| Unit name | Spatial Modelling 4 |
|---|---|
| Unit code | GEOGM0012 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. David Manley |
| Open unit status | Not open |
| Pre-requisites | |
| Co-requisites |
None |
| School/department | School of Geographical Sciences |
| Faculty | Faculty of Science |
The unit teaches the theory and methods of applied spatial econometrics and modelling using a combination of the statistical computing package, R, STATA, and GIS software. The focus is on how representations of space are encoded in such software and 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.
Unit aims: To understand how applied spatial data modelling is undertaken in R and how various representations of space and of spatial processes are encoded in it, as well as in other geographical data handling software. The course will deliver the technical competency and understanding required to research at the “coal face” of spatial statistics and spatial modelling, answering questions of policy relevance and informed, especially, by the field of spatial econometrics.
On completion of this Unit students should be able to:
The following transferable skills are developed in this Unit:
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%, to be submitted in week 3)
Individual project and report of about 3000 words, reporting on an applied data handling assignment (70%, to be submitted in week 10)
Progress is monitored by a series of computer exercises / practical sessions. Penalties apply for non attendance.
Percentage of the unit that is coursework: 100%
Percentage of overall unit mark involving group work: 0%
Total student learning and assessment hours (indicative)
Lectures/Seminars 10 Supervised Practicals 10 Unsupervised Practicals 60 Tutorials 1 Field Trips 0 Coursework 60 (of which c. 30% for the review essay, the remainder for the project) Revision, Reading and Self-Study 59 Examination 0 Total for unit 200 Hours
Jones, K. and Subramanian SV, (2012) Developing multilevel models for analysing contextuality, heterogeneity and change. (to be available via Blackboard)
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
Ward, M. D., Gleditsch, K. W. (2008). Spatial Regression Models. Quantitative Applications in the Social Sciences Series, 155. LA: Sage.
