Spatial clustering is a topic of growing interest in many disciplines, from geoscience to epidemiology. In recent years, many new advances have appeared, particularly in the area of Bayesian cluster modeling. However, much of this work has been scattered in disparate areas of the literature. In one integrated volume, this book provides a review of the state-of-the-art in spatial clustering and spatial cluster modelling. Organized in three sections, it includes an overview of the field and its applications, discussions of the theoretical underpinnings of methodology, and explorations of spatio-temporal cluster modelling, all written by leading researchers in the field.

Spatial Cluster Modelling: An Overview. POINT PROCESS CLUSTER MODELLING: Significance in Scale-Space for Clustering. Statistical Inference for Cox Processes. Extrapolating and Interpolating Spatial Patterns. Perfect Sampling for Point Process Cluster Modelling. Bayesian Estimation and Segmentation of Spatial Point Processes Using Voronoi Tilings. SPATIAL PROCESS CLUSTER MODELLING: Partition Modelling. Cluster Modelling for Disease Rate Mapping. Analyzing Spatial Data Using Skew-Gaussian Processes. Accounting for Absorption Lines In Images Obtained With The Chandra X-Ray Observatory. Spatial Modelling of Count Data: A Case Study in Modelling Breeding Bird Survey Data on Large Spatial Domains. SPATIO-TEMPORAL CLUSTER MODELLING: Modelling Strategies for Spatial-Temporal Data. Spatio-Temporal Partition Modelling: An Example FROM Neurophysiology. Spatio-Temporal Cluster Modelling of Small Area Health Data. References. Index. Author Index.

Andrew B. Lawson, David G.T. Denison