Applied Spatial Data Analysis with R

1. Front Matter

   Pages I-XIV

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2. Handling Spatial Data in R

   1. Front Matter

      Pages 17-19

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   2. Hello World: Introducing Spatial Data

      Pages 1-15

   3. Classes for Spatial Data in R

      Pages 21-55

   4. Visualising Spatial Data

      Pages 57-80

   5. Spatial Data Import and Export

      Pages 81-111

   6. Further Methods for Handling Spatial Data

      Pages 113-126

   7. Customising Spatial Data Classes and Methods

      Pages 127-148

3. Analysing Spatial Data

   1. Front Matter

      Pages 149-153

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   2. Spatial Point Pattern Analysis

      Pages 155-190

   3. Interpolation and Geostatistics

      Pages 191-235

   4. Areal Data and Spatial Autocorrelation

      Pages 237-272

   5. Modelling Areal Data

      Pages 273-309

   6. Disease Mapping

      Pages 311-341

4. Back Matter

   Pages 343-375

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We began writing this book in parallel with developing software for handling and analysing spatial data withR (R Development Core Team, 2008). - 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 frust- tions of getting things done. There is little doubt that without pressure from users, the development ofR would not have reached its present scale, and the same applies to analysing spatial data analysis withR. It would, however, not be su?cient to describe the development of the R project mainly in terms of narrowly de?ned utility. In addition to being a communityprojectconcernedwiththedevelopmentofworld-classdataana- sis software implementations, it promotes speci?c 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.

• Map
• calculus
• correlation
• data analysis
• disease mapping
• ecology
• environment
• geographical information systems
• geostatistics
• modeling
• sets
• spatial data analysis
• spatial statistics
• statistics

From the reviews:

"The first half of this book certainly convinced me that some extra effort in organizing my data into certain spatial class structures makes the analysis easier and less subject to mistakes. I also admit that I found it very interesting and I learned a lot. Several years ago, I struggled on a project that required managing various spatial data with different projections and support using the rgdal package in R; I really wish I had this book at that time!... In summary, this is an excellent book that should be on the shelf of any applied statistician who is analyzing spatial data using R.…, [I]t would be a valuable companion to any course that uses spatial packages in R." (Jay M. Ver Hoef, Biometrics, June 2009, 65)

"Applied Spatial Data Analysis with R is an accessible text that demonstrates and explains the handling of spatial data using the R software platform. The authors have all been key contributors to the R spatial data analysis community, and the range of their contributions is evident from the comprehensive coverage of this work. It will appeal to those familiar with R but not spatial data, and vice versa, as well as those proficient in both and in search of a reference text.… In short, this book is highly recommended to statisticians and geographers interested in plotting and analyzing spatial data." (James Cheshire, Significance September 2009)

“I would highly recommend instructors, students, and researchers to have this text on their bookshelf. This book is an enrichment for the community of people conducting research in spatial statistics. It provides tools for simple spatial problems as well as for cutting edge research problems. The book is priced reasonably relative to its content. Overall, it is an excellent reference book.” (The American Statistician, May 2010, Vol. 64, No. 2)

“This book constitutes a complete and accessible manual dedicated to the use of R forhandling spatial data. … the book will appeal equally to beginners and to experts in the field of spatial data analysis. … As a summary, I strongly recommend this book to any person who needs to study the behaviour of one or several geo-referenced variables. … It certainly gives a good and quite exhaustive overview to the different techniques available in order to characterize spatial data.” (Didier Renard, Mathematical Geosciences, Vol. 43, 2011)

• Norwegian School of Economics and Business Administration, Bergen, Norway

  Roger S. Bivand

• Department of Physical Geography, University of Utrecht, TC Utrecht, Netherlands

  Edzer J. Pebesma

• Department of Epidemiology and Public Health, Imperial College London, London, UK

  Virgilio Gómez-Rubio

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