Hawthorne Beyer

Introduction to GIS using R

R is well known as being among the most powerful statistics and modelling environments. Less well known is that it is also one of the most powerful packages for geospatial (GIS) analysis.

Several advantages of GIS analysis in R:

  • a feature-rich analytical environment (arguably unrivalled)
  • straightforward integration of geospatial functionality with statistical and modelling functions
  • precise analytical control (often better than commercial software in my experience)
  • transparency: no black boxes
  • cutting edge analytical capabilities if you want them
  • a flexible system for scripting, automation and parallelisation
  • free, open source, crossplatform software providing maximum potential for sharing data, code and workflows

... and some disadvantages of GIS analysis in R:

  • a steep learning curve as some of the spatial libraries are not straighforward to understand, and you obviously need to know how to use R too
  • not always easy to find which functionality is contained in which package (though Google is useful here)
  • functionality can change over time because R evolves rapidly (that said, all older versions of R remain available for download and can be installed alongside newer versions, so older functionality is always accessible)
  • processing speeds are relatively slow for some functions
  • command line driven interface (personally I do not view this as a disadvantage but I know some people do)

I run a workshop that introduces students to GIS analysis in R. It is designed to accelerate learning for people who have a basic working knowledge of R but have had no experience with GIS analysis in R.

Some of the themes this workshop covers:

  • load vector and raster data
  • displaying data
  • obtaining summary statistics
  • raster operations: reclassify thematic raster data, clipping rasters, calculate slope and aspect
  • generating random points
  • extracting raster values for points, lines and polygons
  • defining projections and reprojecting data

The workshop manual is provided here for anyone who wishes to learn from it:

An introduction to geospatial analysis in R An introduction to geospatial analysis in R

Australian Research Council DECRA Research Fellow

Email: hawthorne -at- spatialecology.com or h.beyer -at- uq.edu.au

Affiliations:

ARC Centre of Excellence for Environmental Decisions &

Centre for Biodiversity and Conservation Science &

Environmental Decisions Group,

School of Biological Sciences, Goddard Building

University of Queensland

Brisbane, Queensland 4072 Australia