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Unit information: Spatial data analysis, spatial regression modelling and GIS in R in 2019/20

Please note: It is possible that the information shown for future academic years may change due to developments in the relevant academic field. Optional unit availability varies depending on both staffing and student choice.

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

Description

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.

Intended learning outcomes

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.

Teaching details

Two full days of teaching and lab classes early on in the teaching block followed by a seminar and assessment later in the term.

Assessment Details

An individual data analysis project and report (100%)

Reading and References

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.

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