| Unit name | Spatial data analysis, spatial regression modelling and GIS in R |
|---|---|
| Unit code | GEOGM0023 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Richard Harris |
| Open unit status | Not open |
| Pre-requisites |
An elementary knowledge of inferential statistics and of regression analysis |
| Co-requisites |
None |
| School/department | School of Geographical Sciences |
| Faculty | Faculty of Science |
The course looks at the theory and practice of how geographical information is analysed and modelled in R, a popular open source statistical and computing environment that offers both GIS and spatial statistical functionality suitable for research and commercial application. Day 1 is an introduction to R, focusing on geographical data analysis, manipulation and visualization. Day 2 will focus especially on measures of spatial autocorrelation, spatial regression modelling and geographically weighted regression.
On completing this course students will:
Have expertise in using R to map and model geographic data
Understand why the presence of geography can disrupt the assumptions of classic statistical analysis
Be able to employ methods of spatial analysis to detect, to allow for and to model patterns of geographical clustering
Know the differences between global and local approaches
To understand the centrality of a spatial weights matrix to most spatial analysis Be able to compare and contrast field based conceptions of geography with discrete and hierarchical approaches.
Two full days of teaching and lab classes early on in the teaching block followed by a seminar and assessment later in the term.
An individual data analysis project and report (100%)
Brunsdon C & Comber A, forthcoming, An introduction to geographical analysis and mapping in R. London: Sage.
Elhorst JP, 2013, Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Berlin: Springer-Verlag.
Harris R, not dated, An Introduction to Mapping and Spatial Modelling in R. http://www.researchgate.net/publication/258151270_An_Introduction_to_Mapping_and_Spatial_Modelling_in_R
Harris R & Jarvis C, 2011, Statistics for Geography and Environmental Science. London: Prentice Hall. Chapters 8 & 9.
de Smith MJ, Goodchild MF, Longley PA, 2013, Geospatial Analysis (4th edn.). http://www.spatialanalysisonline.com/
Ward MD & Gleditsch KS, 2008, Spatial Regression Models (Quantitative Applications in the Social Sciences series, 155). London: Sage.
