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Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England

Research output: Contribution to journalJournal article

Published
  • Daniel Grose
  • Richard Harris
  • Chris Brunsdon
  • Paul Longley
  • Alex Singleton
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<mark>Journal publication date</mark>2010
<mark>Journal</mark>Transactions in GIS
Issue number1
Volume14
Number of pages18
Pages (from-to)43-61
Publication statusPublished
Original languageEnglish

Abstract

Geographically Weighted Regression (GWR) is a method of spatial statistic alanalysis used to explore geographical differences in the effect of one or more predictor variables upon a response variable. However, as a form of local analysis,it does not scale well to (especially) large data sets because of the repeated processes of fitting and then comparing multiple regression surfaces. A solution is to make use of developing grid infrastructures, such as that provided by the National Grid Service (NGS) in the UK, treating GWR as an “embarrassing parallel” problem and building on existing software platforms to provide a bridge between an open source implementation of GWR (in R) and the grid system. To demonstrate the approach, we apply it to a case study of participation in Higher Education, using GWR to detect spatial variation in social, cultural and demographic indicators of participation.