Applied Spatial Data Analysis With R

19 Oct 2011book-reviewsday-jobrgis

by Roger S. Bivand, Edzer J. Pebesma & Virgilio Gómez-Rubio

I recently had to do a bunch of geostatistical analysis on some climate data (to be specific, using universal kriging to interpolate a time series of solar radiation data covering the region I’m working on to a different grid). I started off trying to use the geostatistical analysis toolbox in ArcGIS, which works fine as far as it goes, but seems to be very difficult indeed to access via ArcGIS’s Python scripting interface. Since I had 36 years of daily data to process, doing it by hand was not an option.

I did the job using R, eventually, more by trial and error than through any great process of inspiration. I’d never done much geostatistical processing with R before, although I use it quite a lot for “normal” statistics and data analysis. My little voyage through the land of R-GeoStatistica was quite an eye opener. You can really get a lot done this way. If you know what you’re doing. Which is where this excellent little book comes in.

Part of the Use R! series published by Springer, Applied Spatial Data Analysis With R is written by the authors of some of the main R geostatistical packages, in particular the sp package which is used for the representation of geostatistical data. The book is divided into two main parts, the first dealing with general issues related to the representation of spatial data, and the second part divided into three sections dealing with point data, interpolation and geostatistics, and areal data. The point data and areal data sections seem to be well done, but they’re about subjects I’m not too familiar with, and that I don’t really need to know about for my current work. I mostly just skimmed them, but they seem comprehensive and well-explained. The interpolation and geostatistics section was what I was really here to see, and it’s good.

If I’d had this book before I started, I could easily have saved myself a day or two of scrabbling around not quite knowing which packages to use or how to set things up. The examples in the book are realistic, comprehensive and clear, and they serve as a very good basis to build from. Applied Spatial Data Analysis With R is never going to be ranked among the greats of scientific literature, but that’s not its aim–its aim is to explain how to do spatial data analysis in R, and it does that perfectly. Highly recommended.