GIS interface packages on CRAN

GIS interfaces may be tightly or loosely coupled - for many purposes, the exchange of files in well-documented formats, such as text files, is sufficient, and no interface software is needed as such. On occasion, it is useful for R to be able to read from and write to the GIS application's database - this is tight coupling.

Spatial data analysis
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GRASS 5

GRASS provides an interface between the GRASS 5.0 geographical information system and R, based on starting R from within the GRASS environment using values of environment variables set in the GISRC file. Interface examples should be run outside GRASS, others may be run within. Wrapper and helper functions are provided for a range of R functions to match the interface metadata structures. It runs on Unix/Linux, and with Cygwin GRASS under Windows. It is documented in: Markus Neteler and Helena Mitasova: Open source GIS: A GRASS GIS approach. 2002. Kluwer: Boston, chapter 13; on the GRASS website, in Bivand, R. S., (2000) Using the R statistical data analysis language on GRASS 5.0 GIS data base files. Computers and Geosciences, 26, pp. 1043-1052, in a presentation at DSC2001, and in this draft paper. It has no homepage as such, but draft releases may be posted here.

GRASS 6

The GRASS/R interface, spgrass6, has been submitted to CRAN. It may also be accessed at the SourceForge site. The completed package depends on sp and uses the new sp classes; it also depends on the maptools and rgdal packages. The approach is described in Roger Bivand (2005) "Interfacing GRASS6
and R", GRASS Newsletter 3, pp. 11-16, which is now rather dated. A more up-to-date description for spgrass6 >= 0.3 is in this note.

StatConnector

It is possible to use StatConnector with ArcGIS, examples may be accessed (and new ones contributed) at a website maintained by Christophe Saint-Jean. ArcRstats - produces multivariate habitat prediction rasters using ArcGIS and the open-source R statistical package for implementing classification and regression (CART), generalized linear models (GLM) and generalized additive models (GAM) - using Python on the Arc side and StatConnector in R.