistencies represent key challenges in the realization of their full potential as a game changer in Earth observation. To address this issue, we developed a Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) that uses a multi-scale machine-learning technique to correct for radiometric inconsistencies between CubeSat acquisitions. The CESTEM produces Landsat 8 consistent atmospherically corrected surface reflectances in blue, green, red, and NIR bands, but at the spatial scale and temporal frequency of the CubeSat observations. An application of CESTEM over an agricultural dryland system in Saudi Arabia demonstrated CubeSat-based reproduction of Landsat 8 consistent VNIR data with an overall relative mean absolute deviation of 1.6% or better, even when the Landsat 8 and CubeSat acquisitions were temporally displaced by >32 days. The consistently high retrieval accuracies were achieved using a multi-scale target sampling scheme that draws Landsat 8 reference data from a series of scenes by using MODIS-consistent surface reflectance time series to quantify relative changes in Landsat-scale

Movement reveals scale dependence in habitat selection of a large ungulate

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Northrup, Joseph; Anderson, Charles R.; Hooten, Mevin B.; Wittemyer, George

2016-01-01

Ecological processes operate across temporal and spatial scales. Anthropogenic disturbances impact these processes, but examinations of scale dependence in impacts are infrequent. Such examinations can provide important insight to wildlife–human interactions and guide management efforts to reduce impacts. We assessed spatiotemporal scale dependence in habitat selection of mule deer (Odocoileus hemionus) in the Piceance Basin of Colorado, USA, an area of ongoing natural gas development. We employed a newly developed animal movement method to assess habitat selection across scales defined using animal-centric spatiotemporal definitions ranging from the local (defined from five hour movements) to the broad (defined from weekly movements). We extended our analysis to examine variation in scale dependence between night and day and assess functional responses in habitat selection patterns relative to the density of anthropogenic features. Mule deer displayed scale invariance in the direction of their response to energy development features, avoiding well pads and the areas closest to roads at all scales, though with increasing strength of avoidance at coarser scales. Deer displayed scale-dependent responses to most other habitat features, including land cover type and habitat edges. Selection differed between night and day at the finest scales, but homogenized as scale increased. Deer displayed functional responses to development, with deer inhabiting the least developed ranges more strongly avoiding development relative to those with more development in their ranges. Energy development was a primary driver of habitat selection patterns in mule deer, structuring their behaviors across all scales examined. Stronger avoidance at coarser scales suggests that deer behaviorally mediated their interaction with development, but only to a degree. At higher development densities than seen in this area, such mediation may not be possible and thus maintenance of sufficient

Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever.

Science.gov (United States)

Raghavan, Ram K; Goodin, Douglas G; Neises, Daniel; Anderson, Gary A; Ganta, Roman R

2016-01-01

This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C) in the region, and the relevance of these factors in the context of climate-change impacts on tick-borne diseases are discussed.

The use of spatio-temporal correlation to forecast critical transitions

Science.gov (United States)

Karssenberg, Derek; Bierkens, Marc F. P.

2010-05-01

Complex dynamical systems may have critical thresholds at which the system shifts abruptly from one state to another. Such critical transitions have been observed in systems ranging from the human body system to financial markets and the Earth system. Forecasting the timing of critical transitions before they are reached is of paramount importance because critical transitions are associated with a large shift in dynamical regime of the system under consideration. However, it is hard to forecast critical transitions, because the state of the system shows relatively little change before the threshold is reached. Recently, it was shown that increased spatio-temporal autocorrelation and variance can serve as alternative early warning signal for critical transitions. However, thus far these second order statistics have not been used for forecasting in a data assimilation framework. Here we show that the use of spatio-temporal autocorrelation and variance in the state of the system reduces the uncertainty in the predicted timing of critical transitions compared to classical approaches that use the value of the system state only. This is shown by assimilating observed spatio-temporal autocorrelation and variance into a dynamical system model using a Particle Filter. We adapt a well-studied distributed model of a logistically growing resource with a fixed grazing rate. The model describes the transition from an underexploited system with high resource biomass to overexploitation as grazing pressure crosses the critical threshold, which is a fold bifurcation. To represent limited prior information, we use a large variance in the prior probability distributions of model parameters and the system driver (grazing rate). First, we show that the rate of increase in spatio-temporal autocorrelation and variance prior to reaching the critical threshold is relatively consistent across the uncertainty range of the driver and parameter values used. This indicates that an increase in

Self-organization of spatio-temporal earthquake clusters

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S. Hainzl

2000-01-01

Full Text Available Cellular automaton versions of the Burridge-Knopoff model have been shown to reproduce the power law distribution of event sizes; that is, the Gutenberg-Richter law. However, they have failed to reproduce the occurrence of foreshock and aftershock sequences correlated with large earthquakes. We show that in the case of partial stress recovery due to transient creep occurring subsequently to earthquakes in the crust, such spring-block systems self-organize into a statistically stationary state characterized by a power law distribution of fracture sizes as well as by foreshocks and aftershocks accompanying large events. In particular, the increase of foreshock and the decrease of aftershock activity can be described by, aside from a prefactor, the same Omori law. The exponent of the Omori law depends on the relaxation time and on the spatial scale of transient creep. Further investigations concerning the number of aftershocks, the temporal variation of aftershock magnitudes, and the waiting time distribution support the conclusion that this model, even "more realistic" physics in missed, captures in some ways the origin of the size distribution as well as spatio-temporal clustering of earthquakes.

Spatiotemporal approaches to analyzing pedestrian fatalities: the case of Cali, Colombia.

Science.gov (United States)

Fox, Lani; Serre, Marc L; Lippmann, Steven J; Rodríguez, Daniel A; Bangdiwala, Shrikant I; Gutiérrez, María Isabel; Escobar, Guido; Villaveces, Andrés

2015-01-01

Injuries among pedestrians are a major public health concern in Colombian cities such as Cali. This is one of the first studies in Latin America to apply Bayesian maximum entropy (BME) methods to visualize and produce fine-scale, highly accurate estimates of citywide pedestrian fatalities. The purpose of this study is to determine the BME method that best estimates pedestrian mortality rates and reduces statistical noise. We further utilized BME methods to identify and differentiate spatial patterns and persistent versus transient pedestrian mortality hotspots. In this multiyear study, geocoded pedestrian mortality data from the Cali Injury Surveillance System (2008 to 2010) and census data were utilized to accurately visualize and estimate pedestrian fatalities. We investigated the effects of temporal and spatial scales, addressing issues arising from the rarity of pedestrian fatality events using 3 BME methods (simple kriging, Poisson kriging, and uniform model Bayesian maximum entropy). To reduce statistical noise while retaining a fine spatial and temporal scale, data were aggregated over 9-month incidence periods and censal sectors. Based on a cross-validation of BME methods, Poisson kriging was selected as the best BME method. Finally, the spatiotemporal and urban built environment characteristics of Cali pedestrian mortality hotspots were linked to intervention measures provided in Mead et al.'s (2014) pedestrian mortality review. The BME space-time analysis in Cali resulted in maps displaying hotspots of high pedestrian fatalities extending over small areas with radii of 0.25 to 1.1 km and temporal durations of 1 month to 3 years. Mapping the spatiotemporal distribution of pedestrian mortality rates identified high-priority areas for prevention strategies. The BME results allow us to identify possible intervention strategies according to the persistence and built environment of the hotspot; for example, through enforcement or long-term environmental

World Spatiotemporal Analytics and Mapping Project (WSTAMP): Discovering, Exploring, and Mapping Spatiotemporal Patterns across the World s Largest Open Source Geographic Data Sets

Energy Technology Data Exchange (ETDEWEB)

Stewart, Robert N [ORNL; Piburn, Jesse O [ORNL; Sorokine, Alexandre [ORNL; Myers, Aaron T [ORNL; White, Devin A [ORNL

2015-01-01

The application of spatiotemporal (ST) analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings. Acknowledgment Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT-Battelle, LLC for the U. S. Department of Energy under contract no. DEAC05-00OR22725. Copyright This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or

Spatiotemporal complexity in coupled map lattices

International Nuclear Information System (INIS)

Kaneko, Kunihiko

1986-01-01

Some spatiotemporal patterns of couple map lattices are presented. The chaotic kink-like motions are shown for the phase motion of the coupled circle lattices. An extension of the couple map lattice approach to Hamiltonian dynamics is briefly reported. An attempt to characterize the high-dimensional attractor by the extension of the correlation dimension is discussed. (author)

A Geographic Information Science (GISc) Approach to Characterizing Spatiotemporal Patterns of Terrorist Incidents in Iraq, 2004-2009

Energy Technology Data Exchange (ETDEWEB)

Medina, Richard M [ORNL; Siebeneck, Laura K. [University of Utah; Hepner, George F. [University of Utah

2011-01-01

As terrorism on all scales continues, it is necessary to improve understanding of terrorist and insurgent activities. This article takes a Geographic Information Systems (GIS) approach to advance the understanding of spatial, social, political, and cultural triggers that influence terrorism incidents. Spatial, temporal, and spatiotemporal patterns of terrorist attacks are examined to improve knowledge about terrorist systems of training, planning, and actions. The results of this study aim to provide a foundation for understanding attack patterns and tactics in emerging havens as well as inform the creation and implementation of various counterterrorism measures.

Adjusted functional boxplots for spatio-temporal data visualization and outlier detection

KAUST Repository

Sun, Ying

2011-10-24

This article proposes a simulation-based method to adjust functional boxplots for correlations when visualizing functional and spatio-temporal data, as well as detecting outliers. We start by investigating the relationship between the spatio-temporal dependence and the 1.5 times the 50% central region empirical outlier detection rule. Then, we propose to simulate observations without outliers on the basis of a robust estimator of the covariance function of the data. We select the constant factor in the functional boxplot to control the probability of correctly detecting no outliers. Finally, we apply the selected factor to the functional boxplot of the original data. As applications, the factor selection procedure and the adjusted functional boxplots are demonstrated on sea surface temperatures, spatio-temporal precipitation and general circulation model (GCM) data. The outlier detection performance is also compared before and after the factor adjustment. © 2011 John Wiley & Sons, Ltd.

Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon.

Science.gov (United States)

Lu, Dengsheng; Li, Guiying; Moran, Emilio; Hetrick, Scott

2013-01-01

This paper provides a comparative analysis of land use and land cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired in the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes - forest, savanna, other-vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water, was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition and rates among the three study areas and indicates the importance of analyzing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g., urban expansion, roads, and land use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.

Analysis of Spatiotemporal Statistical Properties of Rainfall in the Phoenix Metropolitan Area

Science.gov (United States)

Mascaro, G.

2016-12-01

The analysis of the rainfall statistical properties at multiple spatiotemporal scales is a necessary preliminary step to support modeling of urban hydrology, including flood prediction and simulation of impacts of land use changes. In this contribution, the rainfall statistical properties are analyzed in the Phoenix Metropolitan area and its surroundings ( 29600 km2) in Arizona using observations from 310 gauges of the Flood Control District of the Maricopa County network. Different techniques are applied to investigate the rainfall properties at temporal scales from 1 min to years and to quantify the associated spatial variability. Results reveal the following. The rainfall regime is characterized by high interannual variability, which is partially explained by teleconnections with El Niño Southern Oscillation, and marked seasonality, with two maxima in the monsoon season from July to September and in winter from November to March. Elevation has a significant control on seasonal rainfall accumulation, strength of thermal convective activity during the monsoon, and peak occurrence of the rainfall diurnal cycle present in summer. The spatial correlation of wintertime rainfall is high even at short aggregation times (cells).

Identification of Watershed-scale Critical Source Areas Using Bayesian Maximum Entropy Spatiotemporal Analysis

Science.gov (United States)

Roostaee, M.; Deng, Z.

2017-12-01

The states' environmental agencies are required by The Clean Water Act to assess all waterbodies and evaluate potential sources of impairments. Spatial and temporal distributions of water quality parameters are critical in identifying Critical Source Areas (CSAs). However, due to limitations in monetary resources and a large number of waterbodies, available monitoring stations are typically sparse with intermittent periods of data collection. Hence, scarcity of water quality data is a major obstacle in addressing sources of pollution through management strategies. In this study spatiotemporal Bayesian Maximum Entropy method (BME) is employed to model the inherent temporal and spatial variability of measured water quality indicators such as Dissolved Oxygen (DO) concentration for Turkey Creek Watershed. Turkey Creek is located in northern Louisiana and has been listed in 303(d) list for DO impairment since 2014 in Louisiana Water Quality Inventory Reports due to agricultural practices. BME method is proved to provide more accurate estimates than the methods of purely spatial analysis by incorporating space/time distribution and uncertainty in available measured soft and hard data. This model would be used to estimate DO concentration at unmonitored locations and times and subsequently identifying CSAs. The USDA's crop-specific land cover data layers of the watershed were then used to determine those practices/changes that led to low DO concentration in identified CSAs. Primary results revealed that cultivation of corn and soybean as well as urban runoff are main contributing sources in low dissolved oxygen in Turkey Creek Watershed.

Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

KAUST Repository

Xie, Le

2014-01-01

We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.

Spatio-temporal patterns of the effects of precipitation variability and land use/cover changes on long-term changes in sediment yield in the Loess Plateau, China

Science.gov (United States)

Gao, Guangyao; Zhang, Jianjun; Liu, Yu; Ning, Zheng; Fu, Bojie; Sivapalan, Murugesu

2017-09-01

Within China's Loess Plateau there have been concerted revegetation efforts and engineering measures since the 1950s aimed at reducing soil erosion and land degradation. As a result, annual streamflow, sediment yield, and sediment concentration have all decreased considerably. Human-induced land use/cover change (LUCC) was the dominant factor, contributing over 70 % of the sediment load reduction, whereas the contribution of precipitation was less than 30 %. In this study, we use 50-year time series data (1961-2011), showing decreasing trends in the annual sediment loads of 15 catchments, to generate spatio-temporal patterns in the effects of LUCC and precipitation variability on sediment yield. The space-time variability of sediment yield was expressed notionally as a product of two factors representing (i) the effect of precipitation and (ii) the fraction of treated land surface area. Under minimal LUCC, the square root of annual sediment yield varied linearly with precipitation, with the precipitation-sediment load relationship showing coherent spatial patterns amongst the catchments. As the LUCC increased and took effect, the changes in sediment yield pattern depended more on engineering measures and vegetation restoration campaign, and the within-year rainfall patterns (especially storm events) also played an important role. The effect of LUCC is expressed in terms of a sediment coefficient, i.e., the ratio of annual sediment yield to annual precipitation. Sediment coefficients showed a steady decrease over the study period, following a linear decreasing function of the fraction of treated land surface area. In this way, the study has brought out the separate roles of precipitation variability and LUCC in controlling spatio-temporal patterns of sediment yield at catchment scale.

Bayesian spatiotemporal model of fMRI data using transfer functions.

Science.gov (United States)

Quirós, Alicia; Diez, Raquel Montes; Wilson, Simon P

2010-09-01

This research describes a new Bayesian spatiotemporal model to analyse BOLD fMRI studies. In the temporal dimension, we describe the shape of the hemodynamic response function (HRF) with a transfer function model. The spatial continuity and local homogeneity of the evoked responses are modelled by a Gaussian Markov random field prior on the parameter indicating activations. The proposal constitutes an extension of the spatiotemporal model presented in a previous approach [Quirós, A., Montes Diez, R. and Gamerman, D., 2010. Bayesian spatiotemporal model of fMRI data, Neuroimage, 49: 442-456], offering more flexibility in the estimation of the HRF and computational advantages in the resulting MCMC algorithm. Simulations from the model are performed in order to ascertain the performance of the sampling scheme and the ability of the posterior to estimate model parameters, as well as to check the model sensitivity to signal to noise ratio. Results are shown on synthetic data and on a real data set from a block-design fMRI experiment. Copyright (c) 2010 Elsevier Inc. All rights reserved.

UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH SPATIOTEMPORAL RELATIONAL RANDOM FORESTS

Data.gov (United States)

National Aeronautics and Space Administration — UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH SPATIOTEMPORAL RELATIONAL RANDOM FORESTS AMY MCGOVERN, TIMOTHY SUPINIE, DAVID JOHN GAGNE II, NATHANIEL TROUTMAN,...

Spatiotemporal Characteristics for the Depth from Luminance Contrast

Directory of Open Access Journals (Sweden)

Kazuya Matsubara

2011-05-01

Full Text Available Images with higher luminance contrast tend to be perceived closer in depth. To investigate a spatiotemporal characteristic of this effect, we evaluated subjective depth of a test stimulus with various spatial and temporal frequencies. For the purpose, the depth of a reference stimulus was matched to that of the test stimulus by changing the binocular disparity. The results showed that the test stimulus was perceived closer with higher luminance contrast for all conditions. Contrast efficiency was obtained from the contrast that provided the subjective depth for each spatiotemporal frequency. The shape of the contrast efficiency function was spatially low-pass and temporally band-pass. This characteristic is different from the one measure for a detection task. This suggests that only subset of contrast signals are used for depth from contrast.

Spatiotemporal patterns of childhood asthma hospitalization and utilization in Memphis Metropolitan Area from 2005 to 2015.

Science.gov (United States)

Oyana, Tonny J; Podila, Pradeep; Wesley, Jagila Minso; Lomnicki, Slawo; Cormier, Stephania

2017-10-01

To identify the key risk factors and explain the spatiotemporal patterns of childhood asthma in the Memphis metropolitan area (MMA) over an 11-year period (2005-2015). We hypothesize that in the MMA region this burden is more prevalent among urban children living south, downtown, and north of Memphis than in other areas. We used a large-scale longitudinal electronic health record database from an integrated healthcare system, Geographic information systems (GIS), and statistical and space-time models to study the spatiotemporal distributions of childhood asthma at census tract level. We found statistically significant spatiotemporal clusters of childhood asthma in the south, west, and north of Memphis city after adjusting for key covariates. The results further show a significant increase in temporal gradient in frequency of emergency department (ED) visits and inpatient hospitalizations from 2009 to 2013, and an upward trajectory from 4 per 1,000 children in 2005 to 16 per 1,000 children in 2015. The multivariate logistic regression identified age, race, insurance, admit source, encounter type, and frequency of visits as significant risk factors for childhood asthma (p asthma burden and healthcare utilization for African American (AA) patients living in a high-risk area than those living in a low-risk area in comparison to the white patients: AA vs. white [odds ratio (OR) = 3.03, 95% confidence interval (CI): 2.75-3.34]; and Hispanic vs. white (OR = 1.62, 95% CI: 1.21-2.17). These findings provide a strong basis for developing geographically tailored population health strategies at the neighborhood level for young children with chronic respiratory conditions.

Developing a comprehensive measure of mobility: mobility over varied environments scale (MOVES).

Science.gov (United States)

Hirsch, Jana A; Winters, Meghan; Sims-Gould, Joanie; Clarke, Philippa J; Ste-Marie, Nathalie; Ashe, Maureen; McKay, Heather A

2017-05-25

While recent work emphasizes the multi-dimensionality of mobility, no current measure incorporates multiple domains of mobility. Using existing conceptual frameworks we identified four domains of mobility (physical, cognitive, social, transportation) to create a "Mobility Over Varied Environments Scale" (MOVES). We then assessed expected patterns of MOVES in the Canadian population. An expert panel identified survey items within each MOVES domain from the Canadian Community Health Survey- Healthy Aging Cycle (2008-2009) for 28,555 (weighted population n = 12,805,067) adults (≥45 years). We refined MOVES using principal components analysis and Cronbach's alpha and weighted items so each domain was 10 points. Expected mobility trends, as assessed by average MOVES, were examined by sociodemographic and health factors, and by province, using Analysis of Variance (ANOVA). MOVES ranged from 0 to 40, where 0 represents individuals who are immobile and 40 those who are fully mobile. Mean MOVES was 29.58 (95% confidence interval (CI) 29.49, 29.67) (10th percentile: 24.17 (95% CI 23.96, 24.38), 90th percentile: 34.70 (CI 34.55, 34.85)). MOVES scores were lower for older, female, and non-white Canadians with worse health and lower socioeconomic status. MOVES was also lower for those who live in less urban areas. MOVES is a holistic measure of mobility for characterizing older adult mobility across populations. Future work should examine individual or neighborhood predictors of MOVES and its relationship to broader health outcomes. MOVES holds utility for research, surveillance, evaluation, and interventions around the broad factors influencing mobility in older adults.

