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Author :
Publisher :
ISBN 13 :
Total Pages : 86 pages
Book Rating : 4.:/5 (633 download)
Download or read book Spatio-temporal Modeling of Environmental and Health Processes written by and published by . This book was released on 2008 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Gavin Shaddick
Publisher : CRC Press
ISBN 13 : 1003808026
Total Pages : 458 pages
Book Rating : 4.0/5 (38 download)
Download or read book Spatio–Temporal Methods in Environmental Epidemiology with R written by Gavin Shaddick and published by CRC Press. This book was released on 2023-12-12 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatio-Temporal Methods in Environmental Epidemiology with R, like its First Edition, explores the interface between environmental epidemiology and spatio-temporal modeling. It links recent developments in spatio-temporal theory with epidemiological applications. Drawing on real-life problems, it shows how recent advances in methodology can assess the health risks associated with environmental hazards. The book's clear guidelines enable the implementation of the methodology and estimation of risks in practice. New additions to the Second Edition include: a thorough exploration of the underlying concepts behind knowledge discovery through data; a new chapter on extracting information from data using R and the tidyverse; additional material on methods for Bayesian computation, including the use of NIMBLE and Stan; new methods for performing spatio-temporal analysis and an updated chapter containing further topics. Throughout the book there are new examples, and the presentation of R code for examples has been extended. Along with these additions, the book now has a GitHub site (https://spacetime-environ.github.io/stepi2) that contains data, code and further worked examples. Features: • Explores the interface between environmental epidemiology and spatio-temporal modeling • Incorporates examples that show how spatio-temporal methodology can inform societal concerns about the effects of environmental hazards on health • Uses a Bayesian foundation on which to build an integrated approach to spatio-temporal modeling and environmental epidemiology • Discusses data analysis and topics such as data visualization, mapping, wrangling and analysis • Shows how to design networks for monitoring hazardous environmental processes and the ill effects of preferential sampling • Through the listing and application of code, shows the power of R, tidyverse, NIMBLE and Stan and other modern tools in performing complex data analysis and modeling Representing a continuing important direction in environmental epidemiology, this book – in full color throughout – underscores the increasing need to consider dependencies in both space and time when modeling epidemiological data. Readers will learn how to identify and model patterns in spatio-temporal data and how to exploit dependencies over space and time to reduce bias and inefficiency when estimating risks to health.
Author : Gavin Shaddick
Publisher : CRC Press
ISBN 13 : 1482237040
Total Pages : 383 pages
Book Rating : 4.4/5 (822 download)
Download or read book Spatio-Temporal Methods in Environmental Epidemiology written by Gavin Shaddick and published by CRC Press. This book was released on 2015-06-17 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological StudiesSpatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and
Author : Jungsoon Choi
Publisher :
ISBN 13 :
Total Pages : 102 pages
Book Rating : 4.:/5 (276 download)
Download or read book Multivariate Spatial-temporal Modeling of Environmental-health Processes written by Jungsoon Choi and published by . This book was released on 2008 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: multivariate spatial-temporal model, air pollution modeling, spatial epidemiology.
Author : George Christakos
Publisher : Springer Science & Business Media
ISBN 13 : 1475728115
Total Pages : 411 pages
Book Rating : 4.4/5 (757 download)
Download or read book Spatiotemporal Environmental Health Modelling: A Tractatus Stochasticus written by George Christakos and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatiotemporal Environmental Health Modelling: A Tractatus Stochasticus provides a holistic, conceptual and quantitative framework for Environmental Health Modelling in space-time. The holistic framework integrates two aspects of Environmental Health Science that have been previously treated separately: the environmental aspect, which involves the natural processes that bring about human exposure to harmful substances; and the health aspect, which focuses on the interactions of these substances with the human body. Some of the fundamental issues addressed in this work include variability, scale, uncertainty, and space-time connectivity. These topics are important in the characterization of natural systems and health processes. Spatiotemporal Environmental Health Modelling: A Tractatus Stochasticus explains why modern stochastics is the appropriate mechanical vehicle for addressing such issues in a rigorous way. In particular, modern stochastics incorporates concepts and methods from probability, classical statistics, geostatistics, statistical mechanics and field theory. The authors present a synthetic view of environmental health that embraces all of the various components and focuses on their mutual interactions. Spatiotemporal Environmental Health Modeling: A Tractatus Stochasticus includes new material on Bayesian maximum entropy estimation techniques and space-time random field estimation methods. The authors show why these methods have clear advantages over the classical geostatistical estimation procedures and how they can be used to provide accurate space-time maps of environmental health processes. Also included are expositions of diagrammatic perturbation and renormalization group analysis, which have not been previously discussed within the context of Environmental Health. Finally, the authors present stochastic indicators that can be used for large-scale characterization of contamination and investigations of health effects at the microscopic level. This book will be a useful reference to both researchers and practitioners of Environmental Health Sciences. It will appeal specifically to environmental engineers, geographers, geostatisticians, earth scientists, toxicologists, epidemiologists, pharmacologists, applied mathematicians, physicists and biologists.
