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Angulo J.,University of Granada | Yu H.-L.,National Taiwan University | Langousis A.,University of Patras | Kolovos A.,SpaceTimeWorks LLC | And 4 more authors.
PLoS ONE | Year: 2013

This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multi-sourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the specification of transmission and recovery rates, the model studies the functional formulation of the evolution of the fractions of susceptible-infected-recovered individuals. The suggested approach is capable of: a) modeling population dynamics within and across localities, b) integrating the disease representation (i.e. susceptible-infected-recovered individuals) with observation time series at different geographical locations and other sources of information (e.g. hard and soft data, empirical relationships, secondary information), and c) generating predictions of disease spread and associated parameters in real time, while considering model and observation uncertainties. Key aspects of the proposed approach are illustrated by means of simulations (i.e. synthetic studies), and a real-world application using hand-foot-mouth disease (HFMD) data from China. © 2013 Angulo et al. Source


Yu H.-L.,National Taiwan University | Ku S.-J.,National Taiwan University | Kolovos A.,SpaceTimeWorks LLC
GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems | Year: 2012

Stochastic analysis and prediction is an important component of space-time data processing for a broad spectrum of Geographic Information Systems scientists and end users. For this task, a variety of numerical tools are available that are based on established statistical techniques. We present an original software tool that implements stochastic data analysis and prediction based on the Bayesian Maximum Entropy methodology, which has attractive advanced analytical features and has been known to address shortcomings of common mainstream techniques. The proposed tool contains a library of Bayesian Maximum Entropy analytical functions, and is available in the form of a plugin for the Quantum GIS open source Geographic Information System software. © 2012 Authors. Source


Yu H.-L.,National Taiwan University | Ku S.-C.,National Taiwan University | Kolovos A.,SpaceTimeWorks LLC
Stochastic Environmental Research and Risk Assessment | Year: 2016

In recent years, there has been a fast growing interest in the space–time data processing capacity of Geographic Information Systems (GIS). In this paper we present a new GIS-based tool for advanced geostatistical analysis of space–time data; it combines stochastic analysis, prediction, and GIS visualization technology. The proposed toolbox is based on the Bayesian Maximum Entropy theory that formulates its approach under a mature knowledge synthesis framework. We exhibit the toolbox features and use it for particulate matter spatiotemporal mapping in Taipei, in a proof-of-concept study where the serious preferential sampling issue is present. The proposed toolbox enables tight coupling of advanced spatiotemporal analysis functions with a GIS environment, i.e. QGIS. As a result, our contribution leads to a more seamless interaction between spatiotemporal analysis tools and GIS built-in functions; and utterly enhances the functionality of GIS software as a comprehensive knowledge processing and dissemination platform. © 2015, Springer-Verlag Berlin Heidelberg. Source


Zagouras A.,University of California at San Diego | Kolovos A.,SpaceTimeWorks LLC | Coimbra C.F.M.,University of California at San Diego
Renewable Energy | Year: 2015

Time-resolved characterization of solar irradiance at the ground level is a critical element in solar energy analysis. Siting of nodes in a network of solar irradiance monitoring stations (MS) is a multi-faceted problem that directly affects the determination of the solar resource and its spatio-temporal variability. The present work proposes an objective framework to optimize the deployment of solar MS over a sub-continental region. There are two main components in the proposed methodology. The first employs cluster analysis using the affinity propagation algorithm, to select the optimal number of clusters (regions with coherent solar microclimates) upon internal coherence criteria. The second component employs stochastic prediction and validation, through the use of a Bayesian maximum entropy method, and selects the optimal MS configuration, according to geostatistical criteria, among the solutions recommended by the cluster analysis. We apply this two-pronged methodology to determine clusters and optimal locations for global horizontal irradiance monitoring across the state of California. In this proof-of-concept study, 3 disparate MS configurations are examined within the cluster partition. The subsequent geostatistical analysis indicates that all configurations rank almost equally well based on different statistical error measures. The optimal configuration can be singled out depending on desired criteria of choice. © 2015 Elsevier Ltd. Source


Xie Y.-F.,Chinese Research Academy of Environmental Sciences | Li X.-W.,Shandong University | Wang J.-F.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Christakos G.,San Diego State University | And 4 more authors.
Science of the Total Environment | Year: 2012

Antibiotic residues in surface soils can lead to serious health risks and ecological hazards. Spatial mean concentration of antibiotic residues in the soil is the most important indicator of a region's environmental risk to antibiotic residues. Considerable estimation error would lead to an inefficient strategy of pollution control that happens when sample size is small and the estimation model does not match the spatial features of the object to be surveyed. On the basis of the available datasets, it was found that the distribution of antibiotics residue in soil follows a spatial stratification pattern. Accordingly, we used a new spatial estimation method called Mean of Surface with Non-homogeneity (MSN) to estimate antibiotic concentrations in surface soil of the Shandong Province, an important vegetable growing region in China. The standard error of the mean estimates obtained by MSN was significantly smaller (by about 1.02-6.82 μg/kg) than the estimation errors produced by three mainstream methods, simple arithmetic estimation (2.9-11.8 μg/kg), stratified estimation (2.5-10.6 μg/kg) and ordinary kriging estimation (2.2-8.2 μg/kg). © 2012 Elsevier B.V. Source

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