The book "Statistical Methods for Spatial Data Analysis" by Oliver Schabenberger and Carol A. Gotway is a comprehensive guide to the analysis of spatial data, covering both theoretical foundations and practical applications. The authors aim to provide a model-based and frequentist approach to spatial statistics, focusing on the spatial domain rather than the spectral domain. The book is structured into several chapters, each addressing specific aspects of spatial data analysis: 1. **Introduction**: Discusses the need for spatial analysis, the unique characteristics of spatial data, and the importance of autocorrelation in spatial data. 2. **Theory on Random Fields**: Provides the background and theoretical framework necessary for understanding spatial data, including stochastic processes, stationarity, isotropy, and spatial continuity. 3. **Mapped Point Patterns**: Focuses on the analysis of point patterns, including random, aggregated, and regular patterns, and the estimation of second-order properties. 4. **Semivariogram and Covariance Function Analysis and Estimation**: Introduces the semivariogram and covariance function, their estimation, and modeling, with a focus on geostatistical methods. 5. **Spatial Prediction and Kriging**: Explains optimal prediction in random fields, including simple and ordinary Kriging, and the estimation of covariance parameters. 6. **Spatial Regression Models**: Covers linear and generalized linear models, including spatial regression, and Bayesian hierarchical models. 7. **Simulation of Random Fields**: Discusses unconditional and conditional simulation of Gaussian random fields, as well as the simulation of point processes. 8. **Non-Stationary Covariance**: Reviews the concept of non-stationarity in spatial data and methods for modeling it. 9. **Spatio-Temporal Processes**: Explores the extension of spatial models to include temporal components. The book is intended for graduate-level courses in spatial statistics and is supported by additional material available on the CRC Press website, including data sets, software code, and updates. The authors emphasize the importance of maintaining the spatial context in the analysis of georeferenced data and provide numerous examples to illustrate the concepts and methods discussed.The book "Statistical Methods for Spatial Data Analysis" by Oliver Schabenberger and Carol A. Gotway is a comprehensive guide to the analysis of spatial data, covering both theoretical foundations and practical applications. The authors aim to provide a model-based and frequentist approach to spatial statistics, focusing on the spatial domain rather than the spectral domain. The book is structured into several chapters, each addressing specific aspects of spatial data analysis: 1. **Introduction**: Discusses the need for spatial analysis, the unique characteristics of spatial data, and the importance of autocorrelation in spatial data. 2. **Theory on Random Fields**: Provides the background and theoretical framework necessary for understanding spatial data, including stochastic processes, stationarity, isotropy, and spatial continuity. 3. **Mapped Point Patterns**: Focuses on the analysis of point patterns, including random, aggregated, and regular patterns, and the estimation of second-order properties. 4. **Semivariogram and Covariance Function Analysis and Estimation**: Introduces the semivariogram and covariance function, their estimation, and modeling, with a focus on geostatistical methods. 5. **Spatial Prediction and Kriging**: Explains optimal prediction in random fields, including simple and ordinary Kriging, and the estimation of covariance parameters. 6. **Spatial Regression Models**: Covers linear and generalized linear models, including spatial regression, and Bayesian hierarchical models. 7. **Simulation of Random Fields**: Discusses unconditional and conditional simulation of Gaussian random fields, as well as the simulation of point processes. 8. **Non-Stationary Covariance**: Reviews the concept of non-stationarity in spatial data and methods for modeling it. 9. **Spatio-Temporal Processes**: Explores the extension of spatial models to include temporal components. The book is intended for graduate-level courses in spatial statistics and is supported by additional material available on the CRC Press website, including data sets, software code, and updates. The authors emphasize the importance of maintaining the spatial context in the analysis of georeferenced data and provide numerous examples to illustrate the concepts and methods discussed.