Download or read book Multivariate Spatial-Temporal Modeling of Environmental-Health Processes written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In many applications in environmental sciences and epidemiology, data are often collected over space and time. In some cases, the spatial-temporal data of interest are multivariate, and these multivariate spatial-temporal processes often have a complicated dependency structure. Hence, multivariate spatial-temporal modeling is a very challenging task. In this study, we develop statistical models to effectively account for multivariate spatial-temporal dependency structures of air pollution concentrations and human health outcomes. Fine particulate matter (PM2.5) is an atmospheric pollutant that has been linked to serious health problems, including mortality. PM2.5 has five main components: sulfate, nitrate, total carbonaceous mass, ammonium, and crustal material. These components have complex spatial-temporal dependency and cross dependency structures. It is important to gain better understanding about the spatial-temporal distribution of each component of the total PM2.5 mass, and also to estimate how the composition of PM2.5 changes with space and time. We introduce a multivariate spatial-temporal model for speciated PM2.5. Our hierarchical framework combines different sources of data and accounts for potential bias. In addition, a spatiotemporal extension of the linear model of coregionalization is developed to account for spatial and temporal dependency structures for each component as well as the associations among the components. We apply our framework to speciated PM2.5 data in the United States for the year 2004. In addition, the chemical composition of PM2.5 varies across space and time so the association between PM2.5 and mortality could change with space and season. Thus, we develop and implement a multi-stage Bayesian framework that provides a very broad and flexible approach to studying the spatial-temporal associations between mortality and population exposure to daily PM2.5 mass, while accounting for different sources of uncertainty. In the first stage.
Author : Gavin Shaddick
Publisher :
ISBN 13 : 9781003352655
Total Pages : 0 pages
Book Rating : 4.3/5 (526 download)
Download or read book Spatio–Temporal Methods in Environmental Epidemiology with R written by Gavin Shaddick and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : George Christakos
Publisher : Springer
ISBN 13 : 9781441950482
Total Pages : 400 pages
Book Rating : 4.9/5 (54 download)
Download or read book Spatiotemporal Environmental Health Modelling: A Tractatus Stochasticus written by George Christakos and published by Springer. This book was released on 2010-11-30 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatiotemporal Environmental Health Modelling: A Tractatus Stochasticus provides a holistic, conceptual and quantitative framework for Environmental Health Modelling in space-time. The holistic framework integrates two aspects of Environmental Health Science that have been previously treated separately: the environmental aspect, which involves the natural processes that bring about human exposure to harmful substances; and the health aspect, which focuses on the interactions of these substances with the human body. Some of the fundamental issues addressed in this work include variability, scale, uncertainty, and space-time connectivity. These topics are important in the characterization of natural systems and health processes. Spatiotemporal Environmental Health Modelling: A Tractatus Stochasticus explains why modern stochastics is the appropriate mechanical vehicle for addressing such issues in a rigorous way. In particular, modern stochastics incorporates concepts and methods from probability, classical statistics, geostatistics, statistical mechanics and field theory. The authors present a synthetic view of environmental health that embraces all of the various components and focuses on their mutual interactions. Spatiotemporal Environmental Health Modeling: A Tractatus Stochasticus includes new material on Bayesian maximum entropy estimation techniques and space-time random field estimation methods. The authors show why these methods have clear advantages over the classical geostatistical estimation procedures and how they can be used to provide accurate space-time maps of environmental health processes. Also included are expositions of diagrammatic perturbation and renormalization group analysis, which have not been previously discussed within the context of Environmental Health. Finally, the authors present stochastic indicators that can be used for large-scale characterization of contamination and investigations of health effects at the microscopic level. This book will be a useful reference to both researchers and practitioners of Environmental Health Sciences. It will appeal specifically to environmental engineers, geographers, geostatisticians, earth scientists, toxicologists, epidemiologists, pharmacologists, applied mathematicians, physicists and biologists.
