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Wednesday, July 5 • 1:48pm - 2:06pm
**RQGIS** - integrating *R* with QGIS for innovative geocomputing

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Keywords: GIS interface, QGIS, Python interface
Webpages: https://cran.r-project.org/web/packages/RQGIS/index.html, https://github.com/jannes-m/RQGIS
RQGIS establishes an interface to QGIS - the most widely used open-source desktop geographical information system (GIS). Since QGIS itself provides access to other GIS (SAGA, GRASS, GDAL, etc.), RQGIS brings more than 1000 geoalgorithms to the R console. Furthermore, R users do not have to touch Python though RQGIS makes use of the QGIS Python API in the background. Also, several convenience functions facilitate the usage of RQGIS. For instance, open_help provides instant access to the online help and get_args_man automatically collects all function arguments and respective default values of a specified geoalgorithm. The workhorse function run_qgis also accepts spatial objects residing in R’s global environment as input, and also loads QGIS output (such as shapefiles and rasters) directly back into R, if desired. Here, we will demonstrate the fruitful combination of R and QGIS by spatially predicting plant species richness of a Peruvian fog-oasis. For this, we will use RQGIS to extract terrain attributes (from a digital elevation model) which subsequently will serve as predictors in a non-linear Poisson regression. Apart from this, there are many more useful applications that combine R with GIS. For instance, GIS technologies include among others algorithms for the computation of stream networks, surface roughness, terrain classification, landform identification as well as routing and spatial neighbor operations. On the other hand, R provides access to advanced modeling techniques, kriging interpolation, and spatial autocorrelation and spatial cross-validation algorithms to name but a few. Naturally, this paves the way for innovative and advanced statistical geocomputing. Compared to other R packages integrating GIS functionalities (rgrass7, RSAGA, RPyGeo), RQGIS accesses a wider range of GIS functions, and is often easier to use. To conclude, anyone working with large spatio-temporal data in R may benefit from the R-QGIS integration.

Speakers


Wednesday July 5, 2017 1:48pm - 2:06pm
3.01 Wild Gallery
  • Company 58

Attendees (121)