Spatial Statistics and Modeling

Key spatial models for three types of data are discussed in this overview of spatial modeling and statistics. Real-world applications illustrate the concepts and theories described, in addition to probabilistic properties and related statistical methods.

Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environmental and earth sciences, epidemiology, image analysis and more. This book covers the best-known spatial models for three types of spatial data: geostatistical data (stationarity, intrinsic models, variograms, spatial regression and space-time models), areal data (Gibbs-Markov fields and spatial auto-regression) and point pattern data (Poisson, Cox, Gibbs and Markov point processes). The level is relatively advanced, and the presentation concise but complete.

The most important statistical methods and their asymptotic properties are described, including estimation in geostatistics, autocorrelation and second-order statistics, maximum likelihood methods, approximate inference using the pseudo-likelihood or Monte-Carlo simulations, statistics for point processes and Bayesian hierarchical models. A chapter is devoted to Markov Chain Monte Carlo simulation (Gibbs sampler, Metropolis-Hastings algorithms and exact simulation).
A large number of real examples are studied with R, and each chapter ends with a set of theoretical and applied exercises. While a foundation in probability and mathematical statistics is assumed, three appendices introduce some necessary background. The book is accessible to senior undergraduate students with a solid math background and Ph.D. students in statistics. Furthermore, experienced statisticians and researchers in the above-mentioned fields will find the book valuable as a mathematically sound reference.

This book is the English translation of Modélisation et Statistique Spatiales published by Springer in the series Mathématiques & Applications, a series established by Société de Mathématiques Appliquées et Industrielles (SMAI).

Clear and precise presentation of the most important spatial models, including their probabilistic properties and related statistical methods Implements these models and studies their statistics on a wide variety of real spatial data coming from real-world applications Each chapter has numerous exercises to test the reader, and R scripts are provided (see the web site of the book) to help students and researchers deepen their understanding of the subject Includes supplementary material: sn.pub/extras

Contenu
Second-order spatial models and geostatistics.- Gibbs-Markov random fields on networks.- Spatial point processes.- Simulation of spatial models.- Statistics for spatial models.