Author : Noel Cressie
Publisher : John Wiley & Sons
ISBN 13 : 1119243068
Total Pages : 596 pages
Book Rating : 4.1/5 (192 download)
Download or read book Statistics for Spatio-Temporal Data written by Noel Cressie and published by John Wiley & Sons. This book was released on 2015-11-02 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the 2013 DeGroot Prize. A state-of-the-art presentation of spatio-temporal processes, bridging classic ideas with modern hierarchical statistical modeling concepts and the latest computational methods Noel Cressie and Christopher K. Wikle, are also winners of the 2011 PROSE Award in the Mathematics category, for the book “Statistics for Spatio-Temporal Data” (2011), published by John Wiley and Sons. (The PROSE awards, for Professional and Scholarly Excellence, are given by the Association of American Publishers, the national trade association of the US book publishing industry.) Statistics for Spatio-Temporal Data has now been reprinted with small corrections to the text and the bibliography. The overall content and pagination of the new printing remains the same; the difference comes in the form of corrections to typographical errors, editing of incomplete and missing references, and some updated spatio-temporal interpretations. From understanding environmental processes and climate trends to developing new technologies for mapping public-health data and the spread of invasive-species, there is a high demand for statistical analyses of data that take spatial, temporal, and spatio-temporal information into account. Statistics for Spatio-Temporal Data presents a systematic approach to key quantitative techniques that incorporate the latest advances in statistical computing as well as hierarchical, particularly Bayesian, statistical modeling, with an emphasis on dynamical spatio-temporal models. Cressie and Wikle supply a unique presentation that incorporates ideas from the areas of time series and spatial statistics as well as stochastic processes. Beginning with separate treatments of temporal data and spatial data, the book combines these concepts to discuss spatio-temporal statistical methods for understanding complex processes. Topics of coverage include: Exploratory methods for spatio-temporal data, including visualization, spectral analysis, empirical orthogonal function analysis, and LISAs Spatio-temporal covariance functions, spatio-temporal kriging, and time series of spatial processes Development of hierarchical dynamical spatio-temporal models (DSTMs), with discussion of linear and nonlinear DSTMs and computational algorithms for their implementation Quantifying and exploring spatio-temporal variability in scientific applications, including case studies based on real-world environmental data Throughout the book, interesting applications demonstrate the relevance of the presented concepts. Vivid, full-color graphics emphasize the visual nature of the topic, and a related FTP site contains supplementary material. Statistics for Spatio-Temporal Data is an excellent book for a graduate-level course on spatio-temporal statistics. It is also a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.
Author : Ali Arab
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (182 download)
Download or read book Hierarchical Spatio-temporal Models for Environmental Processes written by Ali Arab and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The processes governing environmental systems are often complex, involving different interacting scales of variability in space and time. The complexities and often high dimensionality of such spatio-temporal processes can be effectively addressed using a hierarchical modeling framework where a complex problem is decomposed into a series of simpler problems that are linked through rules of probability. In this dissertation, hierarchical spatio-temporal models are developed and utilized for environmental processes. The methods discussed in this dissertation include a wide scope of problems related to the modeling of spatio-temporal environmental processes. Specifically, methods are described for efficient modeling of spatio-temporal environmental processes using both discrete- and continuous valued data.
Author : Jorge Mateu
Publisher : John Wiley & Sons
ISBN 13 : 1118441885
Total Pages : 320 pages
Book Rating : 4.1/5 (184 download)
Download or read book Spatio-temporal Design written by Jorge Mateu and published by John Wiley & Sons. This book was released on 2012-11-05 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: A state-of-the-art presentation of optimum spatio-temporal sampling design - bridging classic ideas with modern statistical modeling concepts and the latest computational methods. Spatio-temporal Design presents a comprehensive state-of-the-art presentation combining both classical and modern treatments of network design and planning for spatial and spatio-temporal data acquisition. A common problem set is interwoven throughout the chapters, providing various perspectives to illustrate a complete insight to the problem at hand. Motivated by the high demand for statistical analysis of data that takes spatial and spatio-temporal information into account, this book incorporates ideas from the areas of time series, spatial statistics and stochastic processes, and combines them to discuss optimum spatio-temporal sampling design. Spatio-temporal Design: Advances in Efficient Data Acquisition: Provides an up-to-date account of how to collect space-time data for monitoring, with a focus on statistical aspects and the latest computational methods Discusses basic methods and distinguishes between design and model-based approaches to collecting space-time data. Features model-based frequentist design for univariate and multivariate geostatistics, and second-phase spatial sampling. Integrates common data examples and case studies throughout the book in order to demonstrate the different approaches and their integration. Includes real data sets, data generating mechanisms and simulation scenarios. Accompanied by a supporting website featuring R code. Spatio-temporal Design presents an excellent book for graduate level students as well as a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.
