Spatial Data Analysis Prerequisites
This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.
This module introduces students to the capture, display and statistical analysis of spatial data. The first two sessions deal with the construction of a geo-database (using secondary data) and data mapping in a GIS (Geographical Information System). The associated lectures include: descriptions of different spatial data types and spatial objects and a review of spatial data quality issues. Session three asks what is special about spatial data when undertaking statistical analysis and the associated practical looks at spatial autocorrelation – one of the fundamental properties of spatial data. Session four introduces the principles and some of the methods of exploratory spatial data analysis (ESDA). Session five looks at the topic of cluster or “hot spot” detection (identifying areas of excess risk in the context of disease and crime rates). Session six then considers the special issues that need to be recognized when fitting a regression model (to estimate the association between a dependent variable and a set of independent variables) using spatial data. The course concludes with two special topics – session seven looks at non-parametric methods of spatial interpolation (methods for constructing a map from sampled data) whilst session eight looks at areal interpolation (methods for transferring data from one spatial framework to another sometimes referred to as the “change of support problem”). Each session comprises a one hour lecture followed by a one hour practical class.
- Mphil and PhD students from participating departments taking the Social Science Research Methods Course as part of their research degree
- A basic course in statistics up to and including statistical inference (hypothesis testing: confidence intervals), and regression modelling.
Number of sessions: 8
# | Date | Time | Venue | Trainer | |
---|---|---|---|---|---|
1 | Wed 16 Oct 2013 14:00 - 16:00 | 14:00 - 16:00 | Department of Geography, Downing Site - Small Lecture Theatre | map | Robert Haining |
2 | Wed 23 Oct 2013 14:00 - 16:00 | 14:00 - 16:00 | Department of Geography, Downing Site - Small Lecture Theatre | map | Robert Haining |
3 | Wed 30 Oct 2013 14:00 - 16:00 | 14:00 - 16:00 | Department of Geography, Downing Site - Small Lecture Theatre | map | Robert Haining |
4 | Wed 6 Nov 2013 14:00 - 16:00 | 14:00 - 16:00 | Department of Geography, Downing Site - Small Lecture Theatre | map | Robert Haining |
5 | Wed 13 Nov 2013 14:00 - 16:00 | 14:00 - 16:00 | Department of Geography, Downing Site - Small Lecture Theatre | map | Robert Haining |
6 | Wed 20 Nov 2013 14:00 - 16:00 | 14:00 - 16:00 | Department of Geography, Downing Site - Small Lecture Theatre | map | Robert Haining |
7 | Wed 27 Nov 2013 14:00 - 16:00 | 14:00 - 16:00 | Department of Geography, Downing Site - Small Lecture Theatre | map | Robert Haining |
8 | Wed 4 Dec 2013 14:00 - 16:00 | 14:00 - 16:00 | Department of Geography, Downing Site - Small Lecture Theatre | map | Robert Haining |
- Session 1: Building a geo-database
- Session 2: Mapping using a GIS
- Session 3: What is special about spatial data?
- Session 4: Exploratory spatial data analysis
- Session 5: Cluster (hot-spot) detection
- Session 6: Regression with spatial data
- Session 7: Special topics I: non-parametric spatial interpolation
- Session 8: Special topics II: areal interpolation (“change of support”)
To introduce students to the methods of data analysis that are relevant for spatial data.
- understand how data quality is assessed
- attend practical classes on
- quantifying spatial structure - testing for spatial auto-correlation
- Lectures held in the Small Lecture Theatre, Geography Department, Downing Site (Haining, 1400-1500)
- Practicals held in the Top Lab, Geography Department, Downing Site (Amable, 1500-1600)
GIS software
Writing up two practicals, submitted as a single written exercise.
Haining, R.P. (2003) Spatial Data Analysis: Theory and Practice. CUP.
- To gain maximum benefits from the course it is important that students do not see this course in isolation from the other MPhil courses or research training they are taking.
- Responsibility lies with each student to consider the potential for their own research using methods common in fields of the social sciences that may seem remote. Ideally this task will be facilitated by integration of the SSRMC with discipline-specific courses in their departments and through reading and discussion.
Eight sessions of two hours each.
Once a year in Michaelmas term
Booking / availability