GY460 Half Unit
Techniques of Spatial Economic Analysis
This information is for the 2025/26 session.
Course Convenor
Prof Steve Gibbons
Availability
This course is compulsory on the MSc in Geographic Data Science. This course is available on the MPA in Data Science for Public Policy, MPhil/PhD in Economic Geography, MPhil/PhD in Regional and Urban Planning Studies, MSc in Environmental Economics and Climate Change, MSc in Environmental Policy, Technology and Health (Environmental Economics and Climate Change) (LSE and Peking University), MSc in Local Economic Development, MSc in Regional And Urban Planning Studies, MSc in Statistics (Social Statistics), MSc in Statistics (Social Statistics) (Research) and MSc in Urban Policy (LSE and Sciences Po). This course is available with permission as an outside option to students on other programmes where regulations permit. This course uses controlled access as part of the course selection process.
How to apply: Priority: MSc Geographic Data Science, then other (GY) students. Priority is typically for students enrolled in Geography and Environment programmes, or joint degree programmes, however course specific availability is indicated via the 'Availability section' on the LSE course guide webpages. Guidance on how to apply to individual controlled access courses can also be found on LSE for You in the Graduate Course Selection system.
Please note: The number of students that can be accommodated is limited. If a course is over-subscribed, places will be allocated at the Department's discretion and a waiting list may be created. It is advised to have an alternative course in mind as a back-up in case you are unable to secure your first-choice course selection.
Deadline for application: Further guidance and information on course selection for Geography and Environment courses (GY4xx) will be available on the Geography and Environment Course Selection Moodle page which will go live from Monday 8 September and will be updated with course availability information daily throughout the course selection period. This page includes information on the timeline for course selection decisions in the Geography and Environment Department as well as the individual course application processes and requirements
A list of all taught master's courses in this Department are listed on LSE's course guide webpages.
For queries contact: Geog.gds@lse.ac.uk
The number of students that can be accommodated is limited. Students requesting this course should provide information on their prior econometrics and statistics training and their motivation for study. If the course is over-subscribed, places will be allocated at the Department’s discretion based on this information and a waiting list may be created. For further details, please contact your relevant Programme Coordinator
Requisites
Additional requisites:
Students must have a good understanding of statistics and applied micro-econometrics at an undergraduate level or, for example, have studied Applied Quantitative Methods (GY428) in Autumn term or another course which introduces topics such as instrumental variables and panel data methods. It is advisable to look at the first two key readings listed below before signing up for this course. Students who are comfortable working with computers, data and already have basic familiarity with STATA, R or other statistics/econometrics software will get the most out of this course.
Course content
The aim of the course is to develop the technical tools necessary to understand and analyse spatial economic and social phenomena and to apply quantitative techniques to analyse economic and social problems, processes and policies at the urban and regional scale. Some of the content will be covered by studying and replicating the results of academic research papers. The practical sessions on the course make use of STATA for econometric work. Code in R is provided for students' own use. The course introduces Geographical Information Systems and other spatial computer applications for research purposes, but you should not expect to get a training in GIS from this course (GY476 provides a complementary GIS course).
Topics include: spatial weights, aggregation and smoothing methods; spatial econometric models and neighbourhood effects; answering causal questions in the spatial context; spatial interaction and discrete choice models; spatial cluster and point pattern analysis; inequality, competition and diversity; structural spatial economic models, applications of machine learning. Not all topics will be covered every year.
Teaching
20 hours of seminars and 10 hours of lectures in the Winter Term.
In the Department of Geography and Environment, teaching will be delivered through a combination of classes/seminars, pre-recorded lectures, live online lectures and other supplementary interactive live activities.
Formative assessment
Exercises and quizzes are provided during the Winter Term.
Indicative reading
A reading list and outline is available on Moodle. Important readings are
Gibbons, S., H.G Overman and E. Patacchini (2015) Spatial Methods, Ch. 3 in Duranton, G, J.V. Henderson and W. Strange (eds) Handbook of Urban and Regional Economics Vol 5a, Elsevier
Baum-Snow, N. and F. Ferreira (2015) Causal Inference in Urban Economics, Ch. 1 in Duranton, G, J.V. Henderson and W. Strange (eds) Handbook of Urban and Regional Economics Vol 5a, Elsevier
Assessment
Project (100%, 4000 words)
Assessment for this course involves a short independent quantitative research project. Some topics and data suggestions are provided, or students can carry out an approved project of their choosing. Deadline around end of ST. 4000 words. Weighting 100%
Key facts
Department: Geography and Environment
Course Study Period: Winter Term
Unit value: Half unit
FHEQ Level: Level 7
CEFR Level: Null
Total students 2024/25: 37
Average class size 2024/25: 37
Controlled access 2024/25: YesCourse selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Personal development skills
- Self-management
- Team working
- Problem solving
- Application of information skills
- Communication
- Application of numeracy skills
- Specialist skills