Tomislav Hengl of the University of Amsterdam has published new book, A Practical Guide to Geostatistical Mapping. It’s jam-packed with 291 pages on mapping and analyzing spatial data using free software including R, SAGA, GRASS, ILWIS and Google Earth, and freely-available map data. The book itself is also available for free, as an Open Access Publication. You can order the book in printed form for US$12.78, or download it for free as a PDF.
Surprisingly (given the title), this book isn’t just about visual displays of spatial data. In fact, the first two chapters offer a nice overview of statistical analysis of spatial data (although with a greater focus on continuous-field models than point-process models). If you want a concise overview of regression-kriging, this is a great resource.
Chapter 3 addresses the various software tools you’ll use to analyze the data and create the maps. Some care has been taken in considering how the software elements should be integrated, and Hengl recommends a “R on top” model, where R scripts drive the other tools.
This is a clever move: making use of the scripting capabilities of R means you can avoid much of the tedious manual back-and-forth activities that are usually associated with working with several software tools. Hengl offers some other reasons for working with R, too (p. 90):
- It is of high quality — It is a non-proprietary product of international collaboration between top statisticians.
- It helps you think critically — It stimulates critical thinking about problem-solving rather than a push the button mentality.
- It is an open source software — Source code is published, so you can see the exact algorithms being used; expert statisticians can make sure the code is correct.
- It allows automation — Repetitive procedures can easily be automated by user-written scripts or functions.
- It helps you document your work — By scripting in R, anybody is able to reproduce your work (processing metadata). You can record steps taken using history mechanism even without scripting, e.g. by using the savehistory() command.
- It can handle and generate maps — R now also provides rich facilities for interpolation and statistical analysis of spatial data, including export to GIS packages and Google Earth.
Chapter 4 covers the various auxiliary data sources available, listing sources global environmental and socio-economic data, and sources of maps and satellite imagery like GADM, Google Earth and MODIS.
The remaining chapters are devoted to worked examples of spatial data analysis and mapping. By working through the examples, you can recreate charts like these (click to enlarge):
One minor complaint: most of the images in the book are in black-and-white (most likely to facilitate the printing process). But at least you have the R scripts and data for all exercises (these, plus updated maps, are available from the book’s website), so at least you can re-run the examples in R to recreate them in color.