A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets

KAUST Repository

Xu, Ganggang

2015-01-01

When spatio-temporal datasets are large, the computational burden can lead to failures in the implementation of traditional geostatistical tools. In this paper, we propose a computationally efficient Bayesian hierarchical spatio-temporal model in which the spatial dependence is approximated by a Gaussian Markov random field (GMRF) while the temporal correlation is described using a vector autoregressive model. By introducing an auxiliary lattice on the spatial region of interest, the proposed method is not only able to handle irregularly spaced observations in the spatial domain, but it is also able to bypass the missing data problem in a spatio-temporal process. Because the computational complexity of the proposed Markov chain Monte Carlo algorithm is of the order O(n) with n the total number of observations in space and time, our method can be used to handle very large spatio-temporal datasets with reasonable CPU times. The performance of the proposed model is illustrated using simulation studies and a dataset of precipitation data from the coterminous United States.

a Comparative Analysis of Spatiotemporal Data Fusion Models for Landsat and Modis Data

Science.gov (United States)

Hazaymeh, K.; Almagbile, A.

2018-04-01

In this study, three documented spatiotemporal data fusion models were applied to Landsat-7 and MODIS surface reflectance, and NDVI. The algorithms included the spatial and temporal adaptive reflectance fusion model (STARFM), sparse representation based on a spatiotemporal reflectance fusion model (SPSTFM), and spatiotemporal image-fusion model (STI-FM). The objectives of this study were to (i) compare the performance of these three fusion models using a one Landsat-MODIS spectral reflectance image pairs using time-series datasets from the Coleambally irrigation area in Australia, and (ii) quantitatively evaluate the accuracy of the synthetic images generated from each fusion model using statistical measurements. Results showed that the three fusion models predicted the synthetic Landsat-7 image with adequate agreements. The STI-FM produced more accurate reconstructions of both Landsat-7 spectral bands and NDVI. Furthermore, it produced surface reflectance images having the highest correlation with the actual Landsat-7 images. This study indicated that STI-FM would be more suitable for spatiotemporal data fusion applications such as vegetation monitoring, drought monitoring, and evapotranspiration.

Comparing infants' use of featural and spatiotemporal information when individuating objects in an event monitoring design

DEFF Research Database (Denmark)

Krøjgaard, Peter

. The results obtained using this design reveal that infants are more successful using spatiotemporal object information than when using featural information. However, recent studies using the less cognitively demanding event monitoring design have revealed that even younger infants are capable of object...... in the present series of experiments in which infants' use of spatiotemporal and featural information is compared directly using the less demanding event monitoring design. The results are discussed in relation to existing empirical evidence......., to what extent infants rely on spatiotemporal or featural object information when individuating objects is currently under debate. Hitherto, infants' use of spatiotemporal and featural object information has only been compared directly using the rather cognitively demanding event mapping design...

Augmented brain function by coordinated reset stimulation with slowly varying sequences

Directory of Open Access Journals (Sweden)

Magteld eZeitler

2015-03-01

Full Text Available Several brain disorders are characterized by abnormally strong neuronal synchrony. Coordinated Reset (CR stimulation was developed to selectively counteract abnormal neuronal synchrony by desynchronization. For this, phase resetting stimuli are delivered to different subpopulations in a timely coordinated way. In neural networks with spike timing-dependent plasticity CR stimulation may eventually lead to an anti-kindling, i.e. an unlearning of abnormal synaptic connectivity and abnormal synchrony. The spatiotemporal sequence by which all stimulation sites are stimulated exactly once is called the stimulation site sequence, or briefly sequence. So far, in simulations, pre-clinical and clinical applications CR was applied either with fixed sequences or rapidly varying sequences (RVS. In this computational study we show that appropriate repetition of the sequence with occasional random switching to the next sequence may significantly improve the anti-kindling effect of CR. To this end, a sequence is applied many times before randomly switching to the next sequence. This new method is called SVS CR stimulation, i.e. CR with slowly varying sequences. In a neuronal network with strong short-range excitatory and weak long-range inhibitory dynamic couplings SVS CR stimulation turns out to be superior to CR stimulation with fixed sequences or RVS.

Augmented brain function by coordinated reset stimulation with slowly varying sequences.

Science.gov (United States)

Zeitler, Magteld; Tass, Peter A

2015-01-01

Several brain disorders are characterized by abnormally strong neuronal synchrony. Coordinated Reset (CR) stimulation was developed to selectively counteract abnormal neuronal synchrony by desynchronization. For this, phase resetting stimuli are delivered to different subpopulations in a timely coordinated way. In neural networks with spike timing-dependent plasticity CR stimulation may eventually lead to an anti-kindling, i.e., an unlearning of abnormal synaptic connectivity and abnormal synchrony. The spatiotemporal sequence by which all stimulation sites are stimulated exactly once is called the stimulation site sequence, or briefly sequence. So far, in simulations, pre-clinical and clinical applications CR was applied either with fixed sequences or rapidly varying sequences (RVS). In this computational study we show that appropriate repetition of the sequence with occasional random switching to the next sequence may significantly improve the anti-kindling effect of CR. To this end, a sequence is applied many times before randomly switching to the next sequence. This new method is called SVS CR stimulation, i.e., CR with slowly varying sequences. In a neuronal network with strong short-range excitatory and weak long-range inhibitory dynamic couplings SVS CR stimulation turns out to be superior to CR stimulation with fixed sequences or RVS.

Integrated spatiotemporal characterization of dust sources and outbreaks in Central and East Asia

Science.gov (United States)

Darmenova, Kremena T.

The potential of atmospheric dust aerosols to modify the Earth's environment and climate has been recognized for some time. However, predicting the diverse impact of dust has several significant challenges. One is to quantify the complex spatial and temporal variability of dust burden in the atmosphere. Another is to quantify the fraction of dust originating from human-made sources. This thesis focuses on the spatiotemporal characterization of sources and dust outbreaks in Central and East Asia by integrating ground-based data, satellite multisensor observations, and modeling. A new regional dust modeling system capable of operating over a span of scales was developed. The modeling system consists of a dust module DuMo, which incorporates several dust emission schemes of different complexity, and the PSU/NCAR mesoscale model MM5, which offers a variety of physical parameterizations and flexible nesting capability. The modeling system was used to perform for the first time a comprehensive study of the timing, duration, and intensity of individual dust events in Central and East Asia. Determining the uncertainties caused by the choice of model physics, especially the boundary layer parameterization, and the dust production scheme was the focus of our study. Implications to assessments of the anthropogenic dust fraction in these regions were also addressed. Focusing on Spring 2001, an analysis of routine surface meteorological observations and satellite multi-sensor data was carried out in conjunction with modeling to determine the extent to which integrated data set can be used to characterize the spatiotemporal distribution of dust plumes at a range of temporal scales, addressing the active dust sources in China and Mongolia, mid-range transport and trans-Pacific, long-range transport of dust outbreaks on a case-by-case basis. This work demonstrates that adequate and consistent characterization of individual dust events is central to establishing a reliable

Coexistence of collapse and stable spatiotemporal solitons in multimode fibers

Science.gov (United States)

Shtyrina, Olga V.; Fedoruk, Mikhail P.; Kivshar, Yuri S.; Turitsyn, Sergei K.

2018-01-01

We analyze spatiotemporal solitons in multimode optical fibers and demonstrate the existence of stable solitons, in a sharp contrast to earlier predictions of collapse of multidimensional solitons in three-dimensional media. We discuss the coexistence of blow-up solutions and collapse stabilization by a low-dimensional external potential in graded-index media, and also predict the existence of stable higher-order nonlinear waves such as dipole-mode spatiotemporal solitons. To support the main conclusions of our numerical studies we employ a variational approach and derive analytically the stability criterion for input powers for the collapse stabilization.

Spatiotemporal chaos of self-replicating spots in reaction-diffusion systems.

Science.gov (United States)

Wang, Hongli; Ouyang, Qi

2007-11-23

The statistical properties of self-replicating spots in the reaction-diffusion Gray-Scott model are analyzed. In the chaotic regime of the system, the spots that dominate the spatiotemporal chaos grow and divide in two or decay into the background randomly and continuously. The rates at which the spots are created and decay are observed to be linearly dependent on the number of spots in the system. We derive a probabilistic description of the spot dynamics based on the statistical independence of spots and thus propose a characterization of the spatiotemporal chaos dominated by replicating spots.

Pattern control and suppression of spatiotemporal chaos using geometrical resonance

International Nuclear Information System (INIS)

Gonzalez, J.A.; Bellorin, A.; Reyes, L.I.; Vasquez, C.; Guerrero, L.E.

2004-01-01

We generalize the concept of geometrical resonance to perturbed sine-Gordon, Nonlinear Schroedinger, phi (cursive,open) Greek 4 , and Complex Ginzburg-Landau equations. Using this theory we can control different dynamical patterns. For instance, we can stabilize breathers and oscillatory patterns of large amplitudes successfully avoiding chaos. On the other hand, this method can be used to suppress spatiotemporal chaos and turbulence in systems where these phenomena are already present. This method can be generalized to even more general spatiotemporal systems. A short report of some of our results has been published in [Europhys. Lett. 64 (2003) 743

On the scaling limits of Galton Watson processes in varying environment

NARCIS (Netherlands)

Bansaye, V.; Simatos, F.

2011-01-01

Renormalized sequences of Galton Watson processes converge to Continuous State Branching Processes (CSBP), characterized by a L\\'evy triplet of two numbers and a measure. This paper investigates the case of Galton Watson processes in varying environment and provides an explicit sufficient condition

Design and implementation of segment oriented spatio-temporal model in urban panoramic maps

Science.gov (United States)

Li, Haiting; Fei, Lifan; Peng, Qingshan; Li, Yanhong

2009-10-01

Object-oriented spatio-temporal model is directed by human cognition that each object has what/where/when attributes. The precise and flexible structure of such models supports multi-semantics of space and time. This paper reviews current research of spatio-temporal models using object-oriented approach and proposed a new spatio-temporal model based on segmentation in order to resolve the updating problem of some special GIS system by taking advantages of object-oriented spatio-temporal model and adopting category theory. Category theory can be used as a unifying framework for specifying complex systems and it provides rules on how objects may be joined. It characterizes the segments of object through mappings between them. The segment-oriented spatio-temporal model designed for urban panoramic maps is described and implemented. We take points and polylines as objects in this model in the management of panoramic map data. For the randomness of routes which transportation vehicle adopts each time, road objects in this model are split into some segments by crossing points. The segments still remains polyline type, but the splitting makes it easier to update the panoramic data when new photos are captured. This model is capable of eliminating redundant data and accelerating data access when panoramas are unchanged. For evaluation purpose, the data types and operations are designed and implemented in PostgreSQL and the results of experiments come out to prove that this model is efficient and expedient in the application of urban panoramic maps.

Spatial Specificity in Spatiotemporal Encoding and Fourier Imaging

Science.gov (United States)

Goerke, Ute

2015-01-01

Purpose Ultrafast imaging techniques based on spatiotemporal-encoding (SPEN), such as RASER (rapid acquisition with sequential excitation and refocusing), is a promising new class of sequences since they are largely insensitive to magnetic field variations which cause signal loss and geometric distortion in EPI. So far, attempts to theoretically describe the point-spread-function (PSF) for the original SPEN-imaging techniques have yielded limited success. To fill this gap a novel definition for an apparent PSF is proposed. Theory Spatial resolution in SPEN-imaging is determined by the spatial phase dispersion imprinted on the acquired signal by a frequency-swept excitation or refocusing pulse. The resulting signal attenuation increases with larger distance from the vertex of the quadratic phase profile. Methods Bloch simulations and experiments were performed to validate theoretical derivations. Results The apparent PSF quantifies the fractional contribution of magnetization to a voxel’s signal as a function of distance to the voxel. In contrast, the conventional PSF represents the signal intensity at various locations. Conclusion The definition of the conventional PSF fails for SPEN-imaging since only the phase of isochromats, but not the amplitude of the signal varies. The concept of the apparent PSF is shown to be generalizable to conventional Fourier- imaging techniques. PMID:26712657

Transfer of spatio-temporal multifractal properties of rainfall to simulated surface runoff

Science.gov (United States)

Gires, Auguste; Giangola-Murzyn, Agathe; Richard, Julien; Abbes, Jean-Baptiste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Willinger, Bernard; Cardinal, Hervé; Thouvenot, Thomas

2014-05-01

In this paper we suggest to use scaling laws and more specifically Universal Multifractals (UM) to analyse in a spatio-temporal framework both the radar rainfall and the simulated surface runoff. Such tools have been extensively used to analyse and simulate geophysical fields extremely variable over wide range of spatio-temporal scales such as rainfall, but have not often if ever been applied to surface runoff. Such novel combined analysis helps to improve the understanding of the rainfall-runoff relationship. Two catchments of the chair "Hydrology for resilient cities" sponsored by Véolia, and of the European Interreg IV RainGain project are used. They are both located in the Paris area: a 144 ha flat urban area in the Seine-Saint-Denis County, and a 250 ha urban area with a significant portion of forest located on a steep hillside of the Bièvre River. A fully distributed urban hydrological model currently under development called Multi-Hydro is implemented to represent the catchments response. It consists in an interacting core between open source software packages, each of them representing a portion of the water cycle in urban environment. The fully distributed model is tested with pixels of size 5, 10 and 20 m. In a first step the model is validated for three rainfall events that occurred in 2010 and 2011, for which the Météo-France radar mosaic with a resolution of 1 km in space and 5 min in time is available. These events generated significant surface runoff and some local flooding. The sensitivity of the model to the rainfall resolution is briefly checked by stochastically generating an ensemble of realistic downscaled rainfall fields (obtained by continuing the underlying cascade process which is observed on the available range of scales) and inputting them into the model. The impact is significant on both the simulated sewer flow and surface runoff. Then rainfall fields are generated with the help of discrete multifractal cascades and inputted in the

Spatiotemporal alignment of in utero BOLD-MRI series.

Science.gov (United States)

Turk, Esra Abaci; Luo, Jie; Gagoski, Borjan; Pascau, Javier; Bibbo, Carolina; Robinson, Julian N; Grant, P Ellen; Adalsteinsson, Elfar; Golland, Polina; Malpica, Norberto

2017-08-01

To present a method for spatiotemporal alignment of in-utero magnetic resonance imaging (MRI) time series acquired during maternal hyperoxia for enabling improved quantitative tracking of blood oxygen level-dependent (BOLD) signal changes that characterize oxygen transport through the placenta to fetal organs. The proposed pipeline for spatiotemporal alignment of images acquired with a single-shot gradient echo echo-planar imaging includes 1) signal nonuniformity correction, 2) intravolume motion correction based on nonrigid registration, 3) correction of motion and nonrigid deformations across volumes, and 4) detection of the outlier volumes to be discarded from subsequent analysis. BOLD MRI time series collected from 10 pregnant women during 3T scans were analyzed using this pipeline. To assess pipeline performance, signal fluctuations between consecutive timepoints were examined. In addition, volume overlap and distance between manual region of interest (ROI) delineations in a subset of frames and the delineations obtained through propagation of the ROIs from the reference frame were used to quantify alignment accuracy. A previously demonstrated rigid registration approach was used for comparison. The proposed pipeline improved anatomical alignment of placenta and fetal organs over the state-of-the-art rigid motion correction methods. In particular, unexpected temporal signal fluctuations during the first normoxia period were significantly decreased (P quantitative studies of placental function by improving spatiotemporal alignment across placenta and fetal organs. 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:403-412. © 2017 International Society for Magnetic Resonance in Medicine.

Spatiotemporal chaos in coupled logistic maps

International Nuclear Information System (INIS)

Varella Guedes, Andre; Amorim Savi, Marcelo

2010-01-01

The objective of this work is to investigate the spatiotemporal dynamics of coupled logistic maps. These maps are prototypes of high-dimensional dynamical systems and have been used to describe the evolution and pattern formation in different systems. Here, the logistic map lattice is coupled by a power law and, therefore, each map is influenced by other maps in its neighborhood. The Kolmogorov-Sinai entropy density is employed to quantify the complexity of system behavior, permitting a general qualitative understanding of different aspects of system dynamics. Three kinds of boundary conditions are treated and the influence of initial conditions is also of concern. Non-homogeneous maps are investigated, showing interesting aspects of spatiotemporal dynamics. The idea is to analyze the spatial interaction between two qualitative different types of behavior from a grid that is split into two parts. Numerical simulations show what types of conditions present a greater tendency to develop chaotic, periodic and synchronized responses. It should be highlighted that non-homogeneous grids have situations where a chaotic pattern can emerge from two periodic responses and also situations where a periodic pattern can emerge from chaos.

Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA.

Science.gov (United States)

Cao, Xiaodong; MacNaughton, Piers; Deng, Zhengyi; Yin, Jie; Zhang, Xi; Allen, Joseph G

2018-02-02

Twitter provides a rich database of spatiotemporal information about users who broadcast their real-time opinions, sentiment, and activities. In this paper, we sought to investigate the holistic influence of land use and time period on public sentiment. A total of 880,937 tweets posted by 26,060 active users were collected across Massachusetts (MA), USA, through 31 November 2012 to 3 June 2013. The IBM Watson Alchemy API (application program interface) was employed to quantify the sentiment scores conveyed by tweets on a large scale. Then we statistically analyzed the sentiment scores across different spaces and times. A multivariate linear mixed-effects model was used to quantify the fixed effects of land use and the time period on the variations in sentiment scores, considering the clustering effect of users. The results exposed clear spatiotemporal patterns of users' sentiment. Higher sentiment scores were mainly observed in the commercial and public areas, during the noon/evening and on weekends. Our findings suggest that social media outputs can be used to better understand the spatial and temporal patterns of public happiness and well-being in cities and regions.

Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA

Directory of Open Access Journals (Sweden)

Xiaodong Cao

2018-02-01

Full Text Available Twitter provides a rich database of spatiotemporal information about users who broadcast their real-time opinions, sentiment, and activities. In this paper, we sought to investigate the holistic influence of land use and time period on public sentiment. A total of 880,937 tweets posted by 26,060 active users were collected across Massachusetts (MA, USA, through 31 November 2012 to 3 June 2013. The IBM Watson Alchemy API (application program interface was employed to quantify the sentiment scores conveyed by tweets on a large scale. Then we statistically analyzed the sentiment scores across different spaces and times. A multivariate linear mixed-effects model was used to quantify the fixed effects of land use and the time period on the variations in sentiment scores, considering the clustering effect of users. The results exposed clear spatiotemporal patterns of users’ sentiment. Higher sentiment scores were mainly observed in the commercial and public areas, during the noon/evening and on weekends. Our findings suggest that social media outputs can be used to better understand the spatial and temporal patterns of public happiness and well-being in cities and regions.

Spatio-temporal Change Patterns of Tropical Forests from 2000 to 2014 Using MOD09A1 Dataset

Science.gov (United States)

Qin, Y.; Xiao, X.; Dong, J.

2016-12-01

Large-scale deforestation and forest degradation in the tropical region have resulted in extensive carbon emissions and biodiversity loss. However, restricted by the availability of good-quality observations, large uncertainty exists in mapping the spatial distribution of forests and their spatio-temporal changes. In this study, we proposed a pixel- and phenology-based algorithm to identify and map annual tropical forests from 2000 to 2014, using the 8-day, 500-m MOD09A1 (v005) product, under the support of Google cloud computing (Google Earth Engine). A temporal filter was applied to reduce the random noises and to identify the spatio-temporal changes of forests. We then built up a confusion matrix and assessed the accuracy of the annual forest maps based on the ground reference interpreted from high spatial resolution images in Google Earth. The resultant forest maps showed the consistent forest/non-forest, forest loss, and forest gain in the pan-tropical zone during 2000 - 2014. The proposed algorithm showed the potential for tropical forest mapping and the resultant forest maps are important for the estimation of carbon emission and biodiversity loss.

