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.