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Spatial Point Patterns: Methodology and Applications with R

  • Focuses on the statistical principles of analyzing spatial data, the practical details of spatial data analysis, and the scientific interpretation of the results
  • Gives technical details at the end of each chapter when necessary
  • Uses the R package spatstat to process and analyze spatial point pattern data

Series: Chapman & Hall/CRC Interdisciplinary Statistics

By: Adrian Baddeley(Author), Ege Rubak(Author), Rolf Turner(Author)

810 pages, 408 b/w illustrations, 63 tables

Apple Academic Press

Hardback | Dec 2015 | #229608 | ISBN-13: 9781482210200
Availability: Usually dispatched within 6 days Details
NHBS Price: £72.99 $93/€79 approx

About this book

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.

The first part of Spatial Point Patterns 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.

Throughout Spatial Point Patterns, 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.

"[...] 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


- 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
- Plotting
- Manipulating point patterns and windows
- Exploring images
- Using line segment patterns
- Tessellations

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

- Introduction
- 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

- Introduction
- 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
- Theory

- Introduction
- Basic methods
- Nearest-neighbour function G and empty-space function F
- Confidence intervals and simulation envelopes
- Empty-space hazard
- J-function
- 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
- Introduction
- Poisson point process models
- Fitting Poisson models in spatstat
- Statistical inference for Poisson models
- Alternative fitting methods
- More flexible models
- Theory
- Coarse quadrature approximation
- Fine pixel approximation
- Conditional logistic regression
- Approximate Bayesian inference
- Non-loglinear models
- Local likelihood

Hypothesis Tests and Simulation Envelopes
- Introduction
- 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
- Introduction
- Cox processes
- Cluster processes
- Fitting Cox and cluster models to data
- Locally fitted models
- Theory

Gibbs Models
- Introduction
- Conditional intensity
- Key concepts
- Statistical insights
- Fitting Gibbs models to data
- Pairwise interaction models
- Higher-order interactions
- Hybrids of Gibbs models
- Simulation
- 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
- Introduction
- 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
- Theory

Higher-Dimensional Spaces and Marks
- Introduction
- 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
- Introduction
- Methodology
- Lists of objects
- Hyperframes
- Computing with hyperframes
- Replicated point pattern datasets in spatstat
- Exploratory data analysis
- Analysing summary functions from replicated patterns
- Poisson models
- Gibbs models
- Model validation
- Theory

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

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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.

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