Control of Spiral Waves and Spatiotemporal Chaos by Exciting Travel Wave Trains

International Nuclear Information System (INIS)

Yuan Guoyong; Wang Guangrui; Chen Shigang

2005-01-01

Spiral waves and spatiotemporal chaos usually are harmful and need to be suppressed. In this paper, a method is proposed to control them. Travel wave trains can be generated by periodic excitations near left boundary, spiral waves and spatiotemporal chaos can be eliminated by the trains for some certain excitation periods. Obvious resonant behavior can be observed from the relation between the periods of the trains and excitation ones. The method is against noise.

Description of signature scales in a floating wind turbine model wake subjected to varying turbulence intensity

Science.gov (United States)

Kadum, Hawwa; Rockel, Stanislav; Holling, Michael; Peinke, Joachim; Cal, Raul Bayon

2017-11-01

The wake behind a floating model horizontal axis wind turbine during pitch motion is investigated and compared to a fixed wind turbine wake. An experiment is conducted in an acoustic wind tunnel where hot-wire data are acquired at five downstream locations. At each downstream location, a rake of 16 hot-wires was used with placement of the probes increasing radially in the vertical, horizontal, and diagonally at 45 deg. In addition, the effect of turbulence intensity on the floating wake is examined by subjecting the wind turbine to different inflow conditions controlled through three settings in the wind tunnel grid, a passive and two active protocols, thus varying in intensity. The wakes are inspected by statistics of the point measurements, where the various length/time scales are considered. The wake characteristics for a floating wind turbine are compared to a fixed turbine, and uncovering its features; relevant as the demand for exploiting deep waters in wind energy is increasing.

Using nonlinearity and spatiotemporal property modulation to control effective structural properties: dynamic rods

DEFF Research Database (Denmark)

Thomsen, Jon Juel; Blekhman, Iliya I.

2007-01-01

What are the effective properties of a generally nonlinear material or structure, whose local properties are modulated in both space and time? It has been suggested to use spatiotemporal modulation of structural properties to create materials and structures with adjustable effective properties......, and to call these dynamic materials or spatiotemporal composites. Also, according to theoretical predictions, structural nonlinearity enhances the possibilities of achieving specific effective properties. For example, with an elastic rod having cubical elastic nonlinearities, it seems possible to control......, and exemplified. Then simple approximate analytical expressions are derived for the effective wave speed and natural frequencies for one-dimensional wave propagation in a nonlinear elastic rod, where the spatiotemporal modulation is imposed as a high-frequency standing wave, supposed to be given. Finally the more...

Partial Fourier techniques in single-shot cross-term spatiotemporal encoded MRI.

Science.gov (United States)

Zhang, Zhiyong; Frydman, Lucio

2018-03-01

Cross-term spatiotemporal encoding (xSPEN) is a single-shot approach with exceptional immunity to field heterogeneities, the images of which faithfully deliver 2D spatial distributions without requiring a priori information or using postacquisition corrections. xSPEN, however, suffers from signal-to-noise ratio penalties due to its non-Fourier nature and due to diffusion losses-especially when seeking high resolution. This study explores partial Fourier transform approaches that, acting along either the readout or the spatiotemporally encoded dimensions, reduce these penalties. xSPEN uses an orthogonal (e.g., z) gradient to read, in direct space, the low-bandwidth (e.g., y) dimension. This substantially changes the nature of partial Fourier acquisitions vis-à-vis conventional imaging counterparts. A suitable theoretical analysis is derived to implement these procedures, along either the spatiotemporally or readout axes. Partial Fourier single-shot xSPEN images were recorded on preclinical and human scanners. Owing to their reduction in the experiments' acquisition times, this approach provided substantial sensitivity gains vis-à-vis previous implementations for a given targeted in-plane resolution. The physical origins of these gains are explained. Partial Fourier approaches, particularly when implemented along the low-bandwidth spatiotemporal dimension, provide several-fold sensitivity advantages at minimal costs to the execution and processing of the single-shot experiments. Magn Reson Med 79:1506-1514, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Spatio-temporal foreshock activity during stick-slip experiments of large rock samples

Science.gov (United States)

Tsujimura, Y.; Kawakata, H.; Fukuyama, E.; Yamashita, F.; Xu, S.; Mizoguchi, K.; Takizawa, S.; Hirano, S.

2016-12-01

Foreshock activity has sometimes been reported for large earthquakes, and has been roughly classified into the following two classes. For shallow intraplate earthquakes, foreshocks occurred in the vicinity of the mainshock hypocenter (e.g., Doi and Kawakata, 2012; 2013). And for intraplate subduction earthquakes, foreshock hypocenters migrated toward the mainshock hypocenter (Kato, et al., 2012; Yagi et al., 2014). To understand how foreshocks occur, it is useful to investigate the spatio-temporal activities of foreshocks in the laboratory experiments under controlled conditions. We have conducted stick-slip experiments by using a large-scale biaxial friction apparatus at NIED in Japan (e.g., Fukuyama et al., 2014). Our previous results showed that stick-slip events repeatedly occurred in a run, but only those later events were preceded by foreshocks. Kawakata et al. (2014) inferred that the gouge generated during the run was an important key for foreshock occurrence. In this study, we proceeded to carry out stick-slip experiments of large rock samples whose interface (fault plane) is 1.5 meter long and 0.5 meter wide. After some runs to generate fault gouge between the interface. In the current experiments, we investigated spatio-temporal activities of foreshocks. We detected foreshocks from waveform records of 3D array of piezo-electric sensors. Our new results showed that more than three foreshocks (typically about twenty) had occurred during each stick-slip event, in contrast to the few foreshocks observed during previous experiments without pre-existing gouge. Next, we estimated the hypocenter locations of the stick-slip events, and found that they were located near the opposite end to the loading point. In addition, we observed a migration of foreshock hypocenters toward the hypocenter of each stick-slip event. This suggests that the foreshock activity observed in our current experiments was similar to that for the interplate earthquakes in terms of the

Spatiotemporal patterns, annual baseline and movement-related incidence of Streptococcus agalactiae infection in Danish dairy herds: 2000-2009.

Science.gov (United States)

Mweu, Marshal M; Nielsen, Søren S; Halasa, Tariq; Toft, Nils

2014-02-01

Several decades after the inception of the five-point plan for the control of contagious mastitis pathogens, Streptococcus agalactiae (S. agalactiae) persists as a fundamental threat to the dairy industry in many countries. A better understanding of the relative importance of within- and between-herd sources of new herd infections coupled with the spatiotemporal distribution of the infection, may aid in effective targeting of control efforts. Thus, the objectives of this study were: (1) to describe the spatiotemporal patterns of infection with S. agalactiae in the population of Danish dairy herds from 2000 to 2009 and (2) to estimate the annual herd-level baseline and movement-related incidence risks of S. agalactiae infection over the 10-year period. The analysis involved registry data on bacteriological culture of all bulk tank milk samples collected as part of the mandatory Danish S. agalactiae surveillance scheme as well as live cattle movements into dairy herds during the specified 10-year period. The results indicated that the predicted risk of a herd becoming infected with S. agalactiae varied spatiotemporally; the risk being more homogeneous and higher in the period after 2005. Additionally, the annual baseline risks yielded significant yet distinctive patterns before and after 2005 - the risk of infection being higher in the latter phase. On the contrary, the annual movement-related risks revealed a non-significant pattern over the 10-year period. There was neither evidence for spatial clustering of cases relative to the population of herds at risk nor spatial dependency between herds. Nevertheless, the results signal a need to beef up within-herd biosecurity in order to reduce the risk of new herd infections. Copyright © 2013 Elsevier B.V. All rights reserved.

Spatiotemporal modeling of WNV in mosquitoes in Suffolk County

Data.gov (United States)

U.S. Environmental Protection Agency — R code and dataset to produce spatial models. This dataset is associated with the following publication: Meyer, M., S. Campbell, and J. Johnston. Spatiotemporal...

Discovery of spatio-temporal patterns from location-based social networks

Science.gov (United States)

Béjar, J.; Álvarez, S.; García, D.; Gómez, I.; Oliva, L.; Tejeda, A.; Vázquez-Salceda, J.

2016-03-01

Location-based social networks (LBSNs) such as Twitter or Instagram are a good source for user spatio-temporal behaviour. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user's location information can be obtained during large intervals of time that can be used to discover complex patterns, including mobility profiles, points of interest or unusual events. These patterns can be used as the elements of a knowledge base for different applications in different domains such as mobility route planning, touristic recommendation systems or city planning. The aim of this paper is twofold, first to analyse the frequent spatio-temporal patterns that users share when living and visiting a city. This behaviour is studied by means of frequent itemsets algorithms in order to establish some associations among visits that can be interpreted as interesting routes or spatio-temporal connections. Second, to analyse how the spatio-temporal behaviour of a large number of users can be segmented in different profiles. These behavioural profiles are obtained by means of clustering algorithms that show the different patterns of behaviour of visitors and citizens. The data analysed were obtained from the public data feeds of Twitter and Instagram within an area surrounding the cities of Barcelona and Milan for a period of several months. The analysis of these data shows that these kinds of algorithms can be successfully applied to data from any city (or general area) to discover useful patterns that can be interpreted on terms of singular places and areas and their temporal relationships.

Control and characterization of spatio-temporal disorder in ...

Indian Academy of Sciences (India)

characterizing the type of spatio-temporal disorder that is embodied in this disordered ... The results from this experiment will shed light on the more general questions ... sponds to only odd or even multiples of the common frequency, ω0. Thus ...

The Spatio-Temporal Characteristics and Modeling Research of Inter-Provincial Migration in China

Directory of Open Access Journals (Sweden)

Xiaomei Fan

2018-02-01

Full Text Available The national census data during 1995 and 2000 and during 2005 and 2010 are selected in this paper to make an analysis of the spatio-temporal characteristics of the inter-provincial population migration in China. In addition, the general regression model, the extension regression model considering the historical dependent variable and the spatial lag model are established based on the gravity model to make the regression model on China’s inter-provincial population migration over two periods of time. The results show that: (1 the inter-provincial population migration increases rapidly in size with strong geographical proximity; (2 China’s inter-provincial population migration is still in the primary stage of the general process of population migration. In other words, the inter-provincial population emigration and immigration levels have increased greatly with the economic development; (3 Statistically, the inter-provincial population migration is negatively correlated with the level of economic development in the emigrant place and the migration distance and positively correlated with the level of economic development in the immigrant place and the population scale in the emigrant and immigrant places; and (4 The spatio-temporal factor is an important explanatory variable of population migration. The introduction of the historical dependent variable and the spatial lag factor can improve the regression effect of the gravity model greatly, and the historical variable and the spatial factor have strong explanatory power for the inter-provincial population migration.

Analysis of Dynamic Spatiotemporal Changes in Actual Evapotranspiration and Its Associated Factors in the Pearl River Basin Based on MOD16

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Tao Zhang

2017-11-01

Full Text Available Evapotranspiration is an important part of the hydrological cycle, surface energy balance and global climate system. Due to spatial heterogeneity, the trends in actual evapotranspiration (ET and its associated factors vary in different regions. Because direct measurements of ET are limited over large areas, remote sensing provides an efficient method of ET spatial analysis, and standard data products are available at the global scale. This study uses the monthly MOD16 ET dataset and daily meteorological data to analyze the dynamic spatiotemporal changes in ET and its associated factors in the Pearl River Basin (PRB from 2000 to 2014. The results of the study are as follows. (1 Over time and space, annual ET exhibited a slight increasing trend from 2000 to 2014, with an average value of approximately 946.56 mm/a. ET considerably varied at the monthly and seasonal scales, and in July displayed the highest monthly ET of approximately 119.57 mm, accounting for 36.37% of the annual ET. (2 ET displayed obvious spatial heterogeneity. Specifically, the west was a low-ET region, and moderate and high ET values were interspersed in the central and eastern PRB. Moreover, the rate of change of ET ranged from −13.99 mm/a to 12.81 mm/a in space, and 46.25% of the basin exhibited an increasing trend. (3 Dynamic changes in ET were mainly associated with temperature and relative humidity (RH. Additionally, energy-related elements and wind speed were positively correlated with ET, and temperature was the most influential factor of ET in some months (February–March and September–November. RH was the most important factor in other months but negatively correlated with ET in June and July. Affected by the actual environmental condition, qualitative changes were observed in the correlation between RH and ET in different months. The positive and negative spatial correlations between ET and its associated factors changed in different regions and in different

Against Laplacian Reduction of Newtonian Mass to Spatiotemporal Quantities

Science.gov (United States)

Martens, Niels C. M.

2018-03-01

Laplace wondered about the minimal choice of initial variables and parameters corresponding to a well-posed initial value problem. Discussions of Laplace's problem in the literature have focused on choosing between spatiotemporal variables relative to absolute space (i.e. substantivalism) or merely relative to other material bodies (i.e. relationalism) and between absolute masses (i.e. absolutism) or merely mass ratios (i.e. comparativism). This paper extends these discussions of Laplace's problem, in the context of Newtonian Gravity, by asking whether mass needs to be included in the initial state at all, or whether a purely spatiotemporal initial state suffices. It is argued that mass indeed needs to be included; removing mass from the initial state drastically reduces the predictive and explanatory power of Newtonian Gravity.

Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons.

Science.gov (United States)

Oddo, Calogero M; Mazzoni, Alberto; Spanne, Anton; Enander, Jonas M D; Mogensen, Hannes; Bengtsson, Fredrik; Camboni, Domenico; Micera, Silvestro; Jörntell, Henrik

2017-04-04

Investigations of the mechanisms of touch perception and decoding has been hampered by difficulties in achieving invariant patterns of skin sensor activation. To obtain reproducible spatiotemporal patterns of activation of sensory afferents, we used an artificial fingertip equipped with an array of neuromorphic sensors. The artificial fingertip was used to transduce real-world haptic stimuli into spatiotemporal patterns of spikes. These spike patterns were delivered to the skin afferents of the second digit of rats via an array of stimulation electrodes. Combined with low-noise intra- and extracellular recordings from neocortical neurons in vivo, this approach provided a previously inaccessible high resolution analysis of the representation of tactile information in the neocortical neuronal circuitry. The results indicate high information content in individual neurons and reveal multiple novel neuronal tactile coding features such as heterogeneous and complementary spatiotemporal input selectivity also between neighboring neurons. Such neuronal heterogeneity and complementariness can potentially support a very high decoding capacity in a limited population of neurons. Our results also indicate a potential neuroprosthetic approach to communicate with the brain at a very high resolution and provide a potential novel solution for evaluating the degree or state of neurological disease in animal models.

Spatiotemporal behavior and nonlinear dynamics in a phase conjugate resonator

Science.gov (United States)

Liu, Siuying Raymond

1993-01-01

The work described can be divided into two parts. The first part is an investigation of the transient behavior and stability property of a phase conjugate resonator (PCR) below threshold. The second part is an experimental and theoretical study of the PCR's spatiotemporal dynamics above threshold. The time-dependent coupled wave equations for four-wave mixing (FWM) in a photorefractive crystal, with two distinct interaction regions caused by feedback from an ordinary mirror, was used to model the transient dynamics of a PCR below threshold. The conditions for self-oscillation were determined and the solutions were used to define the PCR's transfer function and analyze its stability. Experimental results for the buildup and decay times confirmed qualitatively the predicted behavior. Experiments were carried out above threshold to study the spatiotemporal dynamics of the PCR as a function of Pragg detuning and the resonator's Fresnel number. The existence of optical vortices in the wavefront were identified by optical interferometry. It was possible to describe the transverse dynamics and the spatiotemporal instabilities by modeling the three-dimensional-coupled wave equations in photorefractive FWM using a truncated modal expansion approach.

Spatiotemporal variability analysis of diffuse radiation in China during 1981-2010

Energy Technology Data Exchange (ETDEWEB)

Ren, X.L.; Zhou, L. [Chinese Academy of Sciences, Beijing (China). Inst. of Geographic Sciences and Natural Resources Research; University of Chinese Academy of Sciences, Beijing (China); He, H.L.; Zhang, L.; Yu, G.R.; Fan, J.W. [Chinese Academy of Sciences, Beijing (China). Inst. of Geographic Sciences and Natural Resources Research

2013-03-01

Solar radiation is the primary driver of terrestrial plant photosynthesis and the diffuse component can enhance canopy light use efficiency (LUE), which in turn influences the carbon balance of terrestrial ecosystems. In this study we calculated the spatial data of diffuse radiation in China from 1981 to 2010, using a radiation decomposition model and spatial interpolation method based on observational data. Furthermore, we explored the spatiotemporal characteristics of diffuse radiation using GIS and trend analysis techniques. The results show the following: (1) The spatial patterns of perennial average of annual diffuse radiation during 1981-2010 are complex and inhomogeneous in China, generally lower in the north and higher in the south and west. The perennial average ranges from 1730.20 to 3064.41 MJm{sup -2}yr{sup -1} across the whole country. (2) There is an increasing trend of annual diffuse radiation in China from 1981 to 2010 on the whole, with mean increasing amplitude of 7.03 MJm{sup -2}yr{sup -1} per decade. Whereas a significant downtrend was observed in the first 10 years, distinct anomalies in 1982, 1983, 1991 and 1992 occurred due to the eruptions of El Chinchon and Pinatubo. (3) The spatial distribution of the temporal variability of diffuse radiation showed significant regional heterogeneity in addition to the seasonal differences. Northwestern China has the most evident downtrend, with highest decreasing rate of 6% per decade, while the Tibetan Plateau has the most evident uptrend, with highest increasing rate of up to 9% per decade. Such quantitative spatiotemporal characteristics of diffuse radiation are essential in regional scale modeling of terrestrial carbon dynamics. (orig.)

Different horse's paces during hippotherapy on spatio-temporal parameters of gait in children with bilateral spastic cerebral palsy: A feasibility study.

Science.gov (United States)

Antunes, Fabiane Nunes; Pinho, Alexandre Severo do; Kleiner, Ana Francisca Rozin; Salazar, Ana Paula; Eltz, Giovana Duarte; de Oliveira Junior, Alcyr Alves; Cechetti, Fernanda; Galli, Manuela; Pagnussat, Aline Souza

2016-12-01

Hippotherapy is often carried out for the rehabilitation of children with Cerebral Palsy (CP), with the horse riding at a walking pace. This study aimed to explore the immediate effects of a hippotherapy protocol using a walk-trot pace on spatio-temporal gait parameters and muscle tone in children with Bilateral Spastic CP (BS-CP). Ten children diagnosed with BS-CP and 10 healthy aged-matched children (reference group) took part in this study. The children with BS-CP underwent two sessions of hippotherapy for one week of washout between them. Two protocols (lasting 30min) were applied on separate days: Protocol 1: the horse's pace was a walking pace; and Protocol 2: the horse's pace was a walk-trot pace. Children from the reference group were not subjected to treatment. A wireless inertial measurement unit measured gait spatio-temporal parameters before and after each session. The Modified Ashworth Scale was applied for muscle tone measurement of hip adductors. The participants underwent the gait assessment on a path with surface irregularities (ecological context). The comparisons between BS-CP and the reference group found differences in all spatio-temporal parameters, except for gait velocity. Within-group analysis of children with BS-CP showed that the swing phase did not change after the walk pace and after the walk-trot pace. The percentage of rolling phase and double support improved after the walk-trot. The spasticity of the hip adductors was significantly reduced as an immediate result of both protocols, but this decrease was more evident after the walk-trot. The walk-trot protocol is feasible and is able to induce an immediate effect that improves the gait spatio-temporal parameters and the hip adductors spasticity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Regionalised spatiotemporal rainfall and temperature models for flood studies in the Basque Country, Spain

Directory of Open Access Journals (Sweden)

P. Cowpertwait

2013-02-01

Full Text Available A spatiotemporal point process model of rainfall is fitted to data taken from three homogeneous regions in the Basque Country, Spain. The model is the superposition of two spatiotemporal Neyman–Scott processes, in which rain cells are modelled as discs with radii that follow exponential distributions. In addition, the model includes a parameter for the radius of storm discs, so that rain only occurs when both a cell and a storm disc overlap a point. The model is fitted to data for each month, taken from each of the three homogeneous regions, using a modified method of moments procedure that ensures a smooth seasonal variation in the parameter estimates.

