Dr Elisabetta Pietrostefani

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 and leads on to show how these tools can be combined with more advanced analysis to augment and enhance social science research. The course covers the techniques and methods of GIS, with a focus on practical skills and will make use of desktop software. The course will introduce the use of GIS tools in R and in QGIS. The course will also introduce data extraction from online geographical services such as google maps. 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 science 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.

Topics covered include:

• Introduction to GIS and GIS data
• Digitising, geographic coordinate systems, georeferencing and editing
• Data query and transformation
• Remote sensed data and processing
• Geostatistical tools
• Network Analysis
• Working with online mapping resources
• Machine Learning, APIs, Web Scraping and spatial data

20 hours of computer workshops in the MT.

MSc in Geographic Data Science students will have an additional two session (4 hours) to cover more advanced material

Students will be expected to produce a number of practical exercises in the MT.

Formative work will be ongoing, with students submitting the answers to some of their weekly class exercises. Feedback will be provided on at least two of these exercises.

• Arribas-Bel, D., & Reades, J. (2018). Geography and computers: Past, present, and future. Geography Compass, 12, 1– 10.
• Singleton, A., & Arribas-Bel, D.,(2019). Geographic Data Science, Geographical Analysis. 53:1, 61-75 
• Overman, H.G. (2010) GIS a Job, What Use Geographical Information Systems in Spatial Economics?, Journal of Regional Science, 50(1) 165-180UR
• Brunsdon, C. and L. Comber, An Introduction to R for Spatial Analysis and Mapping, Sage Publications
• Henderson, J. Vernon, Adam Storeygard, and David N. Weil. 2012. Measuring Economic Growth from Outer Space. American Economic Review, 102 (2): 994-1028.
• Zook, Matthew, et al. "Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake." World Medical & Health Policy 2.2 (2010): 7-33.

Assignment (100%) in the MT.

Summative assessment will comprise a practical GIS analysis task. The task might involve, for example, developing measures of the constraints to growth city, due to availability of developable space, and ruggedness of neighbouring terrain.

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