1st Edition

Spatial Point Patterns Methodology and Applications with R

By Adrian Baddeley, Ege Rubak, Rolf Turner Copyright 2016
    828 Pages 408 B/W Illustrations
    by Chapman & Hall

    Modern Statistical Methodology and Software for Analyzing Spatial Point Patterns

    Spatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on their 25 years of software development experiences, methodological research, and broad scientific collaborations to deliver a book that clearly and succinctly explains concepts and addresses real scientific questions.

    Practical Advice on Data Analysis and Guidance on the Validity and Applicability of Methods

    The first part of the book gives an introduction to R software, advice about collecting data, information about handling and manipulating data, and an accessible introduction to the basic concepts of point processes. The second part presents tools for exploratory data analysis, including non-parametric estimation of intensity, correlation, and spacing properties. The third part discusses model-fitting and statistical inference for point patterns. The final part describes point patterns with additional "structure," such as complicated marks, space-time observations, three- and higher-dimensional spaces, replicated observations, and point patterns constrained to a network of lines.

    Easily Analyze Your Own Data

    Throughout the book, the authors use their spatstat package, which is free, open-source code written in the R language. This package provides a wide range of capabilities for spatial point pattern data, from basic data handling to advanced analytic tools. The book focuses on practical needs from the user’s perspective, offering answers to the most frequently asked questions in each chapter.

    Point patterns
    Statistical methodology for point patterns
    About this book

    Software Essentials
    Introduction to RR
    Packages for R
    Introduction to spatstat
    Getting started with spatstat

    Collecting and Handling Point Pattern Data
    Surveys and experiments
    Data handling
    Entering point pattern data into spatstat
    Data errors and quirks
    Windows in spatstat
    Pixel images in spatstat
    Line segment patterns
    Collections of objects
    Interactive data entry in spatstat
    Reading GIS file formats

    Inspecting and Exploring Data
    Manipulating point patterns and windows
    Exploring images
    Using line segment patterns

    Point Process Methods
    Basic definitions
    Complete spatial randomness
    Inhomogeneous Poisson process
    A menagerie of models
    Fundamental issues
    Goals of analysis

    Estimating homogeneous intensity
    Technical definition
    Quadrat counting
    Smoothing estimation of intensity function
    Investigating dependence of intensity on a covariate
    Formal tests of (non-)dependence on a covariate
    Hot spots, clusters, and local features
    Kernel smoothing of marks

    Manual methods
    The K-function
    Edge corrections for the K-function
    Function objects in spatstat
    The pair correlation function
    Standard errors and confidence intervals
    Testing whether a pattern is completely random
    Detecting anisotropy
    Adjusting for inhomogeneity
    Local indicators of spatial association
    Third- and higher-order summary statistics

    Basic methods
    Nearest-neighbour function G and empty-space function F
    Confidence intervals and simulation envelopes
    Empty-space hazard
    Inhomogeneous F-, G- and J-functions
    Anisotropy and the nearest-neighbour orientation
    Empty-space distance for a spatial pattern
    Distance from a point pattern to another spatial pattern
    Theory for edge corrections
    Palm distribution

    Poisson Models
    Poisson point process models
    Fitting Poisson models in spatstat
    Statistical inference for Poisson models
    Alternative fitting methods
    More flexible models
    Coarse quadrature approximation
    Fine pixel approximation
    Conditional logistic regression
    Approximate Bayesian inference
    Non-loglinear models
    Local likelihood

    Hypothesis Tests and Simulation Envelopes
    Concepts and terminology
    Testing for a covariate effect in a parametric model
    Quadrat counting tests
    Tests based on the cumulative distribution function
    Monte Carlo tests
    Monte Carlo tests based on summary functions
    Envelopes in spatstat
    Other presentations of envelope tests
    Dao-Genton test and envelopes
    Power of tests based on summary functions

    Model Validation
    Overview of validation techniques
    Relative intensity
    Residuals for Poisson processes
    Partial residual plots
    Added variable plots
    Validating the independence assumption
    Leverage and influence
    Theory for leverage and influence

    Cluster and Cox Models
    Cox processes
    Cluster processes
    Fitting Cox and cluster models to data
    Locally fitted models