Daily temperature data from 23 sites are used to fit a stochastic temperature model. A principal component analysis of the maximum daily temperatures across the sites indicates that 92% of the variance is explained by the first component, implying that this component can be used to account for spatial variation. A harmonic equation with autoregressive error terms is fitted to the first principal component. The temperature model is obtained by regressing the maximum daily temperature on the first principal component, an indicator variable for the region, and altitude. This, together with scaling and a regression model of temperature range, enables hourly temperatures to be predicted. Rainfall is included as an explanatory variable but has only a marginal influence when predicting temperatures.

A distributed model (TETIS; Francés et al., 2007 is calibrated for a selected catchment. Five hundred years of data are simulated using the rainfall and temperature models and used as input to the calibrated TETIS model to obtain simulated discharges to compare with observed discharges. Kolmogorov–Smirnov tests indicate that there is no significant difference in the distributions of observed and simulated maximum flows at the same sites, thus supporting the use of the spatiotemporal

Spatiotemporal resonances in mixing of open viscous fluids

DEFF Research Database (Denmark)

Okkels, Fridolin; Tabeling, Patrick

2004-01-01

In this Letter, we reveal a new dynamical phenomenon, called "spatiotemporal resonance," which is expected to take place in a broad range of viscous, periodically forced, open systems. The observation originates from a numerical and theoretical analysis of a micromixer, and is supported...

Evaluating the disparity of female breast cancer mortality among racial groups - a spatiotemporal analysis

Directory of Open Access Journals (Sweden)

Jacobson Holly

2004-02-01

groups at varying levels. There was neither evidence of hot-spot clusters nor persistent spatiotemporal trends of excess mortality into the present decade. Non-Hispanic Whites in the Gulf Coast and Hispanics in West Texas carried the highest burden of mortality, as evidenced by spatial concentration and temporal persistence.

Nonlinear Spatio-Temporal Dynamics and Chaos in Semiconductors

Science.gov (United States)

Schöll, Eckehard

2005-08-01

Nonlinear transport phenomena are an increasingly important aspect of modern semiconductor research. This volume deals with complex nonlinear dynamics, pattern formation, and chaotic behavior in such systems. It bridges the gap between two well-established fields: the theory of dynamic systems and nonlinear charge transport in semiconductors. This unified approach helps reveal important electronic transport instabilities. The initial chapters lay a general framework for the theoretical description of nonlinear self-organized spatio-temporal patterns, such as current filaments, field domains, fronts, and analysis of their stability. Later chapters consider important model systems in detail: impact ionization induced impurity breakdown, Hall instabilities, superlattices, and low-dimensional structures. State-of-the-art results include chaos control, spatio-temporal chaos, multistability, pattern selection, activator-inhibitor kinetics, and global coupling, linking fundamental issues to electronic device applications. This book will be of great value to semiconductor physicists and nonlinear scientists alike.

A novel method for one-way hash function construction based on spatiotemporal chaos

Energy Technology Data Exchange (ETDEWEB)

Ren Haijun [College of Software Engineering, Chongqing University, Chongqing 400044 (China); State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044 (China)], E-mail: jhren@cqu.edu.cn; Wang Yong; Xie Qing [Key Laboratory of Electronic Commerce and Logistics of Chongqing, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China); Yang Huaqian [Department of Computer and Modern Education Technology, Chongqing Education of College, Chongqing 400067 (China)

2009-11-30

A novel hash algorithm based on a spatiotemporal chaos is proposed. The original message is first padded with zeros if needed. Then it is divided into a number of blocks each contains 32 bytes. In the hashing process, each block is partitioned into eight 32-bit values and input into the spatiotemporal chaotic system. Then, after iterating the system for four times, the next block is processed by the same way. To enhance the confusion and diffusion effect, the cipher block chaining (CBC) mode is adopted in the algorithm. The hash value is obtained from the final state value of the spatiotemporal chaotic system. Theoretic analyses and numerical simulations both show that the proposed hash algorithm possesses good statistical properties, strong collision resistance and high efficiency, as required by practical keyed hash functions.

A novel method for one-way hash function construction based on spatiotemporal chaos

International Nuclear Information System (INIS)

Ren Haijun; Wang Yong; Xie Qing; Yang Huaqian

2009-01-01

A novel hash algorithm based on a spatiotemporal chaos is proposed. The original message is first padded with zeros if needed. Then it is divided into a number of blocks each contains 32 bytes. In the hashing process, each block is partitioned into eight 32-bit values and input into the spatiotemporal chaotic system. Then, after iterating the system for four times, the next block is processed by the same way. To enhance the confusion and diffusion effect, the cipher block chaining (CBC) mode is adopted in the algorithm. The hash value is obtained from the final state value of the spatiotemporal chaotic system. Theoretic analyses and numerical simulations both show that the proposed hash algorithm possesses good statistical properties, strong collision resistance and high efficiency, as required by practical keyed hash functions.

Synchronization of spatiotemporal chaotic systems and application to secure communication of digital image

International Nuclear Information System (INIS)

Wang Xing-Yuan; Zhang Na; Ren Xiao-Li; Zhang Yong-Lei

2011-01-01

Coupled map lattices (CMLs) are taken as examples to study the synchronization of spatiotemporal chaotic systems. In this paper, we use the nonlinear coupled method to implement the synchronization of two coupled map lattices. Through the appropriate separation of the linear term from the nonlinear term of the spatiotemporal chaotic system, we set the nonlinear term as the coupling function and then we can achieve the synchronization of two coupled map lattices. After that, we implement the secure communication of digital image using this synchronization method. Then, the discrete characteristics of the nonlinear coupling spatiotemporal chaos are applied to the discrete pixel of the digital image. After the synchronization of both the communication parties, the receiver can decrypt the original image. Numerical simulations show the effectiveness and the feasibility of the proposed program. (general)

Spatio-temporal patterns in simple models of marine systems

Science.gov (United States)

Feudel, U.; Baurmann, M.; Gross, T.

2009-04-01

Spatio-temporal patterns in marine systems are a result of the interaction of population dynamics with physical transport processes. These physical transport processes can be either diffusion processes in marine sediments or in the water column. We study the dynamics of one population of bacteria and its nutrient in in a simplified model of a marine sediments, taking into account that the considered bacteria possess an active as well as an inactive state, where activation is processed by signal molecules. Furthermore the nutrients are transported actively by bioirrigation and passively by diffusion. It is shown that under certain conditions Turing patterns can occur which yield heterogeneous spatial patterns of the species. The influence of bioirrigation on Turing patterns leads to the emergence of ''hot spots``, i.e. localized regions of enhanced bacterial activity. All obtained patterns fit quite well to observed patterns in laboratory experiments. Spatio-temporal patterns appear in a predator-prey model, used to describe plankton dynamics. These patterns appear due to the simultaneous emergence of Turing patterns and oscillations in the species abundance in the neighborhood of a Turing-Hopf bifurcation. We observe a large variety of different patterns where i) stationary heterogeneous patterns (e.g. hot and cold spots) compete with spatio-temporal patterns ii) slowly moving patterns are embedded in an oscillatory background iii) moving fronts and spiral waves appear.

Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever.

Directory of Open Access Journals (Sweden)

Ram K Raghavan

Full Text Available This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C in the region, and the relevance of these factors in the context of climate-change impacts on tick-borne diseases are discussed.

Intermediate-scale Fire Performance of Composite Panels under Varying Loads

Energy Technology Data Exchange (ETDEWEB)

Brown, Alexander [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jernigan, Dann A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Dodd, Amanda B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-04-01

New aircraft are being designed with increasing quantities of composite materials used in their construction. Different from the more traditional metals, composites have a higher propensity to burn. This presents a challenge to transportation safety analyses, as the aircraft structure now represents an additional fuel source involved in the fire scenario. Most of the historical fire testing of composite materials is aime d at studying kinetics, flammability or yield strength under fire conditions. Most of this testing is small - scale. Heterogeneous reactions are often length - scale dependent, and this is thought to be particularly true for composites which exhibit signific ant microscopic dynamics that can affect macro - scale behavior. We have designed a series of tests to evaluate composite materials under various structural loading conditions with a consistent thermal condition. We have measured mass - loss , heat flux, and temperature throughout the experiments. Several types of panels have been tested, including simple composite panels, and sandwich panels. The main objective of the testing was to understand the importance of the structural loading on a composite to its b ehavior in response to fire - like conditions. During flaming combustion at early times, there are some features of the panel decomposition that are unique to the type of loading imposed on the panels. At load levels tested, fiber reaction rates at later t imes appear to be independent of the initial structural loading.

Spatio-temporal diffusion of dynamic PET images

International Nuclear Information System (INIS)

Tauber, C; Chalon, S; Guilloteau, D; Stute, S; Buvat, I; Chau, M; Spiteri, P

2011-01-01

Positron emission tomography (PET) images are corrupted by noise. This is especially true in dynamic PET imaging where short frames are required to capture the peak of activity concentration after the radiotracer injection. High noise results in a possible bias in quantification, as the compartmental models used to estimate the kinetic parameters are sensitive to noise. This paper describes a new post-reconstruction filter to increase the signal-to-noise ratio in dynamic PET imaging. It consists in a spatio-temporal robust diffusion of the 4D image based on the time activity curve (TAC) in each voxel. It reduces the noise in homogeneous areas while preserving the distinct kinetics in regions of interest corresponding to different underlying physiological processes. Neither anatomical priors nor the kinetic model are required. We propose an automatic selection of the scale parameter involved in the diffusion process based on a robust statistical analysis of the distances between TACs. The method is evaluated using Monte Carlo simulations of brain activity distributions. We demonstrate the usefulness of the method and its superior performance over two other post-reconstruction spatial and temporal filters. Our simulations suggest that the proposed method can be used to significantly increase the signal-to-noise ratio in dynamic PET imaging.

Spatiotemporal Interpolation of Rainfall by Combining BME Theory and Satellite Rainfall Estimates

Directory of Open Access Journals (Sweden)

Tingting Shi

2015-09-01

Full Text Available The accurate assessment of spatiotemporal rainfall variability is a crucial and challenging task in many hydrological applications, mainly due to the lack of a sufficient number of rain gauges. The purpose of the present study is to investigate the spatiotemporal variations of annual and monthly rainfall over Fujian province in China by combining the Bayesian maximum entropy (BME method and satellite rainfall estimates. Specifically, based on annual and monthly rainfall data at 20 meteorological stations from 2000 to 2012, (1 the BME method with Tropical Rainfall Measuring Mission (TRMM estimates considered as soft data, (2 ordinary kriging (OK and (3 cokriging (CK were employed to model the spatiotemporal variations of rainfall in Fujian province. Subsequently, the performance of these methods was evaluated using cross-validation statistics. The results demonstrated that BME with TRMM as soft data (BME-TRMM performed better than the other two methods, generating rainfall maps that represented the local rainfall disparities in a more realistic manner. Of the three interpolation (mapping methods, the mean absolute error (MAE and root mean square error (RMSE values of the BME-TRMM method were the smallest. In conclusion, the BME-TRMM method improved spatiotemporal rainfall modeling and mapping by integrating hard data and soft information. Lastly, the study identified new opportunities concerning the application of TRMM rainfall estimates.

Global spatiotemporal distribution of soil respiration modeled using a global database

Science.gov (United States)

Hashimoto, S.; Carvalhais, N.; Ito, A.; Migliavacca, M.; Nishina, K.; Reichstein, M.

2015-07-01

The flux of carbon dioxide from the soil to the atmosphere (soil respiration) is one of the major fluxes in the global carbon cycle. At present, the accumulated field observation data cover a wide range of geographical locations and climate conditions. However, there are still large uncertainties in the magnitude and spatiotemporal variation of global soil respiration. Using a global soil respiration data set, we developed a climate-driven model of soil respiration by modifying and updating Raich's model, and the global spatiotemporal distribution of soil respiration was examined using this model. The model was applied at a spatial resolution of 0.5°and a monthly time step. Soil respiration was divided into the heterotrophic and autotrophic components of respiration using an empirical model. The estimated mean annual global soil respiration was 91 Pg C yr-1 (between 1965 and 2012; Monte Carlo 95 % confidence interval: 87-95 Pg C yr-1) and increased at the rate of 0.09 Pg C yr-2. The contribution of soil respiration from boreal regions to the total increase in global soil respiration was on the same order of magnitude as that of tropical and temperate regions, despite a lower absolute magnitude of soil respiration in boreal regions. The estimated annual global heterotrophic respiration and global autotrophic respiration were 51 and 40 Pg C yr-1, respectively. The global soil respiration responded to the increase in air temperature at the rate of 3.3 Pg C yr-1 °C-1, and Q10 = 1.4. Our study scaled up observed soil respiration values from field measurements to estimate global soil respiration and provide a data-oriented estimate of global soil respiration. The estimates are based on a semi-empirical model parameterized with over one thousand data points. Our analysis indicates that the climate controls on soil respiration may translate into an increasing trend in global soil respiration and our analysis emphasizes the relevance of the soil carbon flux from soil to

Propagation and spatiotemporal coupling characteristics of ultra-short Gaussian vortex pulse

Science.gov (United States)

Nie, Jianye; Liu, Guodong; Zhang, Rongzhu

2018-05-01

Based on Collins diffraction integral formula, the propagation equation of ultra-short Gaussian vortex pulse beam has been derived. Using the equation, the intensity distribution variations of vortex pulse in the propagation process are calculated. Specially, the spatiotemporal coupling characteristics of ultra-short vortex beams are discussed in detail. The results show that some key parameters, such as transverse distance, transmission distance, pulse width and topological charge number will influence the spatiotemporal coupling characteristics significantly. With the increasing of transverse distance, the waveforms of the pulses distort obviously. And when transmission distance is far than 50 mm, the distribution curve of transverse intensity gradually changes into a Gaussian type. In addition, initial pulse width will affect the distribution of light field, however, when initial pulse width is larger than 3 fs, the spatiotemporal coupling effect will be insignificant. Topological charge number does not affect the time delay characteristics, since with the increasing of topological charge number, the waveform of the pulse distorts gradually but the time delay does not occur.

Spatio-temporal patterns of coral recruitment at Vamizi Island ...

African Journals Online (AJOL)

Spatio-temporal patterns of coral recruitment at Vamizi Island, Quirimbas Archipelago, Mozambique. ... Spatial and temporal patterns of recruitment of reef corals were assessed for the first time in Mozambique ... AJOL African Journals Online.

Role of Temporal Diversity in Inferring Social Ties Based on Spatio-Temporal Data

OpenAIRE

Desai, Deshana; Nisar, Harsh; Bhardawaj, Rishab

2016-01-01

The last two decades have seen a tremendous surge in research on social networks and their implications. The studies includes inferring social relationships, which in turn have been used for target advertising, recommendations, search customization etc. However, the offline experiences of human, the conversations with people and face-to-face interactions that govern our lives interactions have received lesser attention. We introduce DAIICT Spatio-Temporal Network (DSSN), a spatiotemporal data...

Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation

OpenAIRE

Le Wang; Xuhuan Duan; Qilin Zhang; Zhenxing Niu; Gang Hua; Nanning Zheng

2018-01-01

Inspired by the recent spatio-temporal action localization efforts with tubelets (sequences of bounding boxes), we present a new spatio-temporal action localization detector Segment-tube, which consists of sequences of per-frame segmentation masks. The proposed Segment-tube detector can temporally pinpoint the starting/ending frame of each action category in the presence of preceding/subsequent interference actions in untrimmed videos. Simultaneously, the Segment-tube detector produces per-fr...

A collaborative large spatio-temporal data visual analytics architecture for emergence response

International Nuclear Information System (INIS)

Guo, D; Li, J; Zhou, Y; Cao, H

2014-01-01

The unconventional emergency, usually outbreaks more suddenly, and is diffused more quickly, but causes more secondary damage and derives more disaster than what it is usually expected. The data volume and urgency of emergency exceeds the capacity of current emergency management systems. In this paper, we propose a three-tier collaborative spatio-temporal visual analysis architecture to support emergency management. The prototype system, based on cloud computation environment, supports aggregation of massive unstructured and semi-structured data, integration of various computing model sand algorithms; collaborative visualization and visual analytics among users with a diversity of backgrounds. The distributed data in 100TB scale is integrated in a unified platform and shared with thousands of experts and government agencies by nearly 100 models. The users explore, visualize and analyse the big data and make a collaborative countermeasures to emergencies

Spatio-Temporal Analysis of Human Activities in Indoor Environments through Mobile Sensing

DEFF Research Database (Denmark)

Prentow, Thor Siiger

with the intuition and personal experience of the planners. Lack of real-time information on task execution has made it difficult to adapt to changes in the schedules, such as delays or suddenly occurring urgent tasks. The recent advances in methods and devices for mobile sensing provides opportunities...... methods for spatio-temporal analysis of human activities in indoor environments based on mobile sensing. The methods aim to improve scheduling and facility utilization by providing information on the used route networks, transportation modes, travel times, and the flow of people through buildings....... The methods are based on large-scale real-time indoor positioning through the use of existing WiFi infrastructures, which allows for easy deployment even in very large building complexes. The methods are designed for real-time operation, which enables them to detect and adjust to changes as they occur...

A topological approach to migration and visualization of time-varying volume data

International Nuclear Information System (INIS)

Fujishiro, Issei; Otsuka, Rieko; Hamaoka, Aya; Takeshima, Yuriko; Takahashi, Shigeo

2004-01-01

Rapid advance in high performance computing and measurement technologies has recently made it possible to produce a stupendous amount of time-varying volume datasets in various disciplines. However, there exist a few known visual exploration tools which allow us to investigate the core of their complex behavior effectively. In this article, our previous approach to topological volume skeletonization is extended to capture the topological skeleton of a 4D volumetric field in terms of critical timing. A cyclic information drilldown scheme, termed T-map, is presented, where a wide choice of information visualization techniques are deployed so that the users are allowed to repeatedly squeeze partial spatiotemporal domains of interest until the size gets fitted into an available computing storage space, prior to topologically-accentuated visualization of the pinpointed volumetric domains. A case study with datasets from atomic collision research is performed to illustrate the feasibility of the present method. (author)

Large-scale electrophysiology: acquisition, compression, encryption, and storage of big data.

Science.gov (United States)

Brinkmann, Benjamin H; Bower, Mark R; Stengel, Keith A; Worrell, Gregory A; Stead, Matt

2009-05-30

The use of large-scale electrophysiology to obtain high spatiotemporal resolution brain recordings (>100 channels) capable of probing the range of neural activity from local field potential oscillations to single-neuron action potentials presents new challenges for data acquisition, storage, and analysis. Our group is currently performing continuous, long-term electrophysiological recordings in human subjects undergoing evaluation for epilepsy surgery using hybrid intracranial electrodes composed of up to 320 micro- and clinical macroelectrode arrays. DC-capable amplifiers, sampling at 32kHz per channel with 18-bits of A/D resolution are capable of resolving extracellular voltages spanning single-neuron action potentials, high frequency oscillations, and high amplitude ultra-slow activity, but this approach generates 3 terabytes of data per day (at 4 bytes per sample) using current data formats. Data compression can provide several practical benefits, but only if data can be compressed and appended to files in real-time in a format that allows random access to data segments of varying size. Here we describe a state-of-the-art, scalable, electrophysiology platform designed for acquisition, compression, encryption, and storage of large-scale data. Data are stored in a file format that incorporates lossless data compression using range-encoded differences, a 32-bit cyclically redundant checksum to ensure data integrity, and 128-bit encryption for protection of patient information.

An Online Atlas for Exploring Spatio-Temporal Patterns of Cancer Mortality (1972-2011) and Incidence (1995-2008) in Taiwan.

