Book Review: Applied Spatial Data Analysis with R by Bivand, Pebsma, and Gomez-Rubio

Originally posted to Goodreads. Applied Spatial Data Analysis with R by Roger S. Bivand

My review

rating: 5 of 5 stars
Applied Spatial Data Analysis with R (ASDAR) is written by the same people who wrote and maintain the spatial sp class in R. The book is not a statistician's text on mathematical geo-statistics, rather is focuses on taking geospatial (e.g. GIS) data and applying analysis within R. Not being a statistician, I used the book to learn how to manipulate geospatial data for my own analytical purposes. The mark of a good technical book, not only did I learn about how to work with the standard geospatial data types, I was able to implement analyses using the material in the book.


The book has three parts. First is an introduction to spatial data. Much of it is orienting the reader to the vocabulary of geospatial data such as point, line, polygon, grid, coordinate system, projection. It also motivates why using R for spatial analysis. (The other options would be within a GIS such as GRASS or ARCInfo, custom functions using C++ or Java, or Python, which has been incorporated into many GIS environments). In particular, it looks at the many packages and analysis built up that uses the sp package and data structure, allowing many developed analytical methods to be used together to build a complex analysis. (this is similar to my purpose, taking advantage of the fact that R provides a standard entry point to several computational toolkits that I use.)


The second part discusses accessing and using geospatial data in R, which fulfilled my purpose. It is detailed documentation on the various spatial classes and the methods that are applicable. There are descriptions and examples of how to visually display geospatial data. The chapter on data import and export covers GDAL/OGR, coordinate reference systems, projections and transformations, and what you would need to work with formats such as shapefiles, PostGIS, KML, image files such as tiff files and Google Earth overlays (PNG), or directly with GRASS, TerraLib, or Python interfaces with ArcGIS, RPyGeo.


The last part is on implementing geostatistical methods such as for pattern analysis, geostatistics, areal analysis (geographic aggregation), or epidemiology. I cannot comment too much on this as this is not an area that I have expertise, but the methods look both adequate as well as practical to use.


While the intended audience of this book are statisticians working with geospatial data, I would also recommend this to those who do data analysis or modeling with geospatial data. Most of the analytical texts I've seen discuss algorithms. This text gets into the practicalities of working with real data formats and real data issues that are the inevitable first step in a project. And it does so at a more analytical level then the point and click interface instructions that are enabled by standard GIS systems alone.


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