Statistical Methods for Spatial Data Analysis

Oliver Schabenberger, Carol A. Gotway

December 20, 2004 by Chapman and Hall/CRC
Textbook - 504 Pages - 81 B/W Illustrations
ISBN 9781584883227 - CAT# C3227
Series: Chapman & Hall/CRC Texts in Statistical Science


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  • Provides a treatment of basic statistical theory and methods for spatial data analysis
  • Offers statistical tools for the analysis of mapped point patterns
  • Includes a comprehensive section on spatial analysis in the spectral domain
  • Gives an up-to-date treatment of semivariogram estimation and modeling
  • Includes a statistical treatment of geostatistical methods for spatial prediction
  • Provides a thorough treatment of the theory and methods for spatial regression analysis including model diagnostics for linear and generalized linear spatial models and spline smoothing with mixed models
  • Reviews Bayesian hierarchical models for spatial data, simulation of random fields, non-stationary covariance and spatio-temporal processes
  • Provides both theoretical and practical problems at the end of the main chapters for use as a graduate-level textbook
  • Summary

    Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data.

    This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression, providing a detailed development of linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields, non-stationary covariance, and spatio-temporal processes.

    Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text, software code that can be used to implement many of the principal methods described and illustrated, and updates to the text itself.