    Gibbs Models
    Conditional intensity
    Key concepts
    Statistical insights
    Fitting Gibbs models to data
    Pairwise interaction models
    Higher-order interactions
    Hybrids of Gibbs models
    Goodness-of-fit and validation for fitted Gibbs models
    Locally fitted models
    Theory: Gibbs processes
    Theory: Fitting Gibbs models
    Determinantal point processes

    Patterns of Several Types of Points
    Methodological issues
    Handling multitype point pattern data
    Exploratory analysis of intensity
    Multitype Poisson models
    Correlation and spacing
    Tests of randomness and independence
    Multitype Gibbs models
    Hierarchical interactions
    Multitype Cox and cluster processes
    Other multitype processes

    Higher-Dimensional Spaces and Marks
    Point patterns with numerical or multidimensional marks
    Three-dimensional point patterns
    Point patterns with any kinds of marks and coordinates

    Replicated Point Patterns and Designed Experiments
    Lists of objects
    Computing with hyperframes
    Replicated point pattern datasets in spatstat
    Exploratory data analysis
    Analysing summary functions from replicated patterns
    Poisson models
    Gibbs models
    Model validation

    Point Patterns on a Linear Network
    Network geometry
    Data handling
    Poisson models
    Intensity on a tree
    Pair correlation function


    Adrian Baddeley is a professor of computational statistics at Curtin University and a fellow of the Australian Academy of Science. He has been a leading researcher in spatial statistics for 40 years.

    Ege Rubak is an associate professor in the world-renowned spatial statistics group at Aalborg University. His research focuses on spatial statistics and statistical computing.

    Rolf Turner is retired and an Honorary Research Fellow at the University of Auckland, where he has taught a graduate course on spatial point processes in the Department of Statistics. He has considerable expertise in statistical computing and has worked as a statistician in the Division of Mathematics and Statistics at CSIRO, the University of New Brunswick, and the Starpath Project at the University of Auckland.

    "… A very broad range of topics is covered over 810 pages, using examples from different fields of science, most notably astronomy, biology, ecology, geology, and environmental sciences. In reading the book, one of the most enjoyable features is the critical attitude encouraged by the authors, who always question the suitability of specific statistical methods in relation to a given scientific question. The reader is guided from the first to the last step of a statistical analysis of SPP data with useful advice on modeling strategies and illustration of open-source statistical software. The style is highly accessible to a nonstatistical audience, with mathematical formalism kept to a minimum. … What sets this book apart from others in its field are the strong link that the authors build between statistical methodology and scientific problems drawn from multidisciplinary case studies, the coverage of a wide range of topics, and its reference to highquality open-source statistical software. For these reasons, the book is likely to become a classic in SPP data analysis."
    —Emanuele Giorgi, Lancester University, in The American Statistician, July 2017

    "The entire publication offers a wealth of information and will serve as an excellent manual and guide for the work of the point process statistician. One of the many strengths of the book is that it consistently considers point process statistics as a part of statistics in general and always to refer to general statistical ideas. The text is very accessible…There are a lot of interesting examples, which can be reproduced by the reader in R. The reader will appreciate the frequent discussions of caveats and the well-selected and well-answered FAQ’s (frequently asked questions) at the end of each chapter…Overall, this publication presents an excellent introduction to and manual for the spatstat package, for which the community of spatial statisticians will be very grateful to the authors. For readers who use this software, it is an indispensable manual that the reviewer strongly recommends…The reviewer is sure that it will initiate a big step forward in the use of statistical methods for point patterns."
    —Dietrich Stoyan, TU Bergakademie Freiberg, Biometrical Journal, January 2017

    "In a nutshell, this book covers a large portion of the methods for the analysis of spatial point patterns and their implementation in the spatstat package… As spatstat has evolved with help from its users and the community, a list of frequently asked questions (FAQ) is included at the end of most chapters. This will help to clarify some of the contents and guide the user in the data analysis by pointing at different important points to consider. The book is also full of tips, clarifications and discussions on how to conduct the analysis, which clearly will benefit practitioners. It presents and discusses many applications from different fields, so that it will be of interest to a wide range of researchers…I really enjoyed reading this book and it has changed my views on spatstat. In addition to a package for the analysis of point patterns, I now regard this package as a toolbox that will allow the development of further methods and software for the analysis of point patterns, as the package provides a number of functions to rely on when developing new methods."
    —Virgilio Gómez-Rubio, Universidad de Castilla-La Mancha, Journal of Statistical Software, December 2016

