Diggle Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition

3. Auflage 2013
ISBN: 978-1-4665-6024-6
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 300 Seiten

Reihe: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

Written by a prominent statistician and author, the first edition of this bestseller broke new ground in the then emerging subject of spatial statistics with its coverage of spatial point patterns. Retaining all the material from the second edition and adding substantial new material, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition presents models and statistical methods for analyzing spatially referenced point process data.

Reflected in the title, this third edition now covers spatio-temporal point patterns. It explores the methodological developments from the last decade along with diverse applications that use spatio-temporally indexed data. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences.

This edition also incorporates the use of R through several packages dedicated to the analysis of spatial point process data. Sample R code and data sets are available on the author’s website.

Autoren/Hrsg.

Diggle, Peter J.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Geowissenschaften Geologie GIS, Geoinformatik

Inhaltsverzeichnis

Introduction
Spatial point patterns
Sampling
Edge-effects
Complete spatial randomness
Objectives of statistical analysis
The Dirichlet tessellation
Monte Carlo tests
Software

Preliminary Testing
Tests of complete spatial randomness
Inter-event distances
Nearest neighbor distances
Point to nearest event distances
Quadrat counts
Scales of pattern
Recommendations

Methods for Sparsely Sampled Patterns
General remarks
Quadrat counts
Distance measurements
Tests of independence
Recommendations

Spatial Point Processes
Processes and summary descriptions
Second-order properties
Higher order moments and nearest neighbor distributions
The homogeneous Poisson process
Independence and random labeling
Estimation of second-order properties
Displaced amacrine cells in the retina of a rabbit
Estimation of nearest neighbor distributions
Concluding remarks

Nonparametric Methods
Estimating weighted integrals of the second-order intensity
Nonparametric estimation of a spatially varying intensity
Analyzing replicated spatial point patterns
Parametric or nonparametric methods?

Models
Contagious distributions
Poisson cluster processes
Inhomogeneous Poisson processes
Cox processes
Trans-Gaussian Cox processes
Simple inhibition processes
Markov point processes
Other constructions
Multivariate models

Model-Fitting Using Summary Descriptions
Parameter estimation using the K-function
Goodness-of-fit assessment using nearest neighbor distributions
Examples
Parameter estimation via goodness-of-fit testing

Model-Fitting Using Likelihood-Based Methods
Likelihood inference for inhomogeneous Poisson processes
Likelihood inference for Markov point processes
Likelihood inference for Cox processes
Additional reading

Point Process Methods in Spatial Epidemiology
Spatial clustering
Spatial variation in risk
Point source models
Stratification and matching
Disentangling heterogeneity and clustering

Spatio-Temporal Point Processes
Motivating examples
A classification of spatio-temporal point patterns and processes
Second-order properties
Conditioning on the past
Empirical and mechanistic models

Exploratory Analysis
Animation
Marginal and conditional summaries
Second-order properties

Empirical Models and Methods
Poisson processes
Cox processes
Log-Gaussian Cox processes
Inference
Gastro-intestinal illness in Hampshire, UK
Concluding remarks: point processes and geostatistics

Mechanistic Models and Methods
Conditional intensity and likelihood
Partial likelihood
The 2001 foot-and-mouth epidemic in Cumbria, UK
Nesting patterns of Arctic terns

References

Über Autor(innen)

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.