Geocomputation provides an introduction to the principles of spatial analysis and the use of programming for spatial data analysis. The purpose of this course is to equip you with an understanding of the principles underlying the conception, representation/measurement and analysis of spatial phenomena as well as how to use programming software, primarily the R programming language within the RStudio software environment. You will also be introduced to key data science principles in terms of the reproducibility of your research, which is important for those seeking careers as Spatial Data Scientists within both industry and academia.

The course is split into three main areas of work:

Foundational Concepts of GIScience
	Core Spatial Analysis Techniques
	Advanced Spatial Analysis Techniques
Over the ten weeks, the course provides an extensive introduction into the theory, methods and tools of spatial analysis whilst implementing small research projects, first using QGIS, and then using the R programming language within the RStudio software environment. It presents an overview of the core organising concepts and techniques of Geographic Information Systems, and the software and analysis systems that are integral to their effective deployment in advanced spatial analysis. The course is designed to have a large practical component in which you will use the latest software and techniques to analyse contemporary datasets. The intention is that you will finish with a broad knowledge of spatial analysis from which you can draw for the dissertation and further study/ employment.

You will learn:

How digital representations of the observable world are created as spatial data.
	How to recognise and explain limitations with spatial analysis, including the Modifiable Areal Unit Problem and ecological fallacy.
	How to understand the nature of spatial data and why the concepts of spatial autocorrelation and spatial heterogeneity are essential to spatial analysis.
	How to analyse both statistical and spatial data using core and advanced spatial and statistical analysis techniques.
	How to find, manage and clean spatial, demographic and socio-economic datasets for use within spatial analysis.
	How to program effectively, with an introduction to the general principles of programming in R.
	How to communicate your results, using both statistical and spatial visualisations, including charts and maps.
	How spatial analysis is being used within spatial data science and how data science is changing how we conduct spatial analysis.
The course will usually consist of approximately 10 lectures and 10 computer practicals.

Numbers taking the course are usually limited to 45. Priority will be given to students taking BA Geography with Quantitative Methods and students may be asked to provide a short written justification of why they wish to take the course if demand exceeds the number of places available.

Pre-requisites

Previous experience with using GIS software and/or programming in R is useful, however, the course provides a full introduction to spatial analysis and no pre-requisite knowledge is required.

Transferable skills

The course is an excellent precursor for those interested in a career in (spatial) data science, as well as those looking to undertake a quantitative dissertation. You will gain practical experience in: data finding, cleaning and management; coding in R; GIS; and presentation skills through the creation and design of maps and other visualisations. At the end of the course, students will have created a social atlas about a topic of their choice to consolidate their practical skills.