    "Several books on analysing point pattern processes have been published in recent years; this is by far the largest, at least in part due to the inclusion of example scripts and output. Its central tool is the spatstat package in R. Chapters cover spatial point pattern statistics from first principles through to some of the more sophisticated techniques. Its audience is scientists looking to employ and interpret these tools, and while technical sections are included, they expand on the applied material rather than being core. This will prove a valuable reference and its guidance will improve standards in the field."
    —Markus Eichhorn, Frontiers of Biogeography, 2016, Volume 8, Issue 3

    "As the authors point out in their preface, the book is not intended to be an introduction to point process theory for mathematicians. Rather, they aim to focus on the principles of statistical inference for spatial data and to help researchers in application domains with the practicalities of the analysis and the interpretation of the results. In this, they have succeeded brilliantly...The book is written in a distinct, at times funny, always accessible style. General principles of every aspect of spatial point pattern analysis, from data collection to model validation, are discussed in great detail with pointers to the specialized literature for those who wish to gain a deeper understanding of the technicalities. The principles are illustrated by means of a wide collection of examples that can be reproduced by the reader in R. Moreover, a selection of frequently asked questions from spatstat users is answered at the end of each chapter...In summary, I warmly recommend the book to anyone who wishes to analyze point patterns professionally."
    —Marie-Colette van Lieshout, reviewed in Biometrics, June 2016

    "Baddeley, Rubak, and Turner have written a uniquely comprehensive account of modern statistical methods for the analysis of spatial point pattern data, aimed firmly at users and, crucially, made accessible to users by explicit linkage of the methods to their own excellent R package, spatstat. Essential reading for anyone who needs to analyze spatial point pattern data properly or to teach others how to do so."
    Peter J. Diggle, Distinguished University Professor, CHICAS, Lancaster University Medical School, UK

    "Baddeley, Rubak, and Turner’s book on spatial point patterns is part of a revolution in statistics, and the reader is buoyantly swept along with it. From data handling, to exploratory data analysis, to advanced analytic tools, we are treated to the best in data science, where open-source software in the R language is used to integrate science and data through statistical thinking. This is an excellent book, founded on methodology derived from statistical models of spatial point patterns, but focusing on the practical needs of the applied scientist."
    Noel Cressie, Distinguished Professor, National Institute for Applied Statistics Research Australia, University of Wollongong

    "Spatial Point Patterns: Methodology and Applications with R is a rich statistical feast. It is by turns humorous, serious, occasionally rather direct, but never talks down to the reader, who is taken as having a well-motivated interest in spatial point patterns. I would argue that applied statisticians not yet conscious of such an interest will also relish the book’s stated intention of bringing its topical treatments back into mainstream statistical practice. Being able to try everything out in R, largely using the spatstat package is a clear advantage; this is coupled with numerous relevant example data sets. While cherry picking is possible—the index is more than adequate—all readers are advised to read at least whole chapters, best complete parts of the book, because the information to be found there is part of a tightly woven fabric. Much can be re-read several times with both profit and pleasure by statisticians and non-statistician practitioners. Sustaining this level of attention to detail through a long book is a splendid achievement."
    Roger Bivand, Professor of Geography, Norwegian School of Economics, and Author and Maintainer of Packages for Spatial Data Analysis, R Project

    "The analysis of spatial point patterns and processes is an exploding field of applied research across many science and social science disciplines. This is thanks in no small part to the development of open-licensed, well-documented, methodologically sophisticated software implementations. For at least a decade, the authors of this book have been at the forefront of a statistical programming revolution. However, with wider academic access to these point pattern-and-process methods, there has also come a pressing need for clearer guidance on good practice for applied researchers at all stages from graduate studies onward. Expressed in an elegant and accessible style, with substantial references for those wanting directions into the more specialist literature, as well as an excellent set of reproducible, multi-disciplinary case studies, this book provides exactly what is needed. It is highly likely to become a classic."
    Andrew Bevan, Institute of Archaeology, University College London