Author : Andrew B. Lawson
Publisher : CRC Press
ISBN 13 : 1000376702
Total Pages : 300 pages
Book Rating : 4.0/5 (3 download)
Download or read book Using R for Bayesian Spatial and Spatio-Temporal Health Modeling written by Andrew B. Lawson and published by CRC Press. This book was released on 2021-04-28 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies. Features: Review of R graphics relevant to spatial health data Overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data Bayesian Computation and goodness-of-fit Review of basic Bayesian disease mapping models Spatio-temporal modeling with MCMC and INLA Special topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling Software for fitting models based on BRugs, Nimble, CARBayes and INLA Provides code relevant to fitting all examples throughout the book at a supplementary website The book fills a void in the literature and available software, providing a crucial link for students and professionals alike to engage in the analysis of spatial and spatio-temporal health data from a Bayesian perspective using R. The book emphasizes the use of MCMC via Nimble, BRugs, and CARBAyes, but also includes INLA for comparative purposes. In addition, a wide range of packages useful in the analysis of geo-referenced spatial data are employed and code is provided. It will likely become a key reference for researchers and students from biostatistics, epidemiology, public health, and environmental science.
Author : Daniel Gladish
Publisher :
ISBN 13 :
Total Pages : 158 pages
Book Rating : 4.:/5 (899 download)
Download or read book Spatio-temporal Models with Time-varying Spatial Model Error for Environmental Processes written by Daniel Gladish and published by . This book was released on 2013 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Environmental processes exhibit uncertainty in the spatial and temporal domains. Often, mechanistic forecast models, such as weather forecasting systems, may not necessarily match the observed data, resulting in the need for a stochastic error term. This is common in the context of data assimilation, where one seeks to blend observations and mechanistic (deterministic) models to create complete spatio-temporal fields and their uncertainty. The observation and state-process error covariances play important roles in the development and implementation of such data assimilation models. However, the mechanistic models in this framework depend on approximations that may fail under certain real-world conditions. These models may be inadequate for various reasons, such as the need for parameters to vary through time and/or space, incomplete knowledge of the process, improper assumptions, such as temporal stationarity, or incomplete data. As such, spatio-temporal structure may exist in the resulting misfit error process. Thus, the evolution of spatial covariances may be nonstationary in time. Because of the complexities involved in characterizing these error processes, Bayesian hierarchical models (BHMs) provide an appropriate framework for modeling time-varying covariances through appropriate decompositions of the spatial covariance matrix. In this dissertation, we develop models that account for the evolution of time-varying covariance matrices in dynamical spatio-temporal models.
Author : Andrew William Correia
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (864 download)
Download or read book Estimating the Health Effects of Environmental Exposures: Statistical Methods for the Analysis of Spatio-temporal Data written by Andrew William Correia and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the field of environmental epidemiology, there is a great deal of care required in constructing models that accurately estimate the effects of environmental exposures on human health. This is because the nature of the data that is available to researchers to estimate these effects is almost always observational in nature, making it difficult to adequately control for all potential confounders - both measured and unmeasured. Here, we tackle three different problems in which the goal is to accurately estimate the effect of an environmental exposure on various health outcomes.
