An Introduction to R for Spatial Analysis and Mapping
- Chris Brunsdon - National University of Ireland, Maynooth, Ireland
- Lex Comber - University of Leeds, UK
"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 Brist#strong/strong#
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.
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.
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.
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.
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.
The pedagogical materials are exceptionally useful, and will certainly be worth the investment of time, effort, and money for students and scholars alike. Brunsdon and Comber’s Introduction to R for Spatial Analysis and Mapping stands out as one of the best and most current foundations for spatial analysis with R for teaching and instruction.
Well laid out and easy to follow even for non-technical people.
Too advanced for my introductory class. Recommended to colleague teaching the advanced section.
Open source programs are coming more important in GIS analyses. Commercial GIS-programs does not always provide all essential statistical or spatial tools. In addition, some commercial GIS programs might have expensive licences. Therefore, it is important to have a possibility to utilize free open source programs. The R program is one of these kinds of programs. It has nowadays thousands users globally.
This book is a valuable handbook for those who want to use R for spatial analysis. This book provides important example strings of codes and informative illustrations. These both are essential when trying to guide reader or student deeper to powerful utilization of R in spatial analysis. I will recommend having this handbook as an additional reading in advanced GIS course if student wants to use R-program in his or her own GIS study project.
Book was too technical. I will definitely recommend it to students but I am not requiring it.
Sample Materials & Chapters
R-Script - Ch 2 Data and Plots
R-Script - Ch 3 Handling Spatial Data
R-Script - Ch 4 Programming in R
R-Script - Ch 5 Using R as a GIS
R-Script - Ch 6 Point Pattern Analysis
R-Script - Ch 7 Spatial Attribute Analysis
R-Script - Ch 8 Localised Spatial Analysis
R-Script - Ch 9 R and Internet Data