Science.gov (United States)

Ku, Wen-Yuan; Liaw, Yung-Po; Huang, Jing-Yang; Nfor, Oswald Ndi; Hsu, Shu-Yi; Ko, Pei-Chieh; Lee, Wen-Chung; Chen, Chien-Jen

2016-05-01

Public health mapping and Geographical Information Systems (GIS) are already being used to locate the geographical spread of diseases. This study describes the construction of an easy-to-use online atlas of cancer mortality (1972-2011) and incidence (1995-2008) in Taiwan.Two sets of color maps were made based on "age-adjusted mortality by rate" and "age-adjusted mortality by rank." AJAX (Asynchronous JavaScript and XML), JSON (JavaScript Object Notation), and SVG (Scaling Vector Graphic) were used to create the online atlas. Spatio-temporal patterns of cancer mortality and incidence in Taiwan over the period from 1972 to 2011 and from 1995 to 2008.The constructed online atlas contains information on cancer mortality and incidence (http://taiwancancermap.csmu-liawyp.tw/). The common GIS functions include zoom and pan and identity tools. Users can easily customize the maps to explore the spatio-temporal trends of cancer mortality and incidence using different devices (such as personal computers, mobile phone, or pad). This study suggests an easy- to-use, low-cost, and independent platform for exploring cancer incidence and mortality. It is expected to serve as a reference tool for cancer prevention and risk assessment.This online atlas is a cheap and fast tool that integrates various cancer maps. Therefore, it can serve as a powerful tool that allows users to examine and compare spatio-temporal patterns of various maps. Furthermore, it is an-easy-to use tool for updating data and assessing risk factors of cancer in Taiwan.

Spatio-temporal patterns of the effects of precipitation variability and land use/cover changes on long-term changes in sediment yield in the Loess Plateau, China

Directory of Open Access Journals (Sweden)

G. Gao

2017-09-01

Full Text Available Within China's Loess Plateau there have been concerted revegetation efforts and engineering measures since the 1950s aimed at reducing soil erosion and land degradation. As a result, annual streamflow, sediment yield, and sediment concentration have all decreased considerably. Human-induced land use/cover change (LUCC was the dominant factor, contributing over 70 % of the sediment load reduction, whereas the contribution of precipitation was less than 30 %. In this study, we use 50-year time series data (1961–2011, showing decreasing trends in the annual sediment loads of 15 catchments, to generate spatio-temporal patterns in the effects of LUCC and precipitation variability on sediment yield. The space–time variability of sediment yield was expressed notionally as a product of two factors representing (i the effect of precipitation and (ii the fraction of treated land surface area. Under minimal LUCC, the square root of annual sediment yield varied linearly with precipitation, with the precipitation–sediment load relationship showing coherent spatial patterns amongst the catchments. As the LUCC increased and took effect, the changes in sediment yield pattern depended more on engineering measures and vegetation restoration campaign, and the within-year rainfall patterns (especially storm events also played an important role. The effect of LUCC is expressed in terms of a sediment coefficient, i.e., the ratio of annual sediment yield to annual precipitation. Sediment coefficients showed a steady decrease over the study period, following a linear decreasing function of the fraction of treated land surface area. In this way, the study has brought out the separate roles of precipitation variability and LUCC in controlling spatio-temporal patterns of sediment yield at catchment scale.

On the expected value and variance for an estimator of the spatio-temporal product density function

DEFF Research Database (Denmark)

Rodríguez-Corté, Francisco J.; Ghorbani, Mohammad; Mateu, Jorge

Second-order characteristics are used to analyse the spatio-temporal structure of the underlying point process, and thus these methods provide a natural starting point for the analysis of spatio-temporal point process data. We restrict our attention to the spatio-temporal product density function......, and develop a non-parametric edge-corrected kernel estimate of the product density under the second-order intensity-reweighted stationary hypothesis. The expectation and variance of the estimator are obtained, and closed form expressions derived under the Poisson case. A detailed simulation study is presented...... to compare our close expression for the variance with estimated ones for Poisson cases. The simulation experiments show that the theoretical form for the variance gives acceptable values, which can be used in practice. Finally, we apply the resulting estimator to data on the spatio-temporal distribution...

Spatiotemporal coding of inputs for a system of globally coupled phase oscillators

Science.gov (United States)

Wordsworth, John; Ashwin, Peter

2008-12-01

We investigate the spatiotemporal coding of low amplitude inputs to a simple system of globally coupled phase oscillators with coupling function g(ϕ)=-sin(ϕ+α)+rsin(2ϕ+β) that has robust heteroclinic cycles (slow switching between cluster states). The inputs correspond to detuning of the oscillators. It was recently noted that globally coupled phase oscillators can encode their frequencies in the form of spatiotemporal codes of a sequence of cluster states [P. Ashwin, G. Orosz, J. Wordsworth, and S. Townley, SIAM J. Appl. Dyn. Syst. 6, 728 (2007)]. Concentrating on the case of N=5 oscillators we show in detail how the spatiotemporal coding can be used to resolve all of the information that relates the individual inputs to each other, providing that a long enough time series is considered. We investigate robustness to the addition of noise and find a remarkable stability, especially of the temporal coding, to the addition of noise even for noise of a comparable magnitude to the inputs.

Transient spatiotemporal chaos in the Morris-Lecar neuronal ring network

Energy Technology Data Exchange (ETDEWEB)

Keplinger, Keegan, E-mail: keegankeplinger@gmail.com; Wackerbauer, Renate, E-mail: rawackerbauer@alaska.edu [Department of Physics, University of Alaska, Fairbanks, Alaska 99775-5920 (United States)

2014-03-15

Transient behavior is thought to play an integral role in brain functionality. Numerical simulations of the firing activity of diffusively coupled, excitable Morris-Lecar neurons reveal transient spatiotemporal chaos in the parameter regime below the saddle-node on invariant circle bifurcation point. The neighborhood of the chaotic saddle is reached through perturbations of the rest state, in which few initially active neurons at an effective spatial distance can initiate spatiotemporal chaos. The system escapes from the neighborhood of the chaotic saddle to either the rest state or to a state of pulse propagation. The lifetime of the chaotic transients is manipulated in a statistical sense through a singular application of a synchronous perturbation to a group of neurons.

Transient spatiotemporal chaos in the Morris-Lecar neuronal ring network.

Science.gov (United States)

Keplinger, Keegan; Wackerbauer, Renate

2014-03-01

Transient behavior is thought to play an integral role in brain functionality. Numerical simulations of the firing activity of diffusively coupled, excitable Morris-Lecar neurons reveal transient spatiotemporal chaos in the parameter regime below the saddle-node on invariant circle bifurcation point. The neighborhood of the chaotic saddle is reached through perturbations of the rest state, in which few initially active neurons at an effective spatial distance can initiate spatiotemporal chaos. The system escapes from the neighborhood of the chaotic saddle to either the rest state or to a state of pulse propagation. The lifetime of the chaotic transients is manipulated in a statistical sense through a singular application of a synchronous perturbation to a group of neurons.

Towards integrated modelling of soil organic carbon cycling at landscape scale

Science.gov (United States)

Viaud, V.

2009-04-01

Soil organic carbon (SOC) is recognized as a key factor of the chemical, biological and physical quality of soil. Numerous models of soil organic matter turnover have been developed since the 1930ies, most of them dedicated to plot scale applications. More recently, they have been applied to national scales to establish the inventories of carbon stocks directed by the Kyoto protocol. However, only few studies consider the intermediate landscape scale, where the spatio-temporal pattern of land management practices, its interactions with the physical environment and its impacts on SOC dynamics can be investigated to provide guidelines for sustainable management of soils in agricultural areas. Modelling SOC cycling at this scale requires accessing accurate spatially explicit input data on soils (SOC content, bulk density, depth, texture) and land use (land cover, farm practices), and combining both data in a relevant integrated landscape representation. The purpose of this paper is to present a first approach to modelling SOC evolution in a small catchment. The impact of the way landscape is represented on SOC stocks in the catchment was more specifically addressed. This study was based on the field map, the soil survey, the crop rotations and land management practices of an actual 10-km² agricultural catchment located in Brittany (France). RothC model was used to drive soil organic matter dynamics. Landscape representation in the form of a systematic regular grid, where driving properties vary continuously in space, was compared to a representation where landscape is subdivided into a set of homogeneous geographical units. This preliminary work enabled to identify future needs to improve integrated soil-landscape modelling in agricultural areas.

New varying speed of light theories

CERN Document Server

Magueijo, J

2003-01-01

We review recent work on the possibility of a varying speed of light (VSL). We start by discussing the physical meaning of a varying $c$, dispelling the myth that the constancy of $c$ is a matter of logical consistency. We then summarize the main VSL mechanisms proposed so far: hard breaking of Lorentz invariance; bimetric theories (where the speeds of gravity and light are not the same); locally Lorentz invariant VSL theories; theories exhibiting a color dependent speed of light; varying $c$ induced by extra dimensions (e.g. in the brane-world scenario); and field theories where VSL results from vacuum polarization or CPT violation. We show how VSL scenarios may solve the cosmological problems usually tackled by inflation, and also how they may produce a scale-invariant spectrum of Gaussian fluctuations, capable of explaining the WMAP data. We then review the connection between VSL and theories of quantum gravity, showing how ``doubly special'' relativity has emerged as a VSL effective model of quantum space...

Precursor of transition to turbulence: spatiotemporal wave front.

Science.gov (United States)

Bhaumik, S; Sengupta, T K

2014-04-01

To understand transition to turbulence via 3D disturbance growth, we report here results obtained from the solution of Navier-Stokes equation (NSE) to reproduce experimental results obtained by minimizing background disturbances and imposing deterministic excitation inside the shear layer. A similar approach was adopted in Sengupta and Bhaumik [Phys. Rev. Lett. 107, 154501 (2011)], where a route of transition from receptivity to fully developed turbulent stage was explained for 2D flow in terms of the spatio-temporal wave-front (STWF). The STWF was identified as the unit process of 2D turbulence creation for low amplitude wall excitation. Theoretical prediction of STWF for boundary layer was established earlier in Sengupta, Rao, and Venkatasubbaiah [Phys. Rev. Lett. 96, 224504 (2006)] from the Orr-Sommerfeld equation as due to spatiotemporal instability. Here, the same unit process of the STWF during transition is shown to be present for 3D disturbance field from the solution of governing NSE.

Noise tolerant spatiotemporal chaos computing.

Science.gov (United States)

Kia, Behnam; Kia, Sarvenaz; Lindner, John F; Sinha, Sudeshna; Ditto, William L

2014-12-01

We introduce and design a noise tolerant chaos computing system based on a coupled map lattice (CML) and the noise reduction capabilities inherent in coupled dynamical systems. The resulting spatiotemporal chaos computing system is more robust to noise than a single map chaos computing system. In this CML based approach to computing, under the coupled dynamics, the local noise from different nodes of the lattice diffuses across the lattice, and it attenuates each other's effects, resulting in a system with less noise content and a more robust chaos computing architecture.

A BAYESIAN SPATIAL AND TEMPORAL MODELING APPROACH TO MAPPING GEOGRAPHIC VARIATION IN MORTALITY RATES FOR SUBNATIONAL AREAS WITH R-INLA.

Science.gov (United States)

Khana, Diba; Rossen, Lauren M; Hedegaard, Holly; Warner, Margaret

2018-01-01

Hierarchical Bayes models have been used in disease mapping to examine small scale geographic variation. State level geographic variation for less common causes of mortality outcomes have been reported however county level variation is rarely examined. Due to concerns about statistical reliability and confidentiality, county-level mortality rates based on fewer than 20 deaths are suppressed based on Division of Vital Statistics, National Center for Health Statistics (NCHS) statistical reliability criteria, precluding an examination of spatio-temporal variation in less common causes of mortality outcomes such as suicide rates (SRs) at the county level using direct estimates. Existing Bayesian spatio-temporal modeling strategies can be applied via Integrated Nested Laplace Approximation (INLA) in R to a large number of rare causes of mortality outcomes to enable examination of spatio-temporal variations on smaller geographic scales such as counties. This method allows examination of spatiotemporal variation across the entire U.S., even where the data are sparse. We used mortality data from 2005-2015 to explore spatiotemporal variation in SRs, as one particular application of the Bayesian spatio-temporal modeling strategy in R-INLA to predict year and county-specific SRs. Specifically, hierarchical Bayesian spatio-temporal models were implemented with spatially structured and unstructured random effects, correlated time effects, time varying confounders and space-time interaction terms in the software R-INLA, borrowing strength across both counties and years to produce smoothed county level SRs. Model-based estimates of SRs were mapped to explore geographic variation.

Multi-scale properties of large eddy simulations: correlations between resolved-scale velocity-field increments and subgrid-scale quantities

Science.gov (United States)

Linkmann, Moritz; Buzzicotti, Michele; Biferale, Luca

2018-06-01

We provide analytical and numerical results concerning multi-scale correlations between the resolved velocity field and the subgrid-scale (SGS) stress-tensor in large eddy simulations (LES). Following previous studies for Navier-Stokes equations, we derive the exact hierarchy of LES equations governing the spatio-temporal evolution of velocity structure functions of any order. The aim is to assess the influence of the subgrid model on the inertial range intermittency. We provide a series of predictions, within the multifractal theory, for the scaling of correlation involving the SGS stress and we compare them against numerical results from high-resolution Smagorinsky LES and from a-priori filtered data generated from direct numerical simulations (DNS). We find that LES data generally agree very well with filtered DNS results and with the multifractal prediction for all leading terms in the balance equations. Discrepancies are measured for some of the sub-leading terms involving cross-correlation between resolved velocity increments and the SGS tensor or the SGS energy transfer, suggesting that there must be room to improve the SGS modelisation to further extend the inertial range properties for any fixed LES resolution.

Brane world cosmologies with varying speed of light

International Nuclear Information System (INIS)

Youm, Donam

2001-02-01

We study cosmologies in the Randall-Sundrum models, incorporating the possibility of time-varying speed of light and Newton's constant. The cosmologies with varying speed of light (VSL) were proposed by Moffat and by Albrecht and Magueijo as an alternative to inflation for solving the cosmological problems. We consider the case in which the speed of light varies with time after the radion or the scale of the extra dimension has been stabilized. We elaborate on the conditions under which the flatness problem and the cosmological constant problem can be resolved. Particularly, the VSL cosmologies may provide a possible mechanism for bringing the quantum corrections to the fine-tuned brane tensions after the SUSY breaking under control. (author)

The application of a hierarchical Bayesian spatiotemporal model for ...

Indian Academy of Sciences (India)

Process (GP) model by using the Gibbs sampling method. The result for ... good indicator of the HBST method. The statistical ... summary and discussion of future works are given .... spatiotemporal package in R language (R core team. 2013).

A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

International Nuclear Information System (INIS)

Kim, Minsun; Stewart, Robert D.; Phillips, Mark H.

2015-01-01

Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T d ), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D mean ≤ 45 Gy), lungs (D mean ≤ 20 Gy), cord (D max ≤ 45 Gy), esophagus (D max ≤ 63 Gy), and unspecified tissues (D 05 ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D 95 of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T d (3–100 days), tumor lag-time (T k = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D 95 were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T d and T k used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating tumors with T d less than 10 days, there was no

[Epidemiologic and spatio-temporal characteristics of hepatitis E in China, 2004-2014].

Science.gov (United States)

Liu, Z Q; Zuo, J L; Yan, Q; Fang, Q W; Zhang, T J

2017-10-10

Objective: To describe and analyze the epidemiologic and spatio-temporal characteristics of hepatitis E in China from 2004 to 2014. Methods: Data on the incidence of hepatitis E in 31 provinces (municipality and autonomous region) from 2004 to 2014, were collected. Empirical Mode Decomposition (EMD) was applied to decompose the time-series data to accurately describe the trend of hepatitis E incidence. Mathematic model was used to estimate the annual change of incidence in each age group and the whole province. Software ArcGIS 10.1 and SaTScan 9.01 were used to analyze the spatio-temporal clusters. Results: During 2004-2014, a total of 245 414 hepatitis E cases were reported in China. The overall incidence showed a slight increase ( OR =1.05, 95 %CI : 1.03-1.10). Incidence rates on hepatitis E were discovered different across the provinces, with significant increase appearing in the southern, central and northwestern areas. The highest increase was seen in the elderly, especially in the 65-69 and 70-74 year-olds. Results from the Local spatial autocorrelation analysis showed that the "high-high cluster" was moving from the north to the south and the "low-low cluster" disappeared as time went by. Data from Spatio-temporal scanning showed that there were five spatio-temporal clustering areas across the country. Conclusion: The overall incidence of hepatitis E was on the rise from 2004 to 2014, in China, but with differences seen across the areas and age groups.

SOLAP technology: Merging business intelligence with geospatial technology for interactive spatio-temporal exploration and analysis of data

Science.gov (United States)

Rivest, Sonia; Bédard, Yvan; Proulx, Marie-Josée; Nadeau, Martin; Hubert, Frederic; Pastor, Julien

To support their analytical processes, today's organizations deploy data warehouses and client tools such as OLAP (On-Line Analytical Processing) to access, visualize, and analyze their integrated, aggregated and summarized data. Since a large part of these data have a spatial component, better client tools are required to take full advantage of the geometry of the spatial phenomena or objects being analyzed. With this regard, Spatial OLAP (SOLAP) technology offers promising possibilities. A SOLAP tool can be defined as "a type of software that allows rapid and easy navigation within spatial databases and that offers many levels of information granularity, many themes, many epochs and many display modes synchronized or not: maps, tables and diagrams" [Bédard, Y., Proulx, M.J., Rivest, S., 2005. Enrichissement du OLAP pour l'analyse géographique: exemples de réalisation et différentes possibilités technologiques. In: Bentayeb, F., Boussaid, O., Darmont, J., Rabaseda, S. (Eds.), Entrepôts de Données et Analyse en ligne, RNTI B_1. Paris: Cépaduès, pp. 1-20]. SOLAP tools offer a new user interface and are meant to be client applications sitting on top of multi-scale spatial data warehouses or datacubes. As they are based on the multidimensional paradigm, they facilitate the interactive spatio-temporal exploration of data. The purpose of this paper is to discuss how SOLAP concepts support spatio-temporal exploration of data and then to present the geovisualization, interactivity, and animation features of the SOLAP software developed by our research group. This paper first reviews the general concepts behind OLAP and SOLAP systems. This is followed by a discussion of how these SOLAP concepts support spatio-temporal exploration of data. In the subsequent section, SOLAP software is introduced along with features that enable geovisualization, interactivity and animation.

Improved kinect-based spatiotemporal and kinematic treadmill gait assessment.

Science.gov (United States)

Eltoukhy, Moataz; Oh, Jeonghoon; Kuenze, Christopher; Signorile, Joseph

2017-01-01

A cost-effective, clinician friendly gait assessment tool that can automatically track patients' anatomical landmarks can provide practitioners with important information that is useful in prescribing rehabilitative and preventive therapies. This study investigated the validity and reliability of the Microsoft Kinect v2 as a potential inexpensive gait analysis tool. Ten healthy subjects walked on a treadmill at 1.3 and 1.6m·s -1 , as spatiotemporal parameters and kinematics were extracted concurrently using the Kinect and three-dimensional motion analysis. Spatiotemporal measures included step length and width, step and stride times, vertical and mediolateral pelvis motion, and foot swing velocity. Kinematic outcomes included hip, knee, and ankle joint angles in the sagittal plane. The absolute agreement and relative consistency between the two systems were assessed using interclass correlations coefficients (ICC2,1), while reproducibility between systems was established using Lin's Concordance Correlation Coefficient (rc). Comparison of ensemble curves and associated 90% confidence intervals (CI90) of the hip, knee, and ankle joint angles were performed to investigate if the Kinect sensor could consistently and accurately assess lower extremity joint motion throughout the gait cycle. Results showed that the Kinect v2 sensor has the potential to be an effective clinical assessment tool for sagittal plane knee and hip joint kinematics, as well as some spatiotemporal temporal variables including pelvis displacement and step characteristics during the gait cycle. Copyright © 2016 Elsevier B.V. All rights reserved.

Anthropogenic Effects on Forest Ecosystems at Various Spatio-Temporal Scales

Directory of Open Access Journals (Sweden)

Michael Bredemeier

2002-01-01

Full Text Available The focus in this review of long-term effects on forest ecosystems is on human impact. As a classification of this differentiated and complex matter, three domains of long-term effects with different scales in space and time are distinguished: 1- Exploitation and conversion history of forests in areas of extended human settlement 2- Long-range air pollution and acid deposition in industrialized regions 3- Current global loss of forests and soil degradation.

