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. 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.

了解空間統計需要應用統計學和數學統計學的工具，線性模型理論，回歸，時間序列和隨機過程。它還需要一種關注空間數據的獨特特徵和專門為空間數據分析而設計的專業分析工具的思維方式。《空間數據分析的統計方法》回應了對一本結合了所有這些因素的教材的需求，通過提供一個平衡的論述，既探索了空間統計領域的理論基礎，又介紹了空間數據分析的實用方法。

本書是一本全面且具有說明性的基礎統計理論和空間數據分析方法的著作，採用基於模型和頻率論的方法，強調空間領域。它介紹了必要的工具和方法，包括：自相關度量及其在數據分析中的作用；支持隨機場的背景和理論框架；對映射的空間點模式的分析；協方差函數和半變異函數的估計和建模；在頻譜域中進行空間分析的全面處理；以及空間預測和克里金法。該書還提供了對空間回歸的詳細分析，包括無相關誤差的線性模型，具有空間相關誤差的線性模型以及用於空間數據的廣義線性混合模型。它簡要討論了貝葉斯階層模型，並以模擬隨機場，非穩態協方差和時空過程的評論作為結尾。

CRC Press網站上的附加資料補充了本書的內容。該網站提供了在文本中用作示例的數據集，可以用來實施許多所描述和演示的主要方法的軟件代碼，以及對本書本身的更新。

Oliver Schabenberger, Carol A. Gotway

Oliver Schabenberger, Carol A. Gotway