Short course 1

R for advanced spatial analysis and mapping

This short course aims to provide an understanding and some experience of spatial data analysis in R,  focussing in particular on the statistical methods and spatial models  featured in the book “An Introduction to R for spatial analysis and mapping” by Chris Brunsdon and Lex Comber.   This will include the use  of R for point pattern analysis,   the analysis of spatial autocorrelation in spatially referenced variables,  and ‘localised’ spatial data analysis techniques such as Geographically Weighted Regression and related approaches.
The talks will outline theoretical ideas underlying each of these methods,  and will also demonstrate the practicalities of carrying them out in R.  A number of R libraries will be introduced,  in particular GWmodel, spdep and spatstat,  but no prior knowledge of any of these is required,    as their use will be covered in a number of practical examples.
We will also provide a number of datasets which may be used in practical sessions – in particular, sets of points used for the detection in clustering,   and sets of geographical areas with a number of associated variable for regression modelling.   As part of the practicals we will provide opportunities for attendees to discuss and interpret the results of the analysis. Attendees are also welcome to bring their own data of the kind described,  and to experiment using the methods we will introduce on this data.

The course will be delivered by Professor Chris Brunsdon and Dr. Martin Charlton,  from the National Centre for Geocomputation in the National University of Ireland, Maynooth.  Chris was a co-author of the book mentioned in the course description,  and both instructors have played key roles in the development of Geographically Weighted Regression (GWR),  one of the approaches covered in the course.   Both have long-established interests in the analysis of social and economic data,  particularly in health related topics (Martin),  and crime (Chris).  They have delivered many courses on spatial data analysis,  and have published extensively on the topic over a period spanning four decades.