The first accessible, self-contained treatment of hierarchial methods, modeling, and data analysis for spatial and spatiotemporal data. Starting with overviews of the types of spatial data, the data analysis tools appropriate for each, and a brief review of the Bayesian approach to statistics, the authors discuss hierarchical modeling for univariate spatial response data, including Bayesian kriging and lattice (areal data) modeling. They then consider the problem of spatially misaligned data, methods for handling multivariate spatial responses, spatiotemporal models, and spatial survival models. The final chapter explores a variety of special topics, including spatially varying coefficient models.

Overview of spatial data problems -- Basics of point-referenced data models -- Basics of areal data models -- Basics of Bayesian inference -- Hierarchical modeling for univariate spatial data -- Spatial misalignment -- Multivariate spatial modeling -- Spatiotemporal modeling -- Spatial survival models -- Special topics in spatial process modeling.