​This course teaches the basics of spatial analysis and geocomputation. Students will be shown the ways in which spatial data can be digitally represented, and how these representations affect how spatial data are analysed. After a brief primer on statistical analysis, students will learn a range of spatial analysis and geocomputational techniques including:

  • Spatial autocorrelation analysis
  • Spatial regression and interpolation
  • Kernel density estimation and clustering
  • Geographically weighted regression and local methods
The aim of this module is to give students a broad understanding of the theories, methods and tools required to analyse spatially referenced data of different types. By the end of the course, students will be able to apply their skills to a range of spatial analysis problems using R Statistical Package and other software.

Learning Outcomes:

  • To understand digital representations of different types of spatial data
  • To gain an understanding of basic statistical analysis methods
  • To understand the need for spatial analysis
  • To gain broad knowledge of a range of spatial analysis and geocomputational techniques
  • To be able to apply spatial analysis and geocomputational methods to a range of spatial datasets using software packages such as R.