Case Studies in Spatial Point Process Modeling

Spatial point processes are complex stochastic models that describe the locations of "interesting" events, and (possibly) some information about each event. This book is focused on case studies, rather than methodological aspects of point process modelling.

Focuses on case studies, rather than methodological aspects of point process modelling Includes supplementary material: sn.pub/extras

Inhalt
Basic Notions and Manipulation of Spatial Point Processes.- Fundamentals of Point Process Statistics.- Modelling Spatial Point Patterns in R.- Theoretical and Methodological Advances in Spatial Point Processes.- Strong Markov Property of Poisson Processes and Slivnyak Formula.- Bayesian Analysis of Markov Point Processes.- Statistics for Locally Scaled Point Processes.- Nonparametric Testing of Distribution Functions in Germ-grain Models.- Principal Component Analysis for Spatial Point Processes Assessing the Appropriateness of the Approach in an Ecological Context.- Practical Applications of Spatial Point Processes.- On Modelling of Refractory Castables by Marked Gibbs and Gibbsian-like Processes.- Source Detection in an Outbreak of Legionnaire's Disease.- Doctors' Prescribing Patterns in the Midi-Pyrénées rRegion of France: Point-process Aggregation.- Strain-typing Transmissible Spongiform Encephalopathies Using Replicated Spatial Data.- Modelling the Bivariate Spatial Distribution of Amacrine Cells.- Analysis of Spatial Point Patterns in Microscopic and Macroscopic Biological Image Data.- Spatial Marked Point Patterns for Herd Dispersion in a Savanna Wildlife Herbivore Community in Kenya.- Diagnostic Analysis of Space-Time Branching Processes for Earthquakes.- Assessing Spatial Point Process Models Using Weighted K-functions: Analysis of California Earthquakes.