New York: Springer, 2006. — 316 p.
Point process statistics is successfully used in fields such as material science, human epidemiology, social sciences, animal epidemiology, biology, and seismology. Its further application depends greatly on good software and instructive case studies that show the way to successful work. This book satisfies this need by a presentation of the spatstat package and many statistical examples.
Researchers, spatial statisticians, and scientists from biology, geosciences, materials sciences, and other fields will use this book as a helpful guide to the application of point process statistics. No other book presents so many well-founded point process case studies.
Adrian Baddeley is a Professor of Statistics at the University of Western Australia (Perth, Australia) and a Fellow of the Australian Academy of Science. His main research interests are in stochastic geometry, stereology, spatial statistics, image analysis and statistical software.
Pablo Gregori is a senior lecturer of Statistics and Probability at the Department of Mathematics, University Jaume I of Castellon. His research fields of interest are spatial statistics, mainly on spatial point processes, and the measure theory of functional analysis.
Jorge Mateu is Assistant Professor of Statistics and Probability at the Department of Mathematics, University Jaume I of Castellon and a Fellow of the Spanish Statistical Society and of Wessex Institute of Great Britain. His main research interests are in stochastic geometry and spatial statistics, mainly spatial point processes and geostatistics.
Radu Stoica obtained his Ph.D. in 2001 from the University of Nice Sophia Anitpolis. He works within the biometry group at INRA Avignon. His research interests are related to the study and the simulation of point processes applied to pattern modeling and recognition. The aimed application domains are image processing, astronomy, and environmental sciences.
Dietrich Stoyan is Professor of Applied Stochastics at TU Bergakademie Freiberg, Germany. Since the end of the 1970s he has worked in the fields of stochastic geometry and spatial statistics.
Fundamentals of Point Process Statistics
Modeling Spatial Point Patterns in R
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
On Modeling 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 region of France: Point-process Aggregation
Strain-typing Transmissible Spongiform Encephalopathies Using Replicated Spatial Data
Modeling 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