Statistical Analysis of Spatial and Spatio-Temporal Point Patterns: 3rd Edition (Hardback) book cover

Statistical Analysis of Spatial and Spatio-Temporal Point Patterns

3rd Edition

By Peter J. Diggle

Chapman and Hall/CRC

268 pages | 117 B/W Illus.

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pub: 2013-07-23
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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.


"… a valuable addition to the existing literature as it covers a number of topics in point pattern analysis, ranging from the basics of spatial point pattern statistical analysis to more recent developments in the spatio-temporal context. A number of examples are discussed throughout the chapters, which should facilitate the reading for practitioners of applied statistics from various disciplines. Besides this, the fact that data sets and R codes are available online certainly constitutes a nice addition to this application-oriented textbook."

Mathematical Reviews, January 2015

"… well written, concise, and handy. … there are remarkable changes in fundamentals [in this edition]. … All concepts are well illustrated using interesting examples. … an excellent introduction to point process statistics, in particular for beginners."

Biometrical Journal, 2014

Table of Contents


Spatial point patterns



Complete spatial randomness

Objectives of statistical analysis

The Dirichlet tessellation

Monte Carlo tests


Preliminary Testing

Tests of complete spatial randomness

Inter-event distances

Nearest neighbor distances

Point to nearest event distances

Quadrat counts

Scales of pattern


Methods for Sparsely Sampled Patterns

General remarks

Quadrat counts

Distance measurements

Tests of independence


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?


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


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


Marginal and conditional summaries

Second-order properties

Empirical Models and Methods

Poisson processes

Cox processes

Log-Gaussian Cox processes


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


About the Author

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.

About the Series

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems