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An Introduction to R for Spatial Analysis and Mapping Paperback – Feb 5 2015
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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
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 studied mathematics at the University of Durham before moving to the Department of Geography at Newcastle University where he remained for a number of years, until taking a Chair in the School of Computing at the University of Glamorgan, and subsequently a Chair in GIS at the University of Leicester. He is currently (as of July 1st) Professor of Human Geography in the School of Environmental Sciences at the University of Liverpool. His research interests include spatial statistics and statistical computing and spatial data visualisation. He has particular interest in applications of methods in the fields of health, crime and house prices.
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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.
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.
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.
None, I found and purchased the book on my own.
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