Enhancement of peak intensity in a filament core with spatiotemporally focused femtosecond laser pulses

Energy Technology Data Exchange (ETDEWEB)

Zeng Bin; Chu Wei; Li Guihua; Zhang Haisu; Ni Jielei [State Key Laboratory of High Field Laser Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800 (China); Graduate School of Chinese Academy of Sciences, Beijing 100080 (China); Gao Hui; Liu Weiwei [Institute of Modern Optics, Nankai University, Tianjin, 300071 (China); Yao Jinping; Cheng Ya; Xu Zhizhan [State Key Laboratory of High Field Laser Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800 (China); Chin, See Leang [Center for Optics, Photonics and Laser (COPL) and Department of Physics, Engineering Physics and Optics, Universite Laval, Quebec City, QC, G1V 0A6 (Canada)

2011-12-15

We demonstrate that the peak intensity in the filament core, which is inherently limited by the intensity clamping effect during femtosecond laser filamentation, can be significantly enhanced using spatiotemporally focused femtosecond laser pulses. In addition, the filament length obtained by spatiotemporally focused femtosecond laser pulses is {approx}25 times shorter than that obtained by a conventional focusing scheme, resulting in improved high spatial resolution.

A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters.

Science.gov (United States)

Adin, A; Lee, D; Goicoa, T; Ugarte, María Dolores

2018-01-01

Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain.

Nonparametric evaluation of dynamic disease risk: a spatio-temporal kernel approach.

Directory of Open Access Journals (Sweden)

Zhijie Zhang

Full Text Available Quantifying the distributions of disease risk in space and time jointly is a key element for understanding spatio-temporal phenomena while also having the potential to enhance our understanding of epidemiologic trajectories. However, most studies to date have neglected time dimension and focus instead on the "average" spatial pattern of disease risk, thereby masking time trajectories of disease risk. In this study we propose a new idea titled "spatio-temporal kernel density estimation (stKDE" that employs hybrid kernel (i.e., weight functions to evaluate the spatio-temporal disease risks. This approach not only can make full use of sample data but also "borrows" information in a particular manner from neighboring points both in space and time via appropriate choice of kernel functions. Monte Carlo simulations show that the proposed method performs substantially better than the traditional (i.e., frequency-based kernel density estimation (trKDE which has been used in applied settings while two illustrative examples demonstrate that the proposed approach can yield superior results compared to the popular trKDE approach. In addition, there exist various possibilities for improving and extending this method.

Preschoolers' use of spatiotemporal history, appearance, and proper name in determining individual identity.

Science.gov (United States)

Gutheil, Grant; Gelman, Susan A; Klein, Eileen; Michos, Katherine; Kelaita, Kara

2008-04-01

Humans construe their environment as composed largely of discrete individuals, which are also members of kinds (e.g., trees, cars, and people). On what basis do young children determine individual identity? How important are featural properties (e.g., physical appearance, name) relative to spatiotemporal history? Two studies examined the relative importance of these factors in preschoolers' and adults' identity judgments. Participants were shown pairs of individuals who looked identical but differed in their spatiotemporal history (e.g., two physically distinct but identical Winnie-the-Pooh dolls), and were asked whether both members in the pair would have access to knowledge that had been supplied to only one of the pairs. The results provide clear support for spatiotemporal history as the primary basis of identity judgments in both preschoolers and adults, and further place issues of identity within the broader cognitive framework of psychological essentialism.

Optimization of spatiotemporally fractionated radiotherapy treatments with bounds on the achievable benefit

Science.gov (United States)

Gaddy, Melissa R.; Yıldız, Sercan; Unkelbach, Jan; Papp, Dávid

2018-01-01

Spatiotemporal fractionation schemes, that is, treatments delivering different dose distributions in different fractions, can potentially lower treatment side effects without compromising tumor control. This can be achieved by hypofractionating parts of the tumor while delivering approximately uniformly fractionated doses to the surrounding tissue. Plan optimization for such treatments is based on biologically effective dose (BED); however, this leads to computationally challenging nonconvex optimization problems. Optimization methods that are in current use yield only locally optimal solutions, and it has hitherto been unclear whether these plans are close to the global optimum. We present an optimization framework to compute rigorous bounds on the maximum achievable normal tissue BED reduction for spatiotemporal plans. The approach is demonstrated on liver tumors, where the primary goal is to reduce mean liver BED without compromising any other treatment objective. The BED-based treatment plan optimization problems are formulated as quadratically constrained quadratic programming (QCQP) problems. First, a conventional, uniformly fractionated reference plan is computed using convex optimization. Then, a second, nonconvex, QCQP model is solved to local optimality to compute a spatiotemporally fractionated plan that minimizes mean liver BED, subject to the constraints that the plan is no worse than the reference plan with respect to all other planning goals. Finally, we derive a convex relaxation of the second model in the form of a semidefinite programming problem, which provides a rigorous lower bound on the lowest achievable mean liver BED. The method is presented on five cases with distinct geometries. The computed spatiotemporal plans achieve 12-35% mean liver BED reduction over the optimal uniformly fractionated plans. This reduction corresponds to 79-97% of the gap between the mean liver BED of the uniform reference plans and our lower bounds on the lowest

Spatio-temporal joins on symbolic indoor tracking data

DEFF Research Database (Denmark)

Lu, Hua; Yang, Bin; Jensen, Christian S.

2011-01-01

and studies probabilistic, spatio-temporal joins on historical indoor tracking data. Two meaningful types of join are defined. They return object pairs that satisfy spatial join predicates either at a time point or during a time interval. The predicates considered include “same X,” where X is a semantic...

Spatiotemporal Variation of China’s State-Owned Construction Land Supply from 2003 to 2014

Directory of Open Access Journals (Sweden)

Min Jiang

2016-11-01

Full Text Available State-owned construction land is the dominant legal land source for construction in China and its supply influences urban expansion, house prices, and economic development, among other factors. Surprisingly, limited attention has been directly devoted to the spatiotemporal variation in land supply or the driving factors. This paper applied a centroid model and hotspot analysis, and created a newly increased construction land dependence-degree index (NCD to present the spatiotemporal variations of China’s construction land supply magnitude and pattern from 2003 to 2014, using land supply data from 339 cities. A two-way fixed effect model was introduced to reveal the influence of the socio-economic driving factors. The results showed that China’s state-owned construction land supply area (CLSA and newly increased construction land supply area (NCSA both increased during the period from 2003 to 2014, the geographic centroid of CLSA and NCSA moved northwest. NCD showed an overall increasing trend, and hotspots with high NCD migrated from the east region to the west region and shifted from an “east hot and west cold” pattern in 2003 to an “east cold and west hot” pattern in 2014. The gross domestic product (GDP has a U-shape effect on CLSA and NCD. The population, average annual wage of workers, and investment in fixed assets (fiv have positive effects on CLSA, and fiv also has a positive effect on NCD. The increasing ratio of tertiary industry added value to secondary industry added value reduces CLSA and NCD, and the effects of state policies vary from year to year. Different land supply policies should be implemented for cities in different development stages.

Trends in spatio-temporal dynamics of visceral leishmaniasis cases in a highly-endemic focus of Bihar, India: an investigation based on GIS tools.

Science.gov (United States)

Mandal, Rakesh; Kesari, Shreekant; Kumar, Vijay; Das, Pradeep

2018-04-02

Visceral leishmaniasis (VL) in Bihar State (India) continues to be endemic, despite the existence of effective treatment and a vector control program to control disease morbidity. A clear understanding of spatio-temporal distribution of VL may improve surveillance and control implementation. This study explored the trends in spatio-temporal dynamics of VL endemicity at a meso-scale level in Vaishali District, based on geographical information systems (GIS) tools and spatial statistical analysis. A GIS database was used to integrate the VL case data from the study area between 2009 and 2014. All cases were spatially linked at a meso-scale level. Geospatial techniques, such as GIS-layer overlaying and mapping, were employed to visualize and detect the spatio-temporal patterns of a VL endemic outbreak across the district. The spatial statistic Moran's I Index (Moran's I) was used to simultaneously evaluate spatial-correlation between endemic villages and the spatial distribution patterns based on both the village location and the case incidence rate (CIR). Descriptive statistics such as mean, standard error, confidence intervals and percentages were used to summarize the VL case data. There were 624 endemic villages with 2719 (average 906 cases/year) VL cases during 2012-2014. The Moran's I revealed a cluster pattern (P < 0.05) of CIR distribution at the meso-scale level. On average, 68 villages were newly-endemic each year. Of which 93.1% of villages' endemicity were found to have occurred on the peripheries of the previous year endemic villages. The mean CIR of the endemic villages that were peripheral to the following year newly-endemic villages, compared to all endemic villages of the same year, was higher (P < 0.05). The results show that the VL endemicity of new villages tends to occur on the periphery of villages endemic in the previous year. High-CIR plays a major role in the spatial dispersion of the VL cases between non-endemic and endemic villages

Spatiotemporal Features for Asynchronous Event-based Data

Directory of Open Access Journals (Sweden)

Xavier eLagorce

2015-02-01

Full Text Available Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the realiable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

Spatiotemporal Interpolation Methods for Solar Event Trajectories

Science.gov (United States)

Filali Boubrahimi, Soukaina; Aydin, Berkay; Schuh, Michael A.; Kempton, Dustin; Angryk, Rafal A.; Ma, Ruizhe

2018-05-01

This paper introduces four spatiotemporal interpolation methods that enrich complex, evolving region trajectories that are reported from a variety of ground-based and space-based solar observatories every day. Our interpolation module takes an existing solar event trajectory as its input and generates an enriched trajectory with any number of additional time–geometry pairs created by the most appropriate method. To this end, we designed four different interpolation techniques: MBR-Interpolation (Minimum Bounding Rectangle Interpolation), CP-Interpolation (Complex Polygon Interpolation), FI-Interpolation (Filament Polygon Interpolation), and Areal-Interpolation, which are presented here in detail. These techniques leverage k-means clustering, centroid shape signature representation, dynamic time warping, linear interpolation, and shape buffering to generate the additional polygons of an enriched trajectory. Using ground-truth objects, interpolation effectiveness is evaluated through a variety of measures based on several important characteristics that include spatial distance, area overlap, and shape (boundary) similarity. To our knowledge, this is the first research effort of this kind that attempts to address the broad problem of spatiotemporal interpolation of solar event trajectories. We conclude with a brief outline of future research directions and opportunities for related work in this area.

Spatio-temporal analysis of Salmonella surveillance data in Thailand

DEFF Research Database (Denmark)

Coutinho Calado Domingues, Ana Rita; Vieira, Antonio; Hendriksen, Rene S.

2014-01-01

This study evaluates the usefulness of spatio-temporal statistical tools to detect outbreaks using routine surveillance data where limited epidemiological information is available. A dataset from 2002 to 2007 containing information regarding date, origin, source and serotype of 29 586 Salmonella ...

Annual spatiotemporal migration schedules in three larger insectivorous birds

DEFF Research Database (Denmark)

Jacobsen, Lars Bo; Jensen, Niels Odder; Willemoes, Mikkel

2017-01-01

Background: Knowledge of spatiotemporal migration patterns is important for our understanding of migration ecology and ultimately conservation of migratory species. We studied the annual migration schedules of European nightjar, a large nocturnal insectivore and compared it with two other larger ...

Regional and landscape-scale variability of Landsat-observed vegetation dynamics in northwest Siberian tundra

International Nuclear Information System (INIS)

Frost, Gerald V; Epstein, Howard E; Walker, Donald A

2014-01-01

Widespread increases in Arctic tundra productivity have been documented for decades using coarse-scale satellite observations, but finer-scale observations indicate that changes have been very uneven, with a high degree of landscape- and regional-scale heterogeneity. Here we analyze time-series of the Normalized Difference Vegetation Index (NDVI) observed by Landsat (1984–2012), to assess landscape- and regional-scale variability of tundra vegetation dynamics in the northwest Siberian Low Arctic, a little-studied region with varied soils, landscape histories, and permafrost attributes. We also estimate spatio-temporal rates of land-cover change associated with expansion of tall alder (Alnus) shrublands, by integrating Landsat time-series with very-high-resolution imagery dating to the mid-1960s. We compiled Landsat time-series for eleven widely-distributed landscapes, and performed linear regression of NDVI values on a per-pixel basis. We found positive net NDVI trends (‘greening’) in nine of eleven landscapes. Net greening occurred in alder shrublands in all landscapes, and strong greening tended to correspond to shrublands that developed since the 1960s. Much of the spatial variability of greening within landscapes was linked to landscape physiography and permafrost attributes, while between-landscape variability largely corresponded to differences in surficial geology. We conclude that continued increases in tundra productivity in the region are likely in upland tundra landscapes with fine-textured, cryoturbated soils; these areas currently tend to support discontinuous vegetation cover, but are highly susceptible to rapid increases in vegetation cover, as well as land-cover changes associated with the development of tall shrublands. (paper)

Monthly streamflow forecasting at varying spatial scales in the Rhine basin

Science.gov (United States)

Schick, Simon; Rössler, Ole; Weingartner, Rolf

2018-02-01

Model output statistics (MOS) methods can be used to empirically relate an environmental variable of interest to predictions from earth system models (ESMs). This variable often belongs to a spatial scale not resolved by the ESM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the ESM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In doing so, the MOS method is tested for catchments areas covering 4 orders of magnitude. Using data from the period 1981-2011, the results show that skill, with respect to climatology, is restricted on average to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduce the mean absolute error of the former in the range of 5 to 12 %, which is consistently reproduced at the subcatchment scale. An additional experiment conducted for 5-day mean streamflow indicates that the dynamical predictions help to reduce uncertainties up to about 20 days ahead, but it also reveals some shortcomings of the present MOS method.

On spatio-temporal Lévy based Cox processes

DEFF Research Database (Denmark)

Prokesova, Michaela; Hellmund, Gunnar; Jensen, Eva Bjørn Vedel

2006-01-01

The paper discusses a new class of models for spatio-temporal Cox point processes. In these models, the driving field is defined by means of an integral of a weight function with respect to a Lévy basis. The relations to other Cox process models studied previously are discussed and formulas for t...

Nonlinear system identification NARMAX methods in the time, frequency, and spatio-temporal domains

CERN Document Server

Billings, Stephen A

2013-01-01

Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) modelThe orthogonal least squares algorithm that allows models to be built term by

Do spatiotemporal parameters and gait variability differ across the lifespan of healthy adults? A systematic review.

Science.gov (United States)

Herssens, Nolan; Verbecque, Evi; Hallemans, Ann; Vereeck, Luc; Van Rompaey, Vincent; Saeys, Wim

2018-06-12

Aging is often associated with changes in the musculoskeletal system, peripheral and central nervous system. These age-related changes often result in mobility problems influencing gait performance. Compensatory strategies are used as a way to adapt to these physiological changes. The aim of this review is to investigate the differences in spatiotemporal and gait variability measures throughout the healthy adult life. This systematic review was conducted according to the PRISMA guidelines and registered in the PROSPERO database (no. CRD42017057720). Databases MEDLINE (Pubmed), Web of Science (Web of Knowledge), Cochrane Library and ScienceDirect were systematically searched until March 2018. Eighteen of the 3195 original studies met the eligibility criteria and were included in this review. The majority of studies reported spatiotemporal and gait variability measures in adults above the age of 65, followed by the young adult population, information of middle-aged adults is lacking. Spatiotemporal parameters and gait variability measures were extracted from 2112 healthy adults between 18 and 98 years old and, in general, tend to deteriorate with increasing age. Variability measures were only reported in an elderly population and show great variety between studies. The findings of this review suggest that most spatiotemporal parameters significantly differ across different age groups. Elderly populations show a reduction of preferred walking speed, cadence, step and stride length, all related to a more cautious gait, while gait variability measures remain stable over time. A preliminary framework of normative reference data is provided, enabling insights into the influence of aging on spatiotemporal parameters, however spatiotemporal parameters of middle-aged adults should be investigated more thoroughly. Copyright © 2018 Elsevier B.V. All rights reserved.

Evaluation of the Spatiotemporal Dynamics of Oxytetracycline and Its Control Effect Against Citrus Huanglongbing via Trunk Injection.

Science.gov (United States)

Hu, Jiahuai; Wang, Nian

2016-12-01

Citrus huanglongbing (HLB) or greening is a devastating bacterial disease that has destroyed millions of trees and is associated with phloem-residing 'Candidatus Liberibacter asiaticus' (Las) in Florida. In this study, we evaluated the spatiotemporal dynamics of oxytetracycline in planta and its control effect against HLB via trunk injection. Las-infected 'Hamlin' sweet orange trees on 'Swingle' citrumelo rootstock at the early stage of decline were treated with oxytetracycline hydrochloride (OTC) using trunk injection with varying number of injection ports. Spatiotemporal distribution of OTC and dynamics of Las populations were monitored by high-performance liquid chromatography method and qPCR assay, respectively. Uniform distribution of OTC throughout tree canopies and root system was achieved 2 days postinjection. High levels of OTC (>850 µg/kg) were maintained in leaf and root for at least 1 month and moderate OTC (>500 µg/kg) persisted for more than 9 months. Reduction of Las populations in root system and leaves of OTC-treated trees were over 95% and 99% (i.e., 1.76 and 2.19 log reduction) between 2 and 28 days postinjection. Conditions of trees receiving OTC treatment were improved, fruit yield was increased, and juice acidity was lowered than water-injected control even though their differences were not statistically significant during the test period. Our study demonstrated that trunk injection of OTC could be used as an effective measure for integrated management of citrus HLB.

Monitoring Local Changes in Granite Rock Under Biaxial Test: A Spatiotemporal Imaging Application With Diffuse Waves

Science.gov (United States)

Xie, Fan; Ren, Yaqiong; Zhou, Yongsheng; Larose, Eric; Baillet, Laurent

2018-03-01

Diffuse acoustic or seismic waves are highly sensitive to detect changes of mechanical properties in heterogeneous geological materials. In particular, thanks to acoustoelasticity, we can quantify stress changes by tracking acoustic or seismic relative velocity changes in the material at test. In this paper, we report on a small-scale laboratory application of an innovative time-lapse tomography technique named Locadiff to image spatiotemporal mechanical changes on a granite sample under biaxial loading, using diffuse waves at ultrasonic frequencies (300 kHz to 900 kHz). We demonstrate the ability of the method to image reversible stress evolution and deformation process, together with the development of reversible and irreversible localized microdamage in the specimen at an early stage. Using full-field infrared thermography, we visualize stress-induced temperature changes and validate stress images obtained from diffuse ultrasound. We demonstrate that the inversion with a good resolution can be achieved with only a limited number of receivers distributed around a single source, all located at the free surface of the specimen. This small-scale experiment is a proof of concept for frictional earthquake-like failure (e.g., stick-slip) research at laboratory scale as well as large-scale seismic applications, potentially including active fault monitoring.

Spatiotemporal characteristics of heat waves over China in regional climate simulations within the CORDEX-EA project

Science.gov (United States)

Wang, Pinya; Tang, Jianping; Sun, Xuguang; Liu, Jianyong; Juan, Fang

2018-03-01

Using the Weather Research and Forecasting (WRF) model, this paper analyzes the spatiotemporal features of heat waves in 20-year regional climate simulations over East Asia, and investigates the capability of WRF to reproduce observational heat waves in China. Within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX), the WRF model is driven by the ERA-Interim (ERAIN) reanalysis, and five continuous simulations are conducted from 1989 to 2008. Of these, four runs apply the interior spectral nudging (SN) technique with different wavenumbers, nudging variables and nudging coefficients. Model validations show that WRF can reasonably reproduce the spatiotemporal features of heat waves in China. Compared with the experiment without SN, the application of SN is effectie on improving the skill of the model in simulating both the spatial distributions and temporal variations of heat waves of different intensities. The WRF model shows advantages in reproducing the synoptic circulations with SN and therefore yields better representations for heat wave events. Besides, the SN method is able to preserve the variability of large-scale circulations quite well, which in turn adjusts the extreme temperature variability towards the observation. Among the four SN experiments, those with stronger nudging coefficients perform better in modulating both the spatial and temporal features of heat waves. In contrast, smaller nudging coefficients weaken the effects of SN on improving WRF's performances.

