| Unit name | Explanation, Causation and Longitudinal Analysis |
|---|---|
| Unit code | GEOGM0024 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Professor. David Manley |
| Open unit status | Not open |
| Pre-requisites |
A knowledge of regression modelling |
| Co-requisites |
None |
| School/department | School of Geographical Sciences |
| Faculty | Faculty of Science |
The unit teaches the theory and methods of applied econometrics and modelling using a combination of the statistical computing packages, especially MLwiN and STATA, and focusing on how they may be applied to the modelling of social and environmental processes. The unit provides higher level quantitative and spatial statistical research training suitable for individual research projects and postdoctoral work.
On completion of this Unit students should be able to:
(1) Use MLwiN / STATA / R to undertake applied spatial data analysis with R
(2) Have knowledge of the field of spatial econometrics and how it can be used in applied policy and decision-making
(3) Understand how spatial properties and relationships are encoded and represented within Geographical Information Science for geographical data handling and problem solving.
The following transferable skills are developed in this Unit: written communication, numeracy, computer literacy, problem solving, analytical skills, planning project management
Lectures, seminars, project-based practical working.
A review essay of about 2000 words reflecting on the practice of statistics and its suitability for spatial and policy analysis (30%)
Individual project and report of about 3000 words, reporting on an applied data handling assignment (70%)
Progress is monitored by a series of computer exercises / practical sessions. Penalties apply for non attendance.
Jones, K. and Subramanian SV, (2012) Developing multilevel models for analysing contextuality, heterogeneity and change.
Field, A., Miles, J., and Field, Z. (2012). Discovering Statistics Using R. Sage.
Fortheringham, A. S., Brunsdon, C., and Charlton, M. (2000) Quantitative Geography: Perspectives on Spatial Data Analysis. Sage.
Angrist, J. D., and Pischke, J-S. (2009) Mostly harmless econometrics: an empiricist's companion. Princeton University Press.
Levit, S. And Dubner, S. J. (2007) Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. Penguin
