Dr Ana Varela Varela

This course is compulsory on the MSc in Geographic Data Science. This course is available on the MSc in Environment and Development, MSc in Environmental Economics and Climate Change, MSc in Environmental Policy and Regulation, MSc in Human Geography and Urban Studies (Research), MSc in Local Economic Development, MSc in Regional And Urban Planning Studies, MSc in Urban Policy (LSE and Sciences Po) and MSc in Urbanisation and Development. This course is available with permission as an outside option to students on other programmes where regulations permit.

Subject to approval by course organiser.

The number of students that can be accommodated is limited. If the course is over-subscribed, places will be allocated at the Department’s discretion and a waiting list may be created. For further details, please contact your relevant Programme Coordinator.

Geographical Information Systems (GIS) offer the social scientist an array of tools for generating, manipulating and visualising spatial data. This course covers practical GIS techniques for the social scientist, demonstrating how these tools can be combined with advanced analysis to enhance social science research. It emphasises practical skills and the use of relevant software. Specifically, the course will introduce the use of GIS tools in R and in QGIS. Attention will be given to a critical reflection upon the nature of the data used, encouraging students to go beyond traditional data use, and think about the role of the spatial data scientist in selecting and developing evidence to support policymaking and practice. Examples of literature with applications in economic geography, environment, planning and other spatial social sciences will be provided for self-study. Readings are intended to develop a sound understanding of how real-world (geo)data are produced, their potential insights and biases, as well as opportunities and limitations.

Some of the topics covered in the course include introducing GIS and spatial data; processing, editing, and visualising various types of spatial data; spatial modelling; network analysis; working with online mapping resources; and applying machine learning techniques to spatial data.

20 hours of computer workshops in the AT.

MSc in Geographic Data Science students will have additional sessions totalling 4 hours to cover more advanced material.

Formative work includes tasks designed to enhance understanding of the course material through practical application.

• Singleton, A., & Arribas-Bel, D.,(2019). Geographic Data Science, Geographical Analysis. 53:1, 61-75
• Lovelace, R., Nowosad, J., & Muenchow, J. (2024). Geocomputation with R. CRC Press.
• Donaldson, D., & Storeygard, A. (2016). The View from Above: Applications of Satellite Data in Economics. The Journal of Economic Perspectives: A Journal of the American Economic Association, 30(4), 171–198.
• Taylor, C. A., & Druckenmiller, H. (2022). Wetlands, Flooding, and the Clean Water Act. The American Economic Review, 112(4), 1334–1363.
• Davis, D. R., Dingel, J. I., Monras, J., & Morales, E. (2019). How Segregated Is Urban Consumption? The Journal of Political Economy, 127(4), 1684–1738.

Assignment (100%) in the AT.

Summative assessment will comprise a practical GIS analysis task. 

There will be two versions of the assignment, one for Geographic Data Science students and one for students from other programmes.