Hierarchical Modeling and Analysis for Spatial Data

Hierarchical Modeling and Analysis for Spatial Data

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Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics. Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and data analysis for spatial and spatio-temporal data. Starting with overviews of the types of spatial data, the data analysis tools appropriate for each, and a brief review of the Bayesian approach to statistics, the authors discuss hierarchical modeling for univariate spatial response data, including Bayesian kriging and lattice (areal data) modeling. They then consider the problem of spatially misaligned data, methods for handling multivariate spatial responses, spatio-temporal models, and spatial survival models. The final chapter explores a variety of special topics, including spatially varying coefficient models. This book provides clear explanations, plentiful illustrations --some in full color--a variety of homework problems, and tutorials and worked examples using some of the field's most popular software packages. Written by a team of leaders in the field, it will undoubtedly remain the primary textbook and reference on the subject for years to come.show more

Product details

  • Hardback | 474 pages
  • 156 x 236 x 30mm | 821g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 48 black & white illustrations, 13 colour illustrations, 48 black & white tables
  • 158488410X
  • 9781584884101
  • 2,385,624

Review quote

"This book was a pleasure to review. Most of the emphasis is on insight and intuition with relatively little on traditional multivariate techniques. I also found some of the explanations delightful[W]hile they did not convert me to Bayesianism, [the authors] made me reconsider some of my assumptions. They later state 'Our book is intended as a research monograph, presenting the state of the art' and my impression is that they have succeededIn many sections the formulae are augmented by showing R or S code, making it easy to actually apply the mathematics. In summary, this is a nice book." -Short Book Reviews of the International Statistical Institute "The book contains a wealth of material not available elsewhere in a unified manner. Each chapter contains worked out examples using some well known software packages and has exercises with related computer code and data on a supporting web page. The book is up to date in its coveragean important addition to the literature on spatial data analysis." -Zentralblatt MATH 1053show more

Table of contents

OVERVIEW OF SPATIAL DATA PROBLEMS Introduction to Spatial Data and Models Fundamentals of Cartography Exercises BASICS OF POINT-REFERENCED DATA MODELS Elements of Point-Referenced Modeling Spatial Process Models Exploratory Approaches for Point-Referenced Data Classical Spatial Prediction Computer Tutorials Exercises BASICS OF AREAL DATA MODELS Exploratory Approaches for Areal Data Brook's Lemma and Markov Random Fields Conditionally Autoregressive (CAR) Models Simultaneous Autoregressive (SAR) Models Computer Tutorials Exercises BASICS OF BAYESIAN INFERENCE Introduction to Hierarchical Modeling and Bayes Theorem Bayesian Inference Bayesian Computation Computer Tutorials Exercises HIERARCHICAL MODELING FOR UNIVARIATE SPATIAL DATA Stationary Spatial Process Models Generalized Linear Spatial Process Modeling Nonstationary Spatial Process Models Areal Data Models General Linear Areal Data Modeling Exercises SPATIAL MISALIGNMENT Point-Level Modeling Nested Block-Level Modeling Nonnested Block-Level Modeling Misaligned Regression Modeling Exercises MULTIVARIATE SPATIAL MODELING Separable Models Coregionalization Models Other Constructive Approaches Multivariate Models for Areal Data Exercises SPATIOTEMPORAL MODELING General Modeling Formulation Point-Level Modeling with Continuous Time Nonseparable Spatio-Temporal Models Dynamic Spatio-Temporal Models Block-Level Modeling Exercises SPATIAL SURVIVAL MODELS Parametric Models Semiparametric Models Spatio-Temporal Models Multivariate Models Spatial Cure Rate Models Exercises SPECIAL TOPICS IN SPATIAL PROCESS MODELING Process Smoothness Revisited Spatially Varying Coefficient Models Spatial CDFs APPENDICES Matrix Theory and Spatial Computing Methods Answers to Selected Exercises REFERENCES AUTHOR INDEX SUBJECT INDEX Short TOCshow more

Review Text

A practical treatment of Bayesian methods, modeling, and data analysis focused specifically on spatial and spatio-temporal data, this text covers state-of-the-art methods for hierarchical modeling of spatial data sets, but for the uninitiated, includes gentle overviews of both spatial data analysis methods and Bayesian methodology and computing.show more

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