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An Introduction to R for Spatial Analysis and Mapping, by Chris Brunsdon, Lex Comber
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"In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses."
- Richard Harris, Professor of Quantitative Social Science, University of Bristol
R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and ‘non-geography’ students and researchers interested in spatial analysis and mapping.
This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality.
Brunsdon and Comber take readers from ‘zero to hero’ in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes:
- Example data and commands for exploring it
- Scripts and coding to exemplify specific functionality
- Advice for developing greater understanding - through functions such as locator(), View(), and alternative coding to achieve the same ends
- Self-contained exercises for students to work through
- Embedded code within the descriptive text.
This is a definitive 'how to' that takes students - of any discipline - from coding to actual applications and uses of R.
- Sales Rank: #125041 in Books
- Brand: imusti
- Published on: 2015-02-05
- Released on: 2015-01-24
- Original language: English
- Number of items: 1
- Dimensions: 9.53" h x .85" w x 6.69" l, 1.35 pounds
- Binding: Paperback
- 360 pages
Features
Review
In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses.
(Richard Harris, Professor of Quantitative Social Science)
Brunsdon and Comber's An Introduction to R for Spatial Analysis and Mapping is a timely text for students concerned with the exploration of spatial analysis problems and their solutions. The authors combine extensive expertise and practical experience with a clear and accessible pedagogic style in the presentation of problems in spatial analysis. This volume is not only an excellent resource for students in the spatial sciences but should also find a place on the bookshelves of researchers. (Martin Charlton)
If you are new to R and spatial analysis, then this is the book for you. With plenty of examples that are easy to use and adapt, there's something for everyone as it moves comfortably from mapping and spatial data handling to more advanced topics such as point-pattern analysis, spatial interpolation, and spatially varying parameter estimation. Of course, all of this is "free" because R is open source and allows anyone to use, modify, and add to its superb functionality.
(Scott M. Robeson)
The statistical sections each use "real" data, and each section ends with "Self-Test Questions". Thus the book is suitable not only as a reference for specific spatial data problems, but also for self-study or for training courses, if you want to approach the topic in principle. Overall, the book has a very successful, rounded overview of the analysis and visualization of spatial data. (Dr Thomas Rahlf)
About the Author
Chris Brunsdon is Professor of Geocomputation at the National University of Ireland, Maynooth. He studied Mathematics at the University of Durham and Medical Statistics at the University of Newcastle upon Tyne, and has worked in a number of universities, holding the Chair in Human Geography at Liverpool University before taking up his current position. His research interests are in health, crime and environmental data analysis, and in the development of spatial analytical tools, including Geographically Weighted Regression approach. He also has interests in the software tools used to develop such approaches, including R.
Lex Comber is a Professor of Geographical Information Sciences at the University of Leicester. After studying for a BSc in Plant and Crop Sciences at Nottingham, he did his PhD at the Macaulay Land Use Research Institute (now the Hutton Institute) and the University of Aberdeen. His research covers all areas of spatial analyses and the application and development of quantitative geographical. These have been applied across topic areas that straddle both the social and environmental and include accessibility analyses, land cover / land use monitoring and handling uncertainty in geographic information and spatial data.
Most helpful customer reviews
12 of 12 people found the following review helpful.
Accessible, clear and concise, and includes color graphics
By Jonathan Gross
I decided to talk a walk on the wild-side and examined R as a GIS for spatial analysis. I hope to use several of R's spatial statistics packages and to automate tasks--staying within one program. I highly recommend Brunsdon and Comber's book ($50 on Amazon, Paperback, electronic versions also available).
About the Authors: Chris Brundson is the creator of geographically weighted regression or GWR. Lex Comber is a professor at Leeds University.
Four Reasons to choose R as a GIS
1) You are interested in performing tailored exploratory spatial data analysis (ESDA), spatial statistics, regression analysis, and diagnostics.
• Of course, R is also way better than ArcGIS and QGIS for summary statistics too. (Notably, QGIS has integrated a R processing toolbox into it. ArcGIS also has an official bridge to R.)
2) You already use R for non-spatial data, have lots of code written, and need to analyze spatial data.
3) You do not want to export your data (or results) from one program into another and back again!
4) You want to be able to publish or share your code with a wider audience.
Reader Accessibility
The content is extremely well-presented, clear and concise, and includes color graphics. It is not overly technical. Still, R as a GIS and spatial analysis are tough material and is definitely not for the faint-of-heart. The authors assume readers may not have either a R or GIS background, or both. I took a R class in graduate school and occasionally use it.
Additional packages that assist in manipulating and reshaping data, such as plyr, are also discussed. The authors also warn readers that R packages can change over time, causing error messages, but many warn users about recent and upcoming changes.
Overview
In the first 40 pages, you will learn R basics, if you don't already have a foundation. Next, you will learn GIS fundamentals, how to plot data to create a map, taking into account scale, and adding and positioning common map elements like a north arrow and scale bar. This may sound basic but in R nothing is easy! Of course, the advantage with code is that you can reuse it or may only need to modify it slightly for many maps.
Late in Chapter 5-6 the book dives into spatial analysis. The last few chapters are probably the best of the book, as more advanced statistical techniques are discussed including local indicators of spatial auto correlation (LISAs), geographically weighted summary statistics and regression.
The book providers a great guide and reference, and I am sure I will be re-visiting it frequently! Overall, it is a great mix of practice and theory.
Disclosures:
None, I found and purchased the book on my own.
2 of 2 people found the following review helpful.
The font/color/darkness makes the R code impossible to read
By Amazon Customer
It might be a good book if I could read it. Unfortunately, the R code is printed so slightly and in a non-black color that I find it impossible to read. It's true that I don't have the eyes of a 20-year-old (I wore thick glasses even when I was 20), but I would think that many people would have trouble with the code in this book.
13 of 15 people found the following review helpful.
Gentle but comprehensive introduction to R as GIS tool
By Dr. Nickolas E. Jorgensen
I discovered Brunsdon and Comber’s book after completing two of Brunsdon’s GIS tutorials he had posted online. I was impressed by the clarity and tone of the tutorials, so when I found he had co-authored a book on using R for GIS I ordered it and began working through the code and exercises. The same qualities that had made the tutorials so appealing were equally present in the book. The authors not only present all the R code necessary to replicate the images and analyses in the book but also explain the purpose of each command and suggest possible extensions to the code that a reader could incorporate into projects of his or her own. Unlike some other R books whose authors either fail to explain their code fully or skip coding steps entirely (I’m not going to name names, since that could keep me here all day), Brunsdon and Comber do not assume prior expertise in R and instead serve as patient, thorough guides and instructors. As a result, the reader comes away from the book with both a solid understanding of how to use R as a GIS tool as well as a deeper general understanding of the software as a whole. R is free, and the number of publicly-available GIS datasets grows by the day, so if you want to pick up a potentially valuable and fun-to-use skill set this book is well worth the modest price.
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