Retaining all the material from the second edition and adding substantial new material, this third edition presents models and statistical methods for analyzing spatially referenced point process data. Reflected in the title, this edition now covers spatio-temporal point patterns. It also incorporates the use of R through several packages dedicated to the analysis of spatial point process data, with code and data sets available online. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences.

Peter Diggle is a Distinguished University Professor and group leader of CHICAS at Lancaster University. Dr. Diggle is also an adjunct professor of biostatistics at both Johns Hopkins University's and Yale University's Schools of Public Health, adjunct senior researcher in the International Research Institute for Climate and Society at Columbia University, professor of epidemiology and statistics at the University of Liverpool, a trustee for Biometrika, founding co-editor and advisory board member for Biostatistics, and chair of the Strategic Skills Fellowships Panel of the Medical Research Council. His research focuses on the development and application of statistical methods to the biomedical and health sciences.

Introduction. Preliminary Testing. Methods for Sparsely Sampled Patterns. Spatial Point Processes. Nonparametric Methods. Models. Model-Fitting Using Summary Descriptions. Model-Fitting Using Likelihood-Based Methods. Point Process Methods in Spatial Epidemiology. Spatio-Temporal Point Processes. Exploratory Analysis. Empirical Models and Methods. Mechanistic Models and Methods. References.