Module 6: Spatial Data Analysis

Prerequisites

Introducing students to methods of data analysis that are relevant to spatial data. Discussing nature of Geographic Information Science (GISc), describing how space is conceptualised and represented in a GIS.

Mphil 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 the regression model.

• Session 1: GIScience and the nature of geographical space
• Session 2: Properties of spatial data
• Session 3: Quantifying spatial structure
• Session 4: Spatial data quality
• Session 5: Spatial interpolation:geometric and distance weighting methods
• Session 6: Exploratory spatial data analysis
• Session 7: Cluster detection
• Session 8: Regression analysis applied to spatial data

• The objective is 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
• Practicals held in the Top Lab, Geography Department, Downing Site

GIS software

Satisfactory completion of 5 practicals.

Haining, R.P. (2003) Spatial Data Analysis: Theory and Practice. P.432. CUP.

• To gain the 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

Eight times in Michaelmas term