Welcome

Mapping and Modelling Geographic Data in R

About the course

The contents of this course were first developed for a short course at the University of Cape Town (UCT) in August 2022. It also forms part of the MSc Geographic Data Science and Spatial Analytics in the School of Geographical Sciences, University of Bristol.

The aims of this course are to teach an introduction to mapping, spatial analysis in R. It is a course in geographic data science with a particular focus on mapping, measuring and modelling spatial patterns in data. The core parts of the course are:

  • Why use R for mapping and spatial modelling?
  • The basics of mapping in R
  • The Spatial Variable: from maps towards models
  • Spatial clustering and spatial heterogeneity: measuring patterns in data
  • Harnessing spatial autocorrelation with geographically weighted statistics
  • Spatial regression models

This is a work in progress

Changes will be made and additional content added over time so check back here for the latest updates.

Course text

The most relevant texts for this course are:

First, parts II (Chapters 7 to 9) and III (Chapters 10 to 17) of Spatial Data Science with Applications in R. An online version of the book is available here. It takes a deeper dive into the fundamentals of spatial data science than this course does and is more ‘technical’ but is a good resource to extend your knowledge of geographical/spatial data science.

Second, Chapters 2, 3, 5, 7 and 8 of of Spatial Data Science with Applications in R. An online version of the book is available here.

Pre-reading

The following short pre-reading is recommended for the course:

Harris RJ (2019). Not just nuisance: spatialising social statistics. In A Whitworth (ed.) Towards a Spatial Social Policy: Bridging the Gap Between Geography and Social Policy. Chapter 8. Bristol: Policy Press. Available here (or, if that doesn’t work try here).

Other useful resources

Spatial Regression Models for the Social Sciences covers similar statistical ground to this course, For University of Bristol students, it is available to view as an eBook here.

In addition, Geocomputation with R by Robin Lovelace, Jakub Nawosad & Jannes Muenchow offers an extremely useful reference to have to hand if you are stuck when undertaking geocomputation with R. There is a free online version available.

Provisional Masters programme

For the 2024-5 iteration of the Masters unit, the teaching schedule is:

Week Date Lecture Practical Content Led by
1 Tues Sep 17 9 - 10am 10.30 - 1pm Introduction to the course, why R and set-up practical RH
2 Tues Sep 24 - 10.30 - 1pm Flavours of R RH
3 Tues Oct 1 9 - 10am 10.30 - 1pm Introduction to statistics RT
4 Tues Oct 8 9 - 10am 10.30 - 1pm Introduction to regression RT
5 Tues Oct 15 9 - 10am 10.30 - 1pm Mapping the spatial variable 1 RH
6 - - - -
7 Tues Oct 29 9 - 10am 10.30 - 1pm Mapping the spatial variable 2 RH
8 Tues Nov 5 9 - 10am 10.30 - 1pm Getting started on the assignment RH
9 Tues Nov 12 9 - 10am 10.30 - 1pm Measuring spatial autocorrelation RH
10 Tues Nov 19 9 - 10am 10.30 - 1pm Geographical Weighted Statistics RH
11 Tues Nov 26 - 10.30 - 1pm Spatial regression RH
12 TBC - TBC TBC TBC

About the author

This course is authored by Richard Harris (RH), Professor of Quantitative Social Geography at the University of Bristol. You can find out more about me, my research and other interests at https://profrichharris.github.io/. It is taught at the University with the assistance of Dr. Richard Timmerman (RT).

@profrichharris