Author : Gouri Sankar Bhunia
Publisher : Springer Nature
ISBN 13 : 3030711986
Total Pages : 298 pages
Book Rating : 4.0/5 (37 download)
Download or read book GeoComputation and Public Health written by Gouri Sankar Bhunia and published by Springer Nature. This book was released on 2021-06-24 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: GeoComputation and Public Health is fundamentally a multi-disciplinary book, which presents an overview and case studies to exemplify numerous methods and solicitations in addressing vectors borne diseases (e.g, Visceral leishmaniasis, Malaria, Filaria). This book includes a practical coverage of the use of spatial analysis techniques in vector-borne disease using open source software solutions. Environmental factors (relief characters, climatology, ecology, vegetation, water bodies etc.) and socio-economic issues (housing type & pattern, education level, economic status, income level, domestics’ animals, census data, etc) are investigated at micro -level and large scale in addressing the various vector-borne disease. This book will also generate a framework for interdisciplinary discussion, latest innovations, and discoveries on public health. The first section of the book highlights the basic and principal aspects of advanced computational practices. Other sections of the book contain geo-simulation, agent-based modeling, spatio-temporal analysis, geospatial data mining, various geocomputational applications, accuracy and uncertainty of geospatial models, applications in environmental, ecological, and biological modeling and analysis in public health research. This book will be useful to the postgraduate students of geography, remote sensing, ecology, environmental sciences and research scholars, along with health professionals looking to solve grand challenges and management on public health.
Author : Sara Zapata-Marin
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (135 download)
Download or read book Land-use Regression and Spatio-temporal Hierarchical Models for Environmental Processes written by Sara Zapata-Marin and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Land-use regression is a popular method used to describe the spatial variability of different environmental processes using local variables. However, there are situations in which there might be some complex spatio-temporal structure left after accounting for land-use variables.In this work, three different Bayesian hierarchical models are proposed to model the spatial and spatio-temporal dispersion of air pollutants and aeroallergens within cities. Bayesian inference can easily accommodate complex interactions while naturally accounting for uncertainties in the estimation of unknowns in the model when performing predictions.In the first study, a spatial hierarchical model is used to analyze the concentration of volatile organic compounds (VOCs) in Montreal, Canada. The data consists of concentration measurements of five VOCs measured over two-week periods for three monitoring campaigns between 2005 and 2006 over 130 locations in the city. The five VOCs of interest are: benzene, decane, ethylbenzene, hexane, and trimethylbenzene. Four different models are fitted to each of the five VOCs. These models extend land-use regression by accounting for any spatial structure left after including the covariates while also capturing the across campaign variation through an indicator variable or campaign-specific coefficients. Predicted surfaces are obtained for each campaign. For all VOCs higher levels are found during the December campaign, and the predicted areas with the highest levels correspond to multiple sections of major highways.For the second and third studies, we have available data on the daily and weekly measurements of pollen concentration in Toronto, Canada collected in 2018. The measurements consist of tree, weed, grass, and total pollen concentration at 18 monitoring sites and were obtained daily for eleven of these sites and weekly for the other seven sites.In the second study, the weekly concentration of each of the four pollen types is modeled. Instead of considering the temporal window that only has positive values, that is, removing the zeros, a hurdle model is proposed to account for the high number of measurements equal to zero. This structure allows for the estimation of the probability of the pollen concentration being equal to zero at any given week, which provides further information on temporal windows with positive concentrations of the different types of pollen. Additionally, a dynamic linear model is used to capture the weekly trend of pollen concentration in the city.In the third study, the daily concentration of total pollen is modeled. Rather than aggregating the data to the weekly scale, a temporal misalignment model is proposed to account for the difference in scale and to take advantage of the daily measurements. Using the properties of dynamic linear models and the multivariate normal distribution, a spatio-temporal model to account for temporal misalignment is proposed. This model allows to estimate the fine-scale measurements at locations where only coarse-scale observations were available. Additionally, the model is fitted to artificial data with different temporal structures, including trend and seasonality.The predicted surfaces obtained in these three studies will help inform future health-related studies. Furthermore, the methods proposed here are flexible, easily adaptable, and can improve our understanding of similar environmental processes. All codes are publicly available such that the implementation of the proposed approach in similar situations is easily achieved"--
Author : Christopher K. Wikle
Publisher : CRC Press
ISBN 13 : 0429649789
Total Pages : 380 pages
Book Rating : 4.4/5 (296 download)
Download or read book Spatio-Temporal Statistics with R written by Christopher K. Wikle and published by CRC Press. This book was released on 2019-02-18 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these "big data" that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation Provides a gradual entry to the methodological aspects of spatio-temporal statistics Provides broad coverage of using R as well as "R Tips" throughout. Features detailed examples and applications in end-of-chapter Labs Features "Technical Notes" throughout to provide additional technical detail where relevant Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.
