Premium
Grid‐enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England
Author(s) -
Harris Richard,
Singleton Alex,
Grose Daniel,
Brunsdon Chris,
Longley Paul
Publication year - 2010
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/j.1467-9671.2009.01181.x
Subject(s) - geographically weighted regression , grid , regression analysis , bridge (graph theory) , service (business) , variable (mathematics) , geography , linear regression , regression , scale (ratio) , computer science , cartography , econometrics , statistics , regional science , mathematics , business , geodesy , medicine , mathematical analysis , marketing
Geographically Weighted Regression (GