Using R For Bayesian Spatial And Spatio Temporal Health Modeling

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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Author : Andrew B. Lawson
Publisher : CRC Press
ISBN 13 : 1000376702
Total Pages : 300 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Using R for Bayesian Spatial and Spatio-Temporal Health Modeling by : Andrew B. Lawson

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.

Spatial and Spatio-temporal Bayesian Models with R - INLA

Author : Marta Blangiardo
Publisher : John Wiley & Sons
ISBN 13 : 1118326555
Total Pages : 321 pages
Book Rating : 4.1/5 (183 download)

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Book Synopsis Spatial and Spatio-temporal Bayesian Models with R - INLA by : Marta Blangiardo

Download or read book Spatial and Spatio-temporal Bayesian Models with R - INLA written by Marta Blangiardo and published by John Wiley & Sons. This book was released on 2015-06-02 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations

Bayesian Modeling of Spatio-Temporal Data with R

Author : Sujit Sahu
Publisher : CRC Press
ISBN 13 : 1000543692
Total Pages : 385 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Bayesian Modeling of Spatio-Temporal Data with R by : Sujit Sahu

Download or read book Bayesian Modeling of Spatio-Temporal Data with R written by Sujit Sahu and published by CRC Press. This book was released on 2022-02-23 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of spatial and spatio-temporal data in order to make wide ranging inference and prediction. Ideally such inferential tasks should be approached through modelling, which aids in estimation of uncertainties in all conclusions drawn from such data. Unified Bayesian modelling, implemented through user friendly software packages, provides a crucial key to unlocking the full power of these methods for solving challenging practical problems. Key features of the book: • Accessible detailed discussion of a majority of all aspects of Bayesian methods and computations with worked examples, numerical illustrations and exercises • A spatial statistics jargon buster chapter that enables the reader to build up a vocabulary without getting clouded in modeling and technicalities • Computation and modeling illustrations are provided with the help of the dedicated R package bmstdr, allowing the reader to use well-known packages and platforms, such as rstan, INLA, spBayes, spTimer, spTDyn, CARBayes, CARBayesST, etc • Included are R code notes detailing the algorithms used to produce all the tables and figures, with data and code available via an online supplement • Two dedicated chapters discuss practical examples of spatio-temporal modeling of point referenced and areal unit data • Throughout, the emphasis has been on validating models by splitting data into test and training sets following on the philosophy of machine learning and data science This book is designed to make spatio-temporal modeling and analysis accessible and understandable to a wide audience of students and researchers, from mathematicians and statisticians to practitioners in the applied sciences. It presents most of the modeling with the help of R commands written in a purposefully developed R package to facilitate spatio-temporal modeling. It does not compromise on rigour, as it presents the underlying theories of Bayesian inference and computation in standalone chapters, which would be appeal those interested in the theoretical details. By avoiding hard core mathematics and calculus, this book aims to be a bridge that removes the statistical knowledge gap from among the applied scientists.

Geospatial Health Data

Author : Paula Moraga
Publisher : CRC Press
ISBN 13 : 1000732150
Total Pages : 217 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Geospatial Health Data by : Paula Moraga

Download or read book Geospatial Health Data written by Paula Moraga and published by CRC Press. This book was released on 2019-11-26 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.

Regression Modelling wih Spatial and Spatial-Temporal Data

Author : Robert P. Haining
Publisher : CRC Press
ISBN 13 : 0429529104
Total Pages : 527 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Regression Modelling wih Spatial and Spatial-Temporal Data by : Robert P. Haining

Download or read book Regression Modelling wih Spatial and Spatial-Temporal Data written by Robert P. Haining and published by CRC Press. This book was released on 2020-01-27 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.

Spatio-Temporal Methods in Environmental Epidemiology

Author : Gavin Shaddick
Publisher : CRC Press
ISBN 13 : 1482237040
Total Pages : 395 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Spatio-Temporal Methods in Environmental Epidemiology by : Gavin Shaddick

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 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological Studies Spatio-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 environmental epidemiologists, the book links recent developments in spatio-temporal methodology with epidemiological applications. Drawing on real-life problems, it provides the necessary tools to exploit advances in methodology when assessing the health risks associated with environmental hazards. The book’s clear guidelines enable the implementation of the methodology and estimation of risks in practice. Designed for graduate students in both epidemiology and statistics, the text covers a wide range of topics, from an introduction to epidemiological principles and the foundations of spatio-temporal modeling to new research directions. It describes traditional and Bayesian approaches and presents the theory of spatial, temporal, and spatio-temporal modeling in the context of its application to environmental epidemiology. The text includes practical examples together with embedded R code, details of specific R packages, and the use of other software, such as WinBUGS/OpenBUGS and integrated nested Laplace approximations (INLA). A supplementary website provides additional code, data, examples, exercises, lab projects, and more. Representing a major new 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. Students will learn how to identify and model patterns in spatio-temporal data as well as exploit dependencies over space and time to reduce bias and inefficiency.

