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- Covers the three main types of spatial data: geostatistical data, lattice data, and point patterns in theory and application - Adopts a modeling approach that ties the material into well known areas like linear models and Monte Carlo testing - Requires no previous exposure to measure theory and includes the necessary background material from stochastic process theory Spatial statistics -- one of the fastest growing areas of statistics -- has applications across a wide range of disciplines. Statistical Methods for Spatial Data Analysis takes a model-based approach and covers the theory and...

While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical information systems (GISs). This is the first thorough overview to integrate spatial statistics with data management and the display capabilities of GIS. It describes methods for assessing the likelihood of observed patterns and quantifying the link between exposures and outcomes in spatially...

Statistical Methods for Spatial Data Analysis is a comprehensive treatment of statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. The authors deliver an outstanding treatment of semivariogram estimation and modeling, spatial analysis in the s