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Research - Statistical Methods for Spatial Data Analysis: Texts in Statistical Science (Chapman & Hall/CRC Texts in Statistical Science)

Description

Book Synopsis: 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.

Details

Are you looking to enhance your understanding of spatial statistics and master the art of spatial data analysis? Look no further! Statistical Methods for Spatial Data Analysis is the ultimate resource you need. This comprehensive book combines applied and mathematical statistics with linear model theory, regression, time series, and stochastic processes, providing you with a well-rounded knowledge base for tackling spatial data analysis.

What sets this book apart is its emphasis on the unique characteristics of spatial data and the specialized analytical tools that are designed specifically for this type of analysis. Whether you're a beginner or an experienced data analyst, Statistical Methods for Spatial Data Analysis will guide you through the theoretical foundations of spatial statistics and equip you with practical methods to analyze spatial data effectively.

One of the key features of this book is its model-based and frequentist approach that focuses on the spatial domain. You'll learn essential tools and approaches such as measures of autocorrelation, random fields, mapping spatial point patterns, estimation and modeling of the covariance function and semivariogram, spectral domain analysis, spatial prediction, kriging, and much more.

In addition to the comprehensive treatment of spatial analysis, this book also delves into spatial regression, covering linear models with uncorrelated errors, models with spatially-correlated errors, and generalized linear mixed models for spatial data. It even touches on Bayesian hierarchical models, simulating random fields, non-stationary covariance, and spatio-temporal processes.

Ready to dive into the fascinating world of spatial statistics? Grab your copy of Statistical Methods for Spatial Data Analysis now and unlock unlimited possibilities for your data analysis projects. Don't miss out on this invaluable resource that will take your spatial data analysis skills to new heights! Get your copy today!

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