Using R with GIS Software


Most GIS software today, including ArcGIS, QGIS, GRASS, and other industry and open source applications, apply Python as a scripting and add-on language for plugins and programming needs that can increase spatial analytical functionality and spatial processing. However, more recent integration of the R statistical package has been applied, such as in QGIS, where users can access R’s increasingly growing and powerful spatial analysis library.

Growing Uses of R

Although R started as mainly a statistical package, its use has grown to a number of areas, including natural language processing and web scrapping.[1] It also has strong spatial analytical tools including point pattern analysis and Bayesian geostatistical modeling. It can read and handle a variety of vector and raster data, including shapefiles, NetCDF, and GDAL supported formats.

How R is Used to Expand GIS Software

Traditional GIS packages have been limited by the fact their spatial statistics and analytical capabilities were relatively minor, including a small range of built-in functions, forcing users to use alternative platforms for advanced analysis and modeling and simulation. With the utility of R, many popular statistical procedures and more advanced analyses, including a variety of simulation applications, can be applied directly within tools such as QGIS.[2]

Users can also use R natively where visualizations allow for spatial analysis to be done within R. While R and QGIS are both not commonly used in industry, increasingly there are more research applications that integrate these tools. Examples include a recent paper on mapping Borneo’s tropical rainforests where a beta-logistic regression was used to assess structural changes evident.[3]

The Processing Toolbox in QGIS includes tools from R.  From Menke, 2016.

The Processing Toolbox in QGIS includes tools from R. From Menke, 2016.

Another example includes a recent paper on the mammalian fossil record.[4] The examples show that more powerful spatial analytical capabilities, including utilizing R powerful visualization packages, such as ggmap, have allowed users to leverage this new tool within existing popular and open source GIS products.


[1] For more on R, see:

[2] For a useful blog on the integration of R and GIS, see: Kurt Menke’s article – QGIS, Open Source GIS & R, May 2016.  

[3] For more on this example, see:  Pfeifer, M., Kor, L., Nilus, R., Turner, E., Cusack, J., Lysenko, I., … Ewers, R. M. (2016). Mapping the structure of Borneo’s tropical forests across a degradation gradient. Remote Sensing of Environment, 176, 84–97.

[4] For more on this paper, see:  Fortelius, M., Žliobaitė, I., Kaya, F., Bibi, F., Bobe, R., Leakey, L., … Werdelin, L. (2016). An ecometric analysis of the fossil mammal record of the Turkana Basin. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1698), 20150232.



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