Spatio-Temporal Statistics with R

Author : Christopher K. Wikle
Publisher : CRC Press
ISBN 13 : 0429649789
Total Pages : 380 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Spatio-Temporal Statistics with R by : Christopher K. Wikle

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.

Spatio–Temporal Methods in Environmental Epidemiology with R

Author : Gavin Shaddick
Publisher : CRC Press
ISBN 13 : 1003808026
Total Pages : 458 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Spatio–Temporal Methods in Environmental Epidemiology with R by : Gavin Shaddick

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.

Handbook of Spatial Epidemiology

Author : Andrew B. Lawson
Publisher : CRC Press
ISBN 13 : 148225302X
Total Pages : 704 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Handbook of Spatial Epidemiology by : Andrew B. Lawson

Download or read book Handbook of Spatial Epidemiology written by Andrew B. Lawson and published by CRC Press. This book was released on 2016-04-06 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Spatial Epidemiology explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space-time variations in disease incidences. These analyses can provide imp

Disease Mapping

Author : Miguel A. Martinez-Beneito
Publisher : CRC Press
ISBN 13 : 1351645021
Total Pages : 371 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Disease Mapping by : Miguel A. Martinez-Beneito

Download or read book Disease Mapping written by Miguel A. Martinez-Beneito and published by CRC Press. This book was released on 2019-07-02 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field. A multidimensional framework is offered that makes possible the joint modeling of several risks patterns corresponding to combinations of several factors, including age group, time period, disease, etc. Although theory will be covered, the applied component will be equally as important with lots of practical examples offered. Features: Discusses the very latest developments on multivariate and multidimensional mapping. Gives a single state-of-the-art framework that unifies most of the previously proposed disease mapping approaches. Balances epidemiological and statistical points-of-view. Requires no previous knowledge of disease mapping. Includes practical sessions at the end of each chapter with WinBUGs/INLA and real world datasets. Supplies R code for the examples in the book so that they can be reproduced by the reader. About the Authors: Miguel A. Martinez Beneito has spent his whole career working as a statistician for public health services, first at the epidemiology unit of the Valencia (Spain) regional health administration and later as a researcher at the public health division of FISABIO, a regional bio-sanitary research center. He has been also the Bayesian Hierarchical Models professor for several seasons at the University of Valencia Biostatics Master. Paloma Botella Rocamora has spent most of her professional career in academia although she now works as a statistician for the epidemiology unit of the Valencia regional health administration. Most of her research has been devoted to developing and applying disease mapping models to real data, although her work as a statistician in an epidemiology unit makes her develop and apply statistical methods to health data, in general.

Statistics for Spatio-Temporal Data

Author : Noel Cressie
Publisher : John Wiley & Sons
ISBN 13 : 1119243041
Total Pages : 624 pages
Book Rating : 4.1/5 (192 download)

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Book Synopsis Statistics for Spatio-Temporal Data by : Noel Cressie

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 624 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 statisticalmodeling concepts and the latest computational methods Noel Cressie and Christopher K. Wikle, are also winnersof the 2011 PROSE Award in the Mathematics category, for thebook “Statistics for Spatio-Temporal Data” (2011),published by John Wiley and Sons. (The PROSE awards, forProfessional and Scholarly Excellence, are given by the Associationof American Publishers, the national trade association of the USbook publishing industry.) Statistics for Spatio-Temporal Data has now beenreprinted with small corrections to the text andthe bibliography. The overall content and pagination of thenew printing remains the same; the difference comes inthe form of corrections to typographical errors, editing ofincomplete and missing references, and some updated spatio-temporalinterpretations. From understanding environmental processes and climate trends todeveloping new technologies for mapping public-health data and thespread of invasive-species, there is a high demand for statisticalanalyses of data that take spatial, temporal, and spatio-temporalinformation into account. Statistics for Spatio-TemporalData presents a systematic approach to key quantitativetechniques that incorporate the latest advances in statisticalcomputing as well as hierarchical, particularly Bayesian,statistical modeling, with an emphasis on dynamical spatio-temporalmodels. Cressie and Wikle supply a unique presentation thatincorporates ideas from the areas of time series and spatialstatistics as well as stochastic processes. Beginning with separatetreatments of temporal data and spatial data, the book combinesthese concepts to discuss spatio-temporal statistical methods forunderstanding complex processes. Topics of coverage include: Exploratory methods for spatio-temporal data, includingvisualization, spectral analysis, empirical orthogonal functionanalysis, 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 andcomputational algorithms for their implementation Quantifying and exploring spatio-temporal variability inscientific applications, including case studies based on real-worldenvironmental data Throughout the book, interesting applications demonstrate therelevance of the presented concepts. Vivid, full-color graphicsemphasize the visual nature of the topic, and a related FTP sitecontains supplementary material. Statistics for Spatio-TemporalData is an excellent book for a graduate-level course onspatio-temporal statistics. It is also a valuable reference forresearchers and practitioners in the fields of applied mathematics,engineering, and the environmental and health sciences.

Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Author : Elias T. Krainski
Publisher : CRC Press
ISBN 13 : 0429629850
Total Pages : 284 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA by : Elias T. Krainski

Download or read book Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA written by Elias T. Krainski and published by CRC Press. This book was released on 2018-12-07 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

Bayesian Disease Mapping

Author : Andrew B. Lawson
Publisher : CRC Press
ISBN 13 : 1466504811
Total Pages : 398 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Bayesian Disease Mapping by : Andrew B. Lawson

Download or read book Bayesian Disease Mapping written by Andrew B. Lawson and published by CRC Press. This book was released on 2013-03-18 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. A biostatistics professor and WHO advisor, the author illustrates the use of Bayesian hierarchical modeling in the geographical analysis of disease through a range of real-world datasets. New to the Second Edition Three new chapters on regression and ecological analysis, putative hazard modeling, and disease map surveillance Expanded material on case event modeling and spatiotemporal analysis New and updated examples Two new appendices featuring examples of integrated nested Laplace approximation (INLA) and conditional autoregressive (CAR) models In addition to these new topics, the book covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. WinBUGS and R are used throughout for data manipulation and simulation.

Hierarchical Modeling and Analysis for Spatial Data

Author : Sudipto Banerjee
Publisher : CRC Press
ISBN 13 : 020348780X
Total Pages : 470 pages
Book Rating : 4.2/5 (34 download)

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Book Synopsis Hierarchical Modeling and Analysis for Spatial Data by : Sudipto Banerjee

Download or read book Hierarchical Modeling and Analysis for Spatial Data written by Sudipto Banerjee and published by CRC Press. This book was released on 2003-12-17 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,

Spatiotemporal Analysis of Air Pollution and Its Application in Public Health

Author : Lixin Li
Publisher : Elsevier
ISBN 13 : 012816526X
Total Pages : 336 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Spatiotemporal Analysis of Air Pollution and Its Application in Public Health by : Lixin Li

Download or read book Spatiotemporal Analysis of Air Pollution and Its Application in Public Health written by Lixin Li and published by Elsevier. This book was released on 2019-11-13 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatiotemporal Analysis of Air Pollution and Its Application in Public Health reviews, in detail, the tools needed to understand the spatial temporal distribution and trends of air pollution in the atmosphere, including how this information can be tied into the diverse amount of public health data available using accurate GIS techniques. By utilizing GIS to monitor, analyze and visualize air pollution problems, it has proven to not only be the most powerful, accurate and flexible way to understand the atmosphere, but also a great way to understand the impact air pollution has in diverse populations. This book is essential reading for novices and experts in atmospheric science, geography and any allied fields investigating air pollution. Introduces readers to the benefits and uses of geo-spatiotemporal analyses of big data to reveal new and greater understanding of the intersection of air pollution and health Ties in machine learning to improve speed and efficacy of data models Includes developing visualizations, historical data, and real-time air pollution in large geographic areas

Bayesian Disease Mapping

Author : Andrew B. Lawson
Publisher : CRC Press
ISBN 13 : 135127175X
Total Pages : 464 pages
Book Rating : 4.3/5 (512 download)

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Book Synopsis Bayesian Disease Mapping by : Andrew B. Lawson

Download or read book Bayesian Disease Mapping written by Andrew B. Lawson and published by CRC Press. This book was released on 2018-05-20 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. In addition to the new material, the book also covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. The target audience for this text is public health specialists, epidemiologists, and biostatisticians who need to work with geo-referenced health data.

Spatial Cluster Modelling

Author : Andrew B. Lawson
Publisher : CRC Press
ISBN 13 : 142003541X
Total Pages : 305 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Spatial Cluster Modelling by : Andrew B. Lawson

Download or read book Spatial Cluster Modelling written by Andrew B. Lawson and published by CRC Press. This book was released on 2002-05-16 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this b