Statistical Methods for Spatial Data Analysis  book cover
1st Edition

Statistical Methods for Spatial Data Analysis

ISBN 9781584883227
Published December 20, 2004 by Chapman and Hall/CRC
506 Pages 81 B/W Illustrations

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Book Description

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.

Table of Contents

The Need for Spatial Analysis
Types of Spatial Data
Autocorrelation-Concept and Elementary Measures
Autocorrelation Functions
The Effects of Autocorrelation on Statistical Inference
Chapter Problems

Stochastic Processes and Samples of Size One
Stationarity, Isotropy, and Heterogeneity
Spatial Continuity and Differentiability
Random Fields in the Spatial Domain
Random Fields in the Frequency Domain
Chapter Problems

Random, Aggregated, and Regular Patterns
Binomial and Poisson Processes
Testing for Complete Spatial Randomness
Second-Order Properties of Point Patterns
The Inhomogeneous Poisson Process
Marked and Multivariate Point Patterns
Point Process Models
Chapter Problems

Semivariogram and Covariogram
Covariance and Semivariogram Models
Estimating the Semivariogram
Parametric Modeling
Nonparametric Estimation and Modeling
Estimation and Inference in the Frequency Domain
On the Use of Non-Euclidean Distances in Geostatistics
Supplement: Bessel Functions
Chapter Problems

Optimal Prediction in Random Fields
Linear Prediction-Simple and Ordinary Kriging
Linear Prediction with a Spatially Varying Mean
Kriging in Practice
Estimating Covariance Parameters
Nonlinear Prediction
Change of Support
On the Popularity of the Multivariate Gaussian Distribution
Chapter Problems

Linear Models with Uncorrelated Errors
Linear Models with Correlated Errors
Generalized Linear Models
Bayesian Hierarchical Models
Chapter Problems

Unconditional Simulation of Gaussian Random Fields
Conditional Simulation of Gaussian Random Fields
Simulated Annealing
Simulating from Convolutions
Simulating Point Processes
Chapter Problems

Types of Non-Stationarity
Global Modeling Approaches
Local Stationarity

A New Dimension
Separable Covariance Functions
Non-Separable Covariance Functions
The Spatio-Temporal Semivariogram
Spatio-Temporal Point Processes

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"…well-presented research-level text with interesting examples and an extensive reference list, much of which relates to work which has appeared during the last five years or so."
International Statistics Institute, 2005

"This book tackles spatial data analysis from a statistician's point of view. It provides a very natural bridge to spatial data analysis for the classically trained statistician who is familiar with linear models and the like. In terms of detail, it is at a very good level for its stated audience of a graduate class in spatial statistics; there is much useful information….The authors have made a tightly written and well-planned contribution that updates much relevant material and provides welcome and thoughtful advice. …I have no hesitation in recommending it for a graduate class in spatial statistics, and it is a welcome addition to my library."
-Journal of the Royal Statistical Society, Series A, Andrew Robinson, University of Melbourne

"This book provides an introduction to statistical methods for the analysis of spatial data. In a coherent manner, it presents statistical tools and approaches for analysis of three types of spatial data: geostatistical data, lattice data, and point patterns. …The book is intended as a text for a graduate-level course in spatial statistics. I believe that it would be a suitable text for a variety of reasons. First of all, the book provides comprehensive coverage of statistical methods for geostatistical data, lattice data, and point patterns. Not many books on spatial statistics have this feature. …The book has a nice balance of statistical theory, methodology, and applications, with an emphasis on statistical methods. It contains many concrete examples that illustrate both theory and methods. In illustrating the methods, real and interesting data examples are drawn from many disciplines such as agriculture, ecology, geology, epidemiology, and meteorology. …This is a wonderful book that systematically introduces readers to spatial statistics. With a writing style that is illustrative, clear, thoughtful, and cogent, teachers and students alike should find it a delightful text for this diverse and exciting field."
-Journal of the American Statistical Association, Jun Zhu, University of Wisconsin-Madison

"I enjoyed this book and I am sure that it is a valuable addition to the literature which should be widely read."

– Stelios Zimeras, University of the Aegean, in Journal of Applied Statistics, Jan 2008, Vol. 35, No. 1