Using R with GIS Software

Mark Altaweel


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]

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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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About the author
Mark Altaweel
Mark Altaweel is a Reader in Near Eastern Archaeology at the Institute of Archaeology, University College London, having held previous appointments and joint appointments at the University of Chicago, University of Alaska, and Argonne National Laboratory. Mark has an undergraduate degree in Anthropology and Masters and PhD degrees from the University of Chicago’s Department of Near Eastern Languages and Civilizations.