Applied Spatial Data Analysis with R (Use R!) 2008th Edition

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ISBN-13: 978-0387781709
ISBN-10: 0387781706
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Editorial Reviews


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 for handling 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)

From the Back Cover

Applied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website.

This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science.

The book has a website where coloured figures, complete code examples, data sets, and other support material may be found:

The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.

Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshøyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfälische Wilhelms-Universität, Münster, Germany. Virgilio Gómez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom.


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Product Details

  • Series: Use R!
  • Paperback: 376 pages
  • Publisher: Springer; 2008 edition (September 1, 2008)
  • Language: English
  • ISBN-10: 0387781706
  • ISBN-13: 978-0387781709
  • Product Dimensions: 6.1 x 0.9 x 9.2 inches
  • Shipping Weight: 1 pounds
  • Average Customer Review: 4.7 out of 5 stars  See all reviews (9 customer reviews)
  • Amazon Best Sellers Rank: #421,689 in Books (See Top 100 in Books)

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23 of 23 people found the following review helpful By E. A. Eff on January 4, 2009
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This is not a book for a beginner, but is an excellent book for two groups of readers: those who have some background with R and wish to learn about its capabilities for spatial statistics; and those with some background in spatial statistics who wish to learn how to use R. The authors have been the main developers of the spatial statistics packages on R, and therefore know the packages intimately. But the authors also have a deep knowledge of the spatial statistics literature, and I found myself learning something new about these methods in every chapter. Everyone serious about spatial statistics should see what R has to offer. This book is the easiest way to do that.
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16 of 17 people found the following review helpful By GeoVizer on December 19, 2008
Format: Paperback
The R spatial packages are the leading edge for spatial analysis and spatial statistics. This book, by the primary developers of the R Spatial packages, is the best introduction to the subject that I have seen.

Now, if you are comfortable with it, you can dive an and download R and R spatial and go to town. But if you need some help, this is a good place to start. This would also make a good textbook for a class on spatial analysis.
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5 of 5 people found the following review helpful By D. Lamb on February 5, 2010
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This book fills a gap in the Spatial Statistics literature. Most of the treatises are heavy on the math, and I find it difficult to bridge the gap between the formulas and applying them. The modules for R take care of this for you and leave you to interpret the result. This book covers most of these modules and demonstrates how to use them. Really worth getting. There is also the ESRI Guide to GIS Analysis Vol 2, but it is more of an introductory text.
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4 of 4 people found the following review helpful By JFB on May 15, 2010
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I was really excited when I ordered this book as it looked like the type of material I had been looking for for ages, but as it turns out I am mildly disappointed in it, primarily because I found the text somewhat hard to grasp and the code not particularly well explained. It is still a very good read though and certainly helped me enhance my knowledge of statistical analyses in R. I would definitely recommend it to anyone looking to examine spatial data in R, but there is a bit of homework to do to be able to understand how all the pieces fit together.
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Format: Paperback
This book cannot stand alone as a sole text on spatial stats. It is best as a quick-start guide to R spatial models for those who already know spatial statistics, as a supplement to a course on spatial statistics, or as a guide to the R sp data structures for users looking to extend the now-standard spatial objects in R.

The authors clearly know their stuff--they wrote or maintain large portions of the R spatial package universe. They also know the spatial literature quite well. Indeed one of the real strengths of the book is that they not only provide comprehensive references to articles and textbooks but provide guidance on which is best for particular topics.

All in all, this book is well worth purchasing if you do any spatial statistical work in R, just as its title suggests.
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