Spatiotemporal variability of marine renewable energy resources in Norway

NARCIS (Netherlands)

Varlas, George; Christakos, Konstantinos; Cheliotis, Ioannis; Papadopoulos, A.; Steeneveld, G.J.

2017-01-01

Marine Renewable Energy (MRE) resources such as wind and wave energy depend on the complex behaviour of weather and climatic conditions which determine the development of MRE technologies, energy grid, supply and prices. This study investigates the spatiotemporal variability of MRE resources along

Synoptic-scale circulation patterns during summer derived from tree rings in mid-latitude Asia

Science.gov (United States)

Seim, Andrea; Schultz, Johannes A.; Leland, Caroline; Davi, Nicole; Byambasuren, Oyunsanaa; Liang, Eryuan; Wang, Xiaochun; Beck, Christoph; Linderholm, Hans W.; Pederson, Neil

2017-09-01

Understanding past and recent climate and atmospheric circulation variability is vital for regions that are affected by climate extremes. In mid-latitude Asia, however, the synoptic climatology is complex and not yet fully understood. The aim of this study was to investigate dominant synoptic-scale circulation patterns during the summer season using a multi-species tree-ring width (TRW) network comprising 78 sites from mid-latitude Asia. For each TRW chronology, we calculated an atmospheric circulation tree-ring index (ACTI), based on 1000 hPa geopotential height data, to directly link tree growth to 13 summertime weather types and their associated local climate conditions for the period 1871-1993. Using the ACTI, three groups of similarly responding tree-ring sites can be associated with distinct large-scale atmospheric circulation patterns: 1. growth of drought sensitive trees is positively affected by a cyclone over northern Russia; 2. temperature sensitive trees show positive associations to a cyclone over northwestern Russia and an anticyclone over Mongolia; 3. trees at two high elevation sites show positive relations to a zonal cyclone extending from mid-latitude Eurasia to the West Pacific. The identified synoptic-scale circulation patterns showed spatiotemporal variability in their intensity and position, causing temporally varying climate conditions in mid-latitude Asia. Our results highlight that for regions with less pronounced atmospheric action centers during summer such as the occurrence of large-scale cyclones and anticyclones, synoptic-scale circulation patterns can be extracted and linked to the Northern Hemisphere circulation system. Thus, we provide a new and solid envelope for climate studies covering the past to the future.

[Spatiotemporal dynamics of maize water suitability and assessment of agricultural drought in Liaoning Province, China from 1981 to 2010].

Science.gov (United States)

Cai, Fu; Zhang, Shu-jie; Ji, Rui-peng; Mi, Na; Wu, Jin-wen; Zhang, Yu-shu

2015-01-01

Maize water suitability (MWS) model was developed at growth stage scale. Frequency and severity of drought were evaluated by combining MWS estimates and agricultural meteorological drought indexes. The MWS at each growth stage was calculated by using maize observational data and conventional meteorological data at 52 sites in Liaoning during the period from 1981 to 2010. Based on the climatic trend and abrupt change analysis, spatiotemporal dynamics of MWS were investigated. Meanwhile, occurrence of agricultural drought and its severity were also estimated. The results showed that the variation of MWS largely differed at different growth stages. Climatic abrupt change happened in 1994, 1996 and 1999 at the stages of emergence to seven leaves (II), jointing to tasseling (IV) and physiological maturity to maturity (VI). During the past 30 years, MWS showed an obvious increasing trend at the stages of sowing to emergence(I) , seven leaves to jointing(III), IV and tasseling to physiological maturity(V), while it showed a decreasing trend at the stages of II and VI, and that at VI stage was statistically significant. In addition, the climatic trend of MWS showed apparently spatial variability. The frequencies of drought at different severities varied with maize growth stages. Areas of high variability of MWS were located in the Northwest and South of Liaoning at the stages of I , II , III and VI, where were also the regions of high frequency of mid- and severe-drought. At the stages of IV and V, the frequency of drought was low and only light- and mid-drought occurred in few areas. In conclusion, the regional mean MWS could be capable to reasonably assess the agricultural drought in Liaoning at the regional scale.

The Review of Visual Analysis Methods of Multi-modal Spatio-temporal Big Data

Directory of Open Access Journals (Sweden)

ZHU Qing

2017-10-01

Full Text Available The visual analysis of spatio-temporal big data is not only the state-of-art research direction of both big data analysis and data visualization, but also the core module of pan-spatial information system. This paper reviews existing visual analysis methods at three levels:descriptive visual analysis, explanatory visual analysis and exploratory visual analysis, focusing on spatio-temporal big data's characteristics of multi-source, multi-granularity, multi-modal and complex association.The technical difficulties and development tendencies of multi-modal feature selection, innovative human-computer interaction analysis and exploratory visual reasoning in the visual analysis of spatio-temporal big data were discussed. Research shows that the study of descriptive visual analysis for data visualizationis is relatively mature.The explanatory visual analysis has become the focus of the big data analysis, which is mainly based on interactive data mining in a visual environment to diagnose implicit reason of problem. And the exploratory visual analysis method needs a major break-through.

A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

Energy Technology Data Exchange (ETDEWEB)

Kim, Minsun, E-mail: mk688@uw.edu; Stewart, Robert D. [Department of Radiation Oncology, University of Washington, Seattle, Washington 98195-6043 (United States); Phillips, Mark H. [Departments of Radiation Oncology and Neurological Surgery, University of Washington, Seattle, Washington 98195-6043 (United States)

2015-11-15

Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T{sub d}), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D{sub mean} ≤ 45 Gy), lungs (D{sub mean} ≤ 20 Gy), cord (D{sub max} ≤ 45 Gy), esophagus (D{sub max} ≤ 63 Gy), and unspecified tissues (D{sub 05} ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D{sub 95} of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T{sub d} (3–100 days), tumor lag-time (T{sub k} = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D{sub 95} were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T{sub d} and T{sub k} used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating

EXTRACTING SPATIOTEMPORAL OBJECTS FROM RASTER DATA TO REPRESENT PHYSICAL FEATURES AND ANALYZE RELATED PROCESSES

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J. A. Zollweg

2017-10-01

Full Text Available Numerous ground-based, airborne, and orbiting platforms provide remotely-sensed data of remarkable spatial resolution at short time intervals. However, this spatiotemporal data is most valuable if it can be processed into information, thereby creating meaning. We live in a world of objects: cars, buildings, farms, etc. On a stormy day, we don’t see millions of cubes of atmosphere; we see a thunderstorm ‘object’. Temporally, we don’t see the properties of those individual cubes changing, we see the thunderstorm as a whole evolving and moving. There is a need to represent the bulky, raw spatiotemporal data from remote sensors as a small number of relevant spatiotemporal objects, thereby matching the human brain’s perception of the world. This presentation reveals an efficient algorithm and system to extract the objects/features from raster-formatted remotely-sensed data. The system makes use of the Python object-oriented programming language, SciPy/NumPy for matrix manipulation and scientific computation, and export/import to the GeoJSON standard geographic object data format. The example presented will show how thunderstorms can be identified and characterized in a spatiotemporal continuum using a Python program to process raster data from NOAA’s High-Resolution Rapid Refresh v2 (HRRRv2 data stream.

Extracting Spatiotemporal Objects from Raster Data to Represent Physical Features and Analyze Related Processes

Science.gov (United States)

Zollweg, J. A.

2017-10-01

Numerous ground-based, airborne, and orbiting platforms provide remotely-sensed data of remarkable spatial resolution at short time intervals. However, this spatiotemporal data is most valuable if it can be processed into information, thereby creating meaning. We live in a world of objects: cars, buildings, farms, etc. On a stormy day, we don't see millions of cubes of atmosphere; we see a thunderstorm `object'. Temporally, we don't see the properties of those individual cubes changing, we see the thunderstorm as a whole evolving and moving. There is a need to represent the bulky, raw spatiotemporal data from remote sensors as a small number of relevant spatiotemporal objects, thereby matching the human brain's perception of the world. This presentation reveals an efficient algorithm and system to extract the objects/features from raster-formatted remotely-sensed data. The system makes use of the Python object-oriented programming language, SciPy/NumPy for matrix manipulation and scientific computation, and export/import to the GeoJSON standard geographic object data format. The example presented will show how thunderstorms can be identified and characterized in a spatiotemporal continuum using a Python program to process raster data from NOAA's High-Resolution Rapid Refresh v2 (HRRRv2) data stream.

Regional Scale High Resolution δ18O Prediction in Precipitation Using MODIS EVI

Science.gov (United States)

Huang, Cho-Ying; Wang, Chung-Ho; Lin, Shou-De; Lo, Yi-Chen; Huang, Bo-Wen; Hatch, Kent A.; Shiu, Hau-Jie; You, Cheng-Feng; Chang, Yuan-Mou; Shen, Sheng-Feng

2012-01-01

The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ18O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ18O are highly correlated and thus the EVI is a good predictor of precipitated δ18O. We then test the predictability of our EVI-δ18O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ18O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ18O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape. PMID:23029053

A general science-based framework for dynamical spatio-temporal models

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Wikle, C.K.; Hooten, M.B.

2010-01-01

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic

Spatiotemporal modelling and mapping of the bubonic plague epidemic in India

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Christakos George

2006-03-01

Full Text Available Abstract Background This work studies the spatiotemporal evolution of bubonic plague in India during 1896–1906 using stochastic concepts and geographical information science techniques. In the past, most investigations focused on selected cities to conduct different kinds of studies, such as the ecology of rats. No detailed maps existed incorporating the space-time dependence structure and uncertainty sources of the epidemic system and providing a composite space-time picture of the disease propagation characteristics. Results Informative spatiotemporal maps were generated that represented mortality rates and geographical spread of the disease, and epidemic indicator plots were derived that offered meaningful characterizations of the spatiotemporal disease distribution. The bubonic plague in India exhibited strong seasonal and geographical features. During its entire duration, the plague continued to invade new geographical areas, while it followed a re-emergence pattern at many localities; its rate changed significantly during each year and the mortality distribution exhibited space-time heterogeneous patterns; prevalence usually occurred in the autumn and spring, whereas the plague stopped moving towards new locations during the summers. Conclusion Modern stochastic modelling and geographical information science provide powerful means to study the spatiotemporal distribution of the bubonic plague epidemic under conditions of uncertainty and multi-sourced databases; to account for various forms of interdisciplinary knowledge; and to generate informative space-time maps of mortality rates and propagation patterns. To the best of our knowledge, this kind of plague maps and plots become available for the first time, thus providing novel perspectives concerning the distribution and space-time propagation of the deadly epidemic. Furthermore, systematic maps and indicator plots make possible the comparison of the spatial-temporal propagation

Measuring spatially- and directionally-varying light scattering from biological material.

Science.gov (United States)

Harvey, Todd Alan; Bostwick, Kimberly S; Marschner, Steve

2013-05-20

Light interacts with an organism's integument on a variety of spatial scales. For example in an iridescent bird: nano-scale structures produce color; the milli-scale structure of barbs and barbules largely determines the directional pattern of reflected light; and through the macro-scale spatial structure of overlapping, curved feathers, these directional effects create the visual texture. Milli-scale and macro-scale effects determine where on the organism's body, and from what viewpoints and under what illumination, the iridescent colors are seen. Thus, the highly directional flash of brilliant color from the iridescent throat of a hummingbird is inadequately explained by its nano-scale structure alone and questions remain. From a given observation point, which milli-scale elements of the feather are oriented to reflect strongly? Do some species produce broader "windows" for observation of iridescence than others? These and similar questions may be asked about any organisms that have evolved a particular surface appearance for signaling, camouflage, or other reasons. In order to study the directional patterns of light scattering from feathers, and their relationship to the bird's milli-scale morphology, we developed a protocol for measuring light scattered from biological materials using many high-resolution photographs taken with varying illumination and viewing directions. Since we measure scattered light as a function of direction, we can observe the characteristic features in the directional distribution of light scattered from that particular feather, and because barbs and barbules are resolved in our images, we can clearly attribute the directional features to these different milli-scale structures. Keeping the specimen intact preserves the gross-scale scattering behavior seen in nature. The method described here presents a generalized protocol for analyzing spatially- and directionally-varying light scattering from complex biological materials at multiple

Spatiotemporal matrix image formation for programmable ultrasound scanners

Science.gov (United States)

Berthon, Beatrice; Morichau-Beauchant, Pierre; Porée, Jonathan; Garofalakis, Anikitos; Tavitian, Bertrand; Tanter, Mickael; Provost, Jean

2018-02-01

As programmable ultrasound scanners become more common in research laboratories, it is increasingly important to develop robust software-based image formation algorithms that can be obtained in a straightforward fashion for different types of probes and sequences with a small risk of error during implementation. In this work, we argue that as the computational power keeps increasing, it is becoming practical to directly implement an approximation to the matrix operator linking reflector point targets to the corresponding radiofrequency signals via thoroughly validated and widely available simulations software. Once such a spatiotemporal forward-problem matrix is constructed, standard and thus highly optimized inversion procedures can be leveraged to achieve very high quality images in real time. Specifically, we show that spatiotemporal matrix image formation produces images of similar or enhanced quality when compared against standard delay-and-sum approaches in phantoms and in vivo, and show that this approach can be used to form images even when using non-conventional probe designs for which adapted image formation algorithms are not readily available.

Research on Process-oriented Spatio-temporal Data Model

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XUE Cunjin

2016-02-01

Full Text Available According to the analysis of the present status and existing problems of spatio-temporal data models developed in last 20 years,this paper proposes a process-oriented spatio-temporal data model (POSTDM,aiming at representing,organizing and storing continuity and gradual geographical entities. The dynamic geographical entities are graded and abstracted into process objects series from their intrinsic characteristics,which are process objects,process stage objects,process sequence objects and process state objects. The logical relationships among process entities are further studied and the structure of UML models and storage are also designed. In addition,through the mechanisms of continuity and gradual changes impliedly recorded by process objects,and the modes of their procedure interfaces offered by the customized ObjcetStorageTable,the POSTDM can carry out process representation,storage and dynamic analysis of continuity and gradual geographic entities. Taking a process organization and storage of marine data as an example,a prototype system (consisting of an object-relational database and a functional analysis platform is developed for validating and evaluating the model's practicability.

Spatiotemporal throughfall patterns beneath an urban tree row

Science.gov (United States)

Bogeholz, P.; Van Stan, J. T., II; Hildebrandt, A.; Friesen, J.; Dibble, M.; Norman, Z.

2016-12-01

Much recent research has focused on throughfall patterns in natural forests as they can influence the heterogeneity of surface ecohydrological and biogeochemical processes. However, to the knowledge of the authors, no work has assessed how urban forest structures affect the spatiotemporal variability of throughfall water flux. Urbanization greatly alters not only a significant portion of the land surface, but canopy structure, with the most typical urban forest configuration being landscaped tree rows along streets, swales, parking lot medians, etc. This study examines throughfall spatiotemporal patterns for a landscaped tree row of Pinus elliottii (Engelm., slash pine) on Georgia Southern University's campus (southeastern, USA) using 150 individual observations per storm. Throughfall correlation lengths beneath this tree row were similar to, but appeared to be more stable across storm size than, observations in past studies on natural forests. Individual tree overlap and the planting interval also may more strongly drive throughfall patterns in tree rows. Meteorological influences beyond storm magnitude (intensity, intermittency, wind conditions, and atmospheric moisture demand) are also examined.

Intraurban Spatiotemporal Variability of Ambient Air Pollutants across Metropolitan St. Louis

Science.gov (United States)

Du, Li

Ambient air monitoring networks have been established in the United States since the 1970s to comply with the Clean Air Act. The monitoring networks are primarily used to determine compliance but also provide substantive support to air quality management and air quality research including studies on health effects of air pollutants. The Roxana Air Quality Study (RAQS) was conducted at the fenceline of a petroleum refinery in Roxana, Illinois. In addition to providing insights into air pollutant impacts from the refinery, these measurements increased the St. Louis area monitoring network density for gaseous air toxics and fine particulate matter (PM2.5) speciation and thus provided an opportunity to examine intraurban spatiotemporal variability for these air quality parameters. This dissertation focused on exploring and assessing aspects of ambient air pollutant spatiotemporal variability in the St. Louis area from three progressively expanded spatial scales using a suite of methods and metrics. RAQS data were used to characterize air quality conditions in the immediate vicinity of the petroleum refinery. For example, PM2.5 lanthanoids were used to track impacts from refinery fluidized bed catalytic cracker emissions. RAQS air toxics data were interpreted by comparing to network data from the Blair Street station in the City of St. Louis which is a National Air Toxics Trends Station. Species were classified as being spatially homogeneous (similar between sites) or heterogeneous (different between sites) and in the latter case these differences were interpreted using surface winds data. For PM 2.5 species, there were five concurrently operating sites in the St. Louis area - including the site in Roxana - which are either formally part of the national Chemical Speciation Network (CSN) or rigorously follow the CSN sampling and analytical protocols. This unusually large number of speciation sites for a region the size of St. Louis motivated a detailed examination of

Time-varying metamaterials based on graphene-wrapped microwires: Modeling and potential applications

Science.gov (United States)

Salary, Mohammad Mahdi; Jafar-Zanjani, Samad; Mosallaei, Hossein

2018-03-01

The successful realization of metamaterials and metasurfaces requires the judicious choice of constituent elements. In this paper, we demonstrate the implementation of time-varying metamaterials in the terahertz frequency regime by utilizing graphene-wrapped microwires as building blocks and modulation of graphene conductivity through exterior electrical gating. These elements enable enhancement of light-graphene interaction by utilizing optical resonances associated with Mie scattering, yielding a large tunability and modulation depth. We develop a semianalytical framework based on transition-matrix formulation for modeling and analysis of periodic and aperiodic arrays of such time-varying building blocks. The proposed method is validated against full-wave numerical results obtained using the finite-difference time-domain method. It provides an ideal tool for mathematical synthesis and analysis of space-time gradient metamaterials, eliminating the need for computationally expensive numerical models. Moreover, it allows for a wider exploration of exotic space-time scattering phenomena in time-modulated metamaterials. We apply the method to explore the role of modulation parameters in the generation of frequency harmonics and their emerging wavefronts. Several potential applications of such platforms are demonstrated, including frequency conversion, holographic generation of frequency harmonics, and spatiotemporal manipulation of light. The presented results provide key physical insights to design time-modulated functional metadevices using various building blocks and open up new directions in the emerging paradigm of time-modulated metamaterials.

Spatio-temporal distribution of soil-transmitted helminth infections in Brazil.

Science.gov (United States)

Chammartin, Frédérique; Guimarães, Luiz H; Scholte, Ronaldo Gc; Bavia, Mara E; Utzinger, Jürg; Vounatsou, Penelope

2014-09-18

In Brazil, preventive chemotherapy targeting soil-transmitted helminthiasis is being scaled-up. Hence, spatially explicit estimates of infection risks providing information about the current situation are needed to guide interventions. Available high-resolution national model-based estimates either rely on analyses of data restricted to a given period of time, or on historical data collected over a longer period. While efforts have been made to take into account the spatial structure of the data in the modelling approach, little emphasis has been placed on the temporal dimension. We extracted georeferenced survey data on the prevalence of infection with soil-transmitted helminths (i.e. Ascaris lumbricoides, hookworm and Trichuris trichiura) in Brazil from the Global Neglected Tropical Diseases (GNTD) database. Selection of the most important predictors of infection risk was carried out using a Bayesian geostatistical approach and temporal models that address non-linearity and correlation of the explanatory variables. The spatial process was estimated through a predictive process approximation. Spatio-temporal models were built on the selected predictors with integrated nested Laplace approximation using stochastic partial differential equations. Our models revealed that, over the past 20 years, the risk of soil-transmitted helminth infection has decreased in Brazil, mainly because of the reduction of A. lumbricoides and hookworm infections. From 2010 onwards, we estimate that the infection prevalences with A. lumbricoides, hookworm and T. trichiura are 3.6%, 1.7% and 1.4%, respectively. We also provide a map highlighting municipalities in need of preventive chemotherapy, based on a predicted soil-transmitted helminth infection risk in excess of 20%. The need for treatments in the school-aged population at the municipality level was estimated at 1.8 million doses of anthelminthic tablets per year. The analysis of the spatio-temporal aspect of the risk of infection

Gaze control during interceptive actions with different spatiotemporal demands.

NARCIS (Netherlands)

Navia, J.A.; Dicks, M.S.; van der Kamp, J; Ruiz, L.

It is widely accepted that the sources of information used to guide interceptive actions depend on conflicting spatiotemporal task demands. However, there is a paucity of evidence that shows how information pick-up during interceptive actions is adapted to such conflicting constraints. The present

a New Process-Oriented and Spatiotemporal Data Model for GIS Data

Science.gov (United States)

Shen, Y.

2018-04-01

With the rapid development of wireless sensor and information technology, there is a trend of transition from "digital monitoring" to "intelligence monitoring" advancing process. The traditional model cannot completely match the dynamic data to accurately describe changes of geographical and environmental changes. In this paper, we try to build a process-oriented and real-time spatiotemporal data model to meet the demands. With various types of monitoring devices, detection methods and the utilization of new technologies, the model can simulate the possible waterlog area in a specific year by analyzing the given data. By testing and modifying the spatiotemporal model, we can come to a rational conclusion that our model can forecast the actual situation in certain extent.

Quantification of annual wildfire risk; A spatio-temporal point process approach.

Directory of Open Access Journals (Sweden)

Paula Pereira

2013-10-01

Full Text Available Policy responses for local and global firemanagement depend heavily on the proper understanding of the fire extent as well as its spatio-temporal variation across any given study area. Annual fire risk maps are important tools for such policy responses, supporting strategic decisions such as location-allocation of equipment and human resources. Here, we define risk of fire in the narrow sense as the probability of its occurrence without addressing the loss component. In this paper, we study the spatio-temporal point patterns of wildfires and model them by a log Gaussian Cox processes. Themean of predictive distribution of randomintensity function is used in the narrow sense, as the annual fire risk map for next year.

Multi-scale window specification over streaming trajectories

Directory of Open Access Journals (Sweden)

Kostas Patroumpas

2013-12-01

Full Text Available Enormous amounts of positional information are collected by monitoring applications in domains such as fleet management, cargo transport, wildlife protection, etc. With the advent of modern location-based services, processing such data mostly focuses on providing real-time response to a variety of user requests in continuous and scalable fashion. An important class of such queries concerns evolving trajectories that continuously trace the streaming locations of moving objects, like GPS-equipped vehicles, commodities with RFID's, people with smartphones etc. In this work, we propose an advanced windowing operator that enables online, incremental examination of recent motion paths at multiple resolutions for numerous point entities. When applied against incoming positions, this window can abstract trajectories at coarser representations towards the past, while retaining progressively finer features closer to the present. We explain the semantics of such multi-scale sliding windows through parameterized functions that reflect the sequential nature of trajectories and can effectively capture their spatiotemporal properties. Such window specification goes beyond its usual role for non-blocking processing of multiple concurrent queries. Actually, it can offer concrete subsequences from each trajectory, thus preserving continuity in time and contiguity in space along the respective segments. Further, we suggest language extensions in order to express characteristic spatiotemporal queries using windows. Finally, we discuss algorithms for nested maintenance of multi-scale windows and evaluate their efficiency against streaming positional data, offering empirical evidence of their benefits to online trajectory processing.

Spatio-temporal distribution of fecal indicators in three rivers of the Haihe River Basin, China.

Science.gov (United States)

Wang, Yawei; Chen, Yanan; Zheng, Xiang; Gui, Chengmin; Wei, Yuansong

2017-04-01

Because of their significant impact on public health, waterborne pathogens, especially bacteria and viruses, are frequently monitored in surface water to assess microbial quality of water bodies. However, more than one billion people worldwide currently lack access to safe drinking water, and a diversity of waterborne outbreaks caused by pathogens is reported in nations at all levels of economic development. Spatio-temporal distribution of conventional pollutants and five pathogenic microorganisms were discussed for the Haihe River Basin. Land use and socio-economic assessments were coupled with comprehensive water quality monitoring. Physical, chemical, and biological parameters were measured at 20 different sites in the watershed for 1 year, including pH, temperature, conductivity, dissolved oxygen, turbidity, chemical oxygen demand, ammonia-N, total and fecal coliforms, E. coli, and Enterococcus. The results highlighted the high spatio-temporal variability in pathogen distribution at watershed scale: high concentration of somatic coliphages and fecal indicator bacteria in March and December and their very low concentration in June and September. All pathogens were positively correlated to urban/rural residential/industrial land and negatively correlated to other four land use types. Microbial pollution was greatly correlated with population density, urbanization rate, and percentage of the tertiary industry in the gross domestic product. In the future, river microbial risk control strategy should focus more on the effective management of secondary effluent of wastewater treatment plant and land around rivers.

Spatiotemporal Super-Resolution Reconstruction Based on Robust Optical Flow and Zernike Moment for Video Sequences

Directory of Open Access Journals (Sweden)

Meiyu Liang

2013-01-01

Full Text Available In order to improve the spatiotemporal resolution of the video sequences, a novel spatiotemporal super-resolution reconstruction model (STSR based on robust optical flow and Zernike moment is proposed in this paper, which integrates the spatial resolution reconstruction and temporal resolution reconstruction into a unified framework. The model does not rely on accurate estimation of subpixel motion and is robust to noise and rotation. Moreover, it can effectively overcome the problems of hole and block artifacts. First we propose an efficient robust optical flow motion estimation model based on motion details preserving, then we introduce the biweighted fusion strategy to implement the spatiotemporal motion compensation. Next, combining the self-adaptive region correlation judgment strategy, we construct a fast fuzzy registration scheme based on Zernike moment for better STSR with higher efficiency, and then the final video sequences with high spatiotemporal resolution can be obtained by fusion of the complementary and redundant information with nonlocal self-similarity between the adjacent video frames. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of both subjective visual and objective quantitative evaluations.

Synchronization of spatiotemporal chaotic systems by feedback control

International Nuclear Information System (INIS)

Lai, Y.; Grebogi, C.

1994-01-01

We demonstrate that two identical spatiotemporal chaotic systems can be synchronized by (1) linking one or a few of their dynamical variables, and (2) applying a small feedback control to one of the systems. Numerical examples using the diffusively coupled logistic map lattice are given. The effect of noise and the limitation of the technique are discussed

Fine-scale population genetic structure and short-range sex-biased dispersal in a solitary carnivore, Lutra lutra

Czech Academy of Sciences Publication Activity Database

Quaglietta, L.; Fonseca, V. C.; Hájková, Petra; Mira, A.; Boitani, L.

2013-01-01

Roč. 94, č. 3 (2013), s. 561-571 ISSN 0022-2372 Institutional support: RVO:68081766 Keywords : conservation genetics * dispersal distances * Eurasian otter * isolation by distance * radiotracking * restricted gene flow * spatial relatedness structure * spatiotemporal scale Subject RIV: EG - Zoology Impact factor: 2.225, year: 2013

Ecological divergence and conservatism: spatiotemporal patterns of niche evolution in a genus of livebearing fishes (Poeciliidae: Xiphophorus).

Science.gov (United States)

Culumber, Zachary W; Tobler, Michael

2016-02-19

Ecological factors often have a strong impact on spatiotemporal patterns of biodiversity. The integration of spatial ecology and phylogenetics allows for rigorous tests of whether speciation is associated with niche conservatism (constraints on ecological divergence) or niche divergence. We address this question in a genus of livebearing fishes for which the role of sexual selection in speciation has long been studied, but in which the potential role of ecological divergence during speciation has not been tested. By combining reconstruction of ancestral climate tolerances and disparity indices, we show that the earliest evolutionary split in Xiphophorus was associated with significant divergence for temperature variables. Niche evolution and present day niches were most closely associated with each species' geographic distribution relative to a biogeographic barrier, the Trans-Mexican Volcanic Belt. Tests for similarity of the environmental backgrounds of closely related species suggested that the relative importance of niche conservatism and divergence during speciation varied among the primary clades of Xiphophorus. Closely related species in the two swordtail clades exhibited higher levels of niche overlap than expected given environmental background similarity indicative of niche conservatism. In contrast, almost all species of platyfish had significantly divergent niches compared to environmental backgrounds, which is indicative of niche divergence. The results suggest that the relative importance of niche conservatism and divergence differed among the clades of Xiphophorus and that traits associated with niche evolution may be more evolutionarily labile in the platyfishes. Our results ultimately suggest that the taxonomic scale of tests for conservatism and divergence could greatly influence inferences of their relative importance in the speciation process.

Pain Recognition using Spatiotemporal Oriented Energy of Facial Muscles

DEFF Research Database (Denmark)

Irani, Ramin; Nasrollahi, Kamal; Moeslund, Thomas B.

2015-01-01

Pain is a critical sign in many medical situations and its automatic detection and recognition using computer vision techniques is of great importance. Utilizes this fact that pain is a spatiotemporal process, the proposed system in this paper employs steerable and separable filters to measures e...

Spatiotemporal Coupling of the Tongue in Amyotrophic Lateral Sclerosis

Science.gov (United States)

Kuruvilla, Mili S.; Green, Jordan R.; Yunusova, Yana; Hanford, Kathy

2012-01-01

Purpose: The primary aim of the investigation was to identify deficits in spatiotemporal coupling between tongue regions in amyotrophic lateral sclerosis (ALS). The relations between disease-related changes in tongue movement patterns and speech intelligibility were also determined. Methods: The authors recorded word productions from 11…

Synchronizing spatiotemporal chaos by introducing a finite flat region in the local map

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J. Y. Chen

2001-01-01

Full Text Available An approach to synchronize spatiotemporal chaos is proposed. It is achieved by introducing a finite flat region in the local map. By using this scheme, a number of orbits in both the drive and the response subsystems are forced to pass through a fixed point in every dimension. With only an arbitrary phase space variable as drive signal, synchronization of spatiotemporal chaos can be achieved rapidly in the response subsystem. This is an advantage when compared with other synchronization methods that require a linear combination of the original phase space variables.

Controlling spatio-temporal extreme events by decreasing the localized energy

International Nuclear Information System (INIS)

Du Lin; Xu Wei; Li Zhanguo; Zhou Bingchang

2011-01-01

The problem of controlling extreme events in spatially extended dynamical systems is investigated in this Letter. Based on observations of the system state, the control technique we proposed locally decreases the spatial energy of the amplitude in the vicinity of the highest burst, without needs of any knowledge or prediction of the system model. Considering the specific Complex Ginzburg-Landau equation, we provide theoretical analysis for designing the localized state feedback controller. More exactly, a simple control law by varying a damping parameter at control region is chose to achieve the control. Numerical simulations and statistic analysis demonstrate that extreme events can be efficiently suppressed by our strategy. In particular, the cost of the control and the tolerant time delay in applying the control is considered in detail. - Highlights: → We propose a local control scheme to suppress spatio-temporal extreme events. → The control is address by decreasing the spatial energy of the system locally. → The detail control law is to apply localized state feedback based on observations. → The cost of the control increases with the size of the control region exponentially. → The tolerant delay of the control is about 5-6 times of lifetime of extreme events.

DETERMINING SPATIO-TEMPORAL CADASTRAL DATA REQUIREMENT FOR INFRASTRUCTURE OF LADM FOR TURKEY

Directory of Open Access Journals (Sweden)

M. Alkan

2016-06-01

Full Text Available Nowadays, the nature of land title and cadastral (LTC data in the Turkey is dynamic from a temporal perspective which depends on the LTC operations. Functional requirements with respect to the characteristics are investigated based upon interviews of professionals in public and private sectors. These are; Legal authorities, Land Registry and Cadastre offices, Highway departments, Foundations, Ministries of Budget, Transportation, Justice, Public Works and Settlement, Environment and Forestry, Agriculture and Rural Affairs, Culture and Internal Affairs, State Institute of Statistics (SIS, execution offices, tax offices, real estate offices, private sector, local governments and banks. On the other hand, spatio-temporal LTC data very important component for creating infrastructure of Land Administration Model (LADM. For this reason, spatio-temporal LTC data needs for LADM not only updated but also temporal. The investigations ended up with determine temporal analyses of LTC data, traditional LTC system and tracing temporal analyses in traditional LTC system. In the traditional system, the temporal analyses needed by all these users could not be performed in a rapid and reliable way. The reason for this is that the traditional LTC system is a manual archiving system. The aims and general contents of this paper: (1 define traditional LTC system of Turkey; (2 determining the need for spatio-temporal LTC data and analyses for core domain model for LADM. As a results of temporal and spatio-temporal analysis LTC data needs, new system design is important for the Turkish LADM model. Designing and realizing an efficient and functional Temporal Geographic Information Systems (TGIS is inevitable for the Turkish LADM core infrastructure. Finally this paper outcome is creating infrastructure for design and develop LADM for Turkey.

Determining Spatio-Temporal Cadastral Data Requirement for Infrastructure of Ladm for Turkey

Science.gov (United States)

Alkan, M.; Polat, Z. A.

2016-06-01

Nowadays, the nature of land title and cadastral (LTC) data in the Turkey is dynamic from a temporal perspective which depends on the LTC operations. Functional requirements with respect to the characteristics are investigated based upon interviews of professionals in public and private sectors. These are; Legal authorities, Land Registry and Cadastre offices, Highway departments, Foundations, Ministries of Budget, Transportation, Justice, Public Works and Settlement, Environment and Forestry, Agriculture and Rural Affairs, Culture and Internal Affairs, State Institute of Statistics (SIS), execution offices, tax offices, real estate offices, private sector, local governments and banks. On the other hand, spatio-temporal LTC data very important component for creating infrastructure of Land Administration Model (LADM). For this reason, spatio-temporal LTC data needs for LADM not only updated but also temporal. The investigations ended up with determine temporal analyses of LTC data, traditional LTC system and tracing temporal analyses in traditional LTC system. In the traditional system, the temporal analyses needed by all these users could not be performed in a rapid and reliable way. The reason for this is that the traditional LTC system is a manual archiving system. The aims and general contents of this paper: (1) define traditional LTC system of Turkey; (2) determining the need for spatio-temporal LTC data and analyses for core domain model for LADM. As a results of temporal and spatio-temporal analysis LTC data needs, new system design is important for the Turkish LADM model. Designing and realizing an efficient and functional Temporal Geographic Information Systems (TGIS) is inevitable for the Turkish LADM core infrastructure. Finally this paper outcome is creating infrastructure for design and develop LADM for Turkey.

Mapping child maltreatment risk: a 12-year spatio-temporal analysis of neighborhood influences.

Science.gov (United States)

Gracia, Enrique; López-Quílez, Antonio; Marco, Miriam; Lila, Marisol

2017-10-18

'Place' matters in understanding prevalence variations and inequalities in child maltreatment risk. However, most studies examining ecological variations in child maltreatment risk fail to take into account the implications of the spatial and temporal dimensions of neighborhoods. In this study, we conduct a high-resolution small-area study to analyze the influence of neighborhood characteristics on the spatio-temporal epidemiology of child maltreatment risk. We conducted a 12-year (2004-2015) small-area Bayesian spatio-temporal epidemiological study with all families with child maltreatment protection measures in the city of Valencia, Spain. As neighborhood units, we used 552 census block groups. Cases were geocoded using the family address. Neighborhood-level characteristics analyzed included three indicators of neighborhood disadvantage-neighborhood economic status, neighborhood education level, and levels of policing activity-, immigrant concentration, and residential instability. Bayesian spatio-temporal modelling and disease mapping methods were used to provide area-specific risk estimations. Results from a spatio-temporal autoregressive model showed that neighborhoods with low levels of economic and educational status, with high levels of policing activity, and high immigrant concentration had higher levels of substantiated child maltreatment risk. Disease mapping methods were used to analyze areas of excess risk. Results showed chronic spatial patterns of high child maltreatment risk during the years analyzed, as well as stability over time in areas of low risk. Areas with increased or decreased child maltreatment risk over the years were also observed. A spatio-temporal epidemiological approach to study the geographical patterns, trends over time, and the contextual determinants of child maltreatment risk can provide a useful method to inform policy and action. This method can offer a more accurate description of the problem, and help to inform more

Spatiotemporal Relationships among Audiovisual Stimuli Modulate Auditory Facilitation of Visual Target Discrimination.

Science.gov (United States)

Li, Qi; Yang, Huamin; Sun, Fang; Wu, Jinglong

2015-03-01

Sensory information is multimodal; through audiovisual interaction, task-irrelevant auditory stimuli tend to speed response times and increase visual perception accuracy. However, mechanisms underlying these performance enhancements have remained unclear. We hypothesize that task-irrelevant auditory stimuli might provide reliable temporal and spatial cues for visual target discrimination and behavioral response enhancement. Using signal detection theory, the present study investigated the effects of spatiotemporal relationships on auditory facilitation of visual target discrimination. Three experiments were conducted where an auditory stimulus maintained reliable temporal and/or spatial relationships with visual target stimuli. Results showed that perception sensitivity (d') to visual target stimuli was enhanced only when a task-irrelevant auditory stimulus maintained reliable spatiotemporal relationships with a visual target stimulus. When only reliable spatial or temporal information was contained, perception sensitivity was not enhanced. These results suggest that reliable spatiotemporal relationships between visual and auditory signals are required for audiovisual integration during a visual discrimination task, most likely due to a spread of attention. These results also indicate that auditory facilitation of visual target discrimination follows from late-stage cognitive processes rather than early stage sensory processes. © 2015 SAGE Publications.

Spatiotemporal chaos involving wave instability.

Science.gov (United States)

Berenstein, Igal; Carballido-Landeira, Jorge

2017-01-01

In this paper, we investigate pattern formation in a model of a reaction confined in a microemulsion, in a regime where both Turing and wave instability occur. In one-dimensional systems, the pattern corresponds to spatiotemporal intermittency where the behavior of the systems alternates in both time and space between stationary Turing patterns and traveling waves. In two-dimensional systems, the behavior initially may correspond to Turing patterns, which then turn into wave patterns. The resulting pattern also corresponds to a chaotic state, where the system alternates in both space and time between standing wave patterns and traveling waves, and the local dynamics may show vanishing amplitude of the variables.

Broad-Scale Environmental Conditions Responsible for Post-Fire Vegetation Dynamics

OpenAIRE

Casady, Grant M.; Marsh, Stuart E.

2010-01-01

Ecosystem response to disturbance is influenced by environmental conditions at a number of scales. Changes in climate have altered fire regimes across the western United States, and have also likely altered spatio-temporal patterns of post-fire vegetation regeneration. Fire occurrence data and a vegetation index (NDVI) derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR) were used to monitor post-fire vegetation from 1989 to 2007. We first investigated differences in post-fi...

Spatiotemporally Representative and Cost-Efficient Sampling Design for Validation Activities in Wanglang Experimental Site

Directory of Open Access Journals (Sweden)

Gaofei Yin

2017-11-01

Full Text Available Spatiotemporally representative Elementary Sampling Units (ESUs are required for capturing the temporal variations in surface spatial heterogeneity through field measurements. Since inaccessibility often coexists with heterogeneity, a cost-efficient sampling design is mandatory. We proposed a sampling strategy to generate spatiotemporally representative and cost-efficient ESUs based on the conditioned Latin hypercube sampling scheme. The proposed strategy was constrained by multi-temporal Normalized Difference Vegetation Index (NDVI imagery, and the ESUs were limited within a sampling feasible region established based on accessibility criteria. A novel criterion based on the Overlapping Area (OA between the NDVI frequency distribution histogram from the sampled ESUs and that from the entire study area was used to assess the sampling efficiency. A case study in Wanglang National Nature Reserve in China showed that the proposed strategy improves the spatiotemporally representativeness of sampling (mean annual OA = 74.7% compared to the single-temporally constrained (OA = 68.7% and the random sampling (OA = 63.1% strategies. The introduction of the feasible region constraint significantly reduces in-situ labour-intensive characterization necessities at expenses of about 9% loss in the spatiotemporal representativeness of the sampling. Our study will support the validation activities in Wanglang experimental site providing a benchmark for locating the nodes of automatic observation systems (e.g., LAINet which need a spatially distributed and temporally fixed sampling design.

A simple spatiotemporal chaotic Lotka-Volterra model

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