Statistical Methods for Spatial Data Analysis: Texts in Statistical Science

Understanding spatial statistics requires tools from applied and mathematical statistics,linear model theory,regression,time series,and stochastic processes. 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.

Format: Paperback / softback
Length: 504 pages
Publication date: 21 January 2023
Publisher: Taylor & Francis Ltd

Understanding spatial statistics necessitates the utilization of tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also necessitates a mindset that prioritizes the distinct characteristics of spatial data and the creation of specialized analytical tools tailored explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis fulfills the demand for a text that encompasses all of these elements by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics and practical methods for the analysis of spatial data.

This book serves as a comprehensive and illustrative treatment of fundamental 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 such as measures of autocorrelation and their significance 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 provides a thorough analysis of spatial regression, offering 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 website provides data sets used as examples throughout the text, allowing readers to further explore and apply the discussed techniques.

In summary, Statistical Methods for Spatial Data Analysis is an essential resource for researchers, practitioners, and students interested in spatial statistics and data analysis. It provides a comprehensive and up-to-date treatment of the field, covering theoretical foundations, practical methods, and additional resources, making it a valuable tool for advancing our understanding of spatial phenomena and addressing real-world spatial data challenges.

Weight: 930g
Dimension: 254 x 178 (mm)
ISBN-13: 9781032477916