Hierarchical Modeling and Analysis for Spatial Data

Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand, Sudipto Banerjee

December 17, 2003 by Chapman and Hall/CRC
Reference - 474 Pages - 13 Color & 48 B/W Illustrations
ISBN 9781584884101 - CAT# C410X
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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  • Presents an up-to-date, fully Bayesian treatment of modeling for spatial and spatio-temporal data sets
  • Focuses on more practical matters--modeling, computing, applications-- rather than formal mathematics
  • Provides overviews of spatial data and the hierarchical Bayesian approach that make strong previous exposure to Bayesian methods unnecessary
  • Contains exercises in each chapter, with related computer code and data on a supporting Web page
  • Includes brief tutorials on using S+SpatialStats, geoR, and WinBUGS software for Bayesian modeling
  • Summary

    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.