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This course teaches the fundamentals of spatial analysis and geocomputation. The aim of the module is to give students a broad understanding of the theories, methods and tools required to analyse spatially referenced data of different types. Students will be shown the ways in which spatial data can be digitally represented, and how these representations affect how spatial data are analysed. 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. Students will apply their skills in a range of contexts, which may include house price estimation, crime science, transportation, geodeomographics and environmental sciences. 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/or 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 appropriate spatial analysis and geocomputational methods to a range of spatial datasets using software packages such as R

Reading List:

De Smith, M.J., Goodchild, M.F., Longley, P., 2007. Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. TroubadorPublishing Ltd

Grolemund, Garrett, and Hadley Wickham. R for Data Science. Accessed May 1, 2018. http://r4ds.had.co.nz/.

Longley, P., Goodchild, M.F., Maguire, D.J., Rhind, D., 2015. Geographic information Systems & Science. Wiley, Hoboken, NJ.

Worboys, M. 2004. GIS: a computing perspective /Michael Worboys, Matt Duckham., 2nd ed. ed. CRC Press, Boca Raton, Fla.; London.