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Use R! Series Editors: Robert Gentleman Kurt Hornik Giovanni Parmigiani

Use R! Albert: Bayesian Computation with R Bivand/Pebesma/Gómez-Rubio: Applied Spatial Data Analysis with R Cook/Swayne: Interactive and Dynamic Graphics for Data Analysis: With R and GGobi Hahne/Huber/Gentleman/Falcon: Bioconductor Case Studies Paradis: Analysis of Phylogenetics and Evolution with R Pfaff: Analysis of Integrated and Cointegrated Time Series with R Sarkar: Lattice: Multivariate Data Visualization with R Spector: Data Manipulation with R

Roger S. Bivand • Edzer J. Pebesma Virgilio Gómez-Rubio Applied Spatial Data Analysis with R ABC

Virgilio Gómez-Rubio Department of Epidemiology and Public Health Imperial College London St. Mary’s Campus Norfolk Place London W2 1PG United Kingdom Kurt Hornik Department für Statistik und Mathematik Wirtschaftsuniversität Wien Augasse 2-6 A-1090 Wien Austria Roger S. Bivand Norwegian School of Economics and Business Administration Breiviksveien 40 5045 Bergen Norway Edzer J. Pebesma University of Utrecht Department of Physical Geography 3508 TC Utrecht Netherlands Series Editors: Robert Gentleman Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N, M2-B876 Seattle, Washington 98109-1024 USA Giovanni Parmigiani The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University 550 North Broadway Baltimore, MD 21205-2011 USA ISBN 978-0-387-78170-9 DOI 10.1007/978-0-387-78171-6 e-ISBN 978-0-387-78171-6 Library of Congress Control Number: 2008931196 c 2008 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identiﬁed as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com

Ewie Voor Ellen, Ulla en Mandus A mis padres, Victorina y Virgilio Benigno

Preface We began writing this book in parallel with developing software for handling and analysing spatial data with R (R Development Core Team, 2008). Al- though the book is now complete, software development will continue, in the R community fashion, of rich and satisfying interaction with users around the world, of rapid releases to resolve problems, and of the usual joys and frustra- tions of getting things done. There is little doubt that without pressure from users, the development of R would not have reached its present scale, and the same applies to analysing spatial data analysis with R. It would, however, not be suﬃcient to describe the development of the R project mainly in terms of narrowly deﬁned utility. In addition to being a community project concerned with the development of world-class data analy- sis software implementations, it promotes speciﬁc choices with regard to how data analysis is carried out. R is open source not only because open source software development, including the dynamics of broad and inclusive user and developer communities, is arguably an attractive and successful development model. R is also, or perhaps chieﬂy, open source because the analysis of empirical and simulated data in science should be reproducible. As working researchers, we are all too aware of the possibility of reaching inappropriate conclusions in good faith because of user error or misjudgement. When the results of research really matter, as in public health, in climate change, and in many other ﬁelds involving spatial data, good research practice dictates that some- one else should be, at least in principle, able to check the results. Open source software means that the methods used can, if required, be audited, and jour- nalling working sessions can ensure that we have a record of what we actually 1 – a tool that permits did, not what we thought we did. Further, using Sweave the embedding of R code for complete data analyses in documents – through- out this book has provided crucial support (Leisch, 2002; Leisch and Rossini, 2003). 1 http://www.statistik.lmu.de/~leisch/Sweave/.

VIII Preface We acknowledge our debt to the members of R-core for their continu- ing commitment to the R project. In particular, the leadership and example of Professor Brian Ripley has been important to us, although our admitted ‘muddling through’ contrasts with his peerless attention to detail. His inter- ested support at the Distributed Statistical Computing conference in Vienna in 2003 helped us to see that encouraging spatial data analysis in R was a project worth pursuing. Kurt Hornik’s dedication to keep the Comprehensive R Archive Network running smoothly, providing package maintainers with superb, almost 24/7, service, and his dry humour when we blunder, have meant that the useR community is provided with contributed software in an unequalled fashion. We are also grateful to Martin M¨achler for his help in setting up and hosting the R-Sig-Geo mailing list, without which we would have not had a channel for fostering the R spatial community. We also owe a great debt to users participating in discussions on the mail- ing list, sometimes for speciﬁc suggestions, often for fruitful questions, and occasionally for perceptive bug reports or contributions. Other users contact us directly, again with valuable input that leads both to a better understanding on our part of their research realities and to the improvement of the software involved. Finally, participants at R spatial courses, workshops, and tutorials have been patient and constructive. We are also indebted to colleagues who have contributed to improving the ﬁnal manuscript by commenting on earlier drafts and pointing out better pro- cedures to follow in some examples. In particular, we would like to mention Juanjo Abell´an, Nicky Best, Peter J. Diggle, Paul Hiemstra, Rebeca Ramis, Paulo J. Ribeiro Jr., Barry Rowlingson, and Jon O. Skøien. We are also grate- ful to colleagues for agreeing to our use of their data sets. Support from Luc Anselin has been important over a long period, including a very fruitful CSISS workshop in Santa Barbara in 2002. Work by colleagues, such as the ﬁrst book known to us on using R for spatial data analysis (Kopczewska, 2006), provided further incentives both to simplify the software and complete its description. Without John Kimmel’s patient encouragement, it is unlikely that we would have ﬁnished this book. Even though we have beneﬁtted from the help and advice of so many people, there are bound to be things we have not yet grasped – so remaining mistakes and omissions remain our sole responsibility. We would be grateful for messages pointing out errors in this book; errata will be posted on the book website (http://www.asdar-book.org). Bergen M¨unster London April 2008 Roger S. Bivand Edzer J. Pebesma Virgilio G´omez-Rubio

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