Prof Steve Gibbons S511

This course is available on the MPhil/PhD in Economic Geography, MPhil/PhD in Regional and Urban Planning Studies, MSc in Environmental Economics and Climate Change, 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.

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

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 Michaelmas 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.

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. The course also provides a hands-on introduction to using Geographical Information Systems and other spatial computer applications for research purposes, but you should not expect to get a full training in GIS from this course.

Topics typically include: Spatial representation, spatial data and Geographical Information Systems; 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.

In the Department of Geography and Environment, teaching will be delivered through a combination of classes/seminars, pre-recorded lectures, live online lectures, in-person lectures and other supplementary interactive live activities.

This course is delivered through a combination of computer practical classes/seminars and lectures across the Lent Term.

There are surgeries and opportunities for support for projects in the ST.

This course includes a reading week in Week 6 of Lent Term.

Throughout the term, progress and understanding will be assessed by short in-class assessments. Students will receive feedback on one piece of work, such as answers on questions related to one of the computer class assignments.

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

An overview of some topics is provided by: A Fotheringham, C Brunsdon; M Charlton, Quantitative Geography: Perspectives on Spatial Data Analysis. Sage Publications, 2000.

Project (100%, 5000 words) in the ST.

A quantitative research project of not more than 5000 words to be handed in at a specified date in the ST (100%). This project is carried out independently, but with guidance and support from teaching staff.