Geographically weighted discriminant analysis

Chris Brunsdon, Stewart Fotheringham, Martin Charlton

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

In this article, we propose a novel analysis technique for geographical data, Geographically Weighted Discriminant Analysis. This approach adapts the method of Geographically Weighted Regression (GWR), allowing the modeling and prediction of categorical response variables. As with GWR, the relationship between predictor and response variables may alter over space, and calibration is achieved using a moving kernel window approach. The methodology is outlined and is illustrated with an example analysis of voting patterns in the 2005 UK general election. The example shows that similar social conditions can lead to different voting outcomes in different parts of England and Wales. Also discussed are techniques for visualizing the results of the analysis and methods for choosing the extent of the moving kernel window.

Original languageEnglish (US)
Pages (from-to)376-396
Number of pages21
JournalGeographical Analysis
Volume39
Issue number4
DOIs
StatePublished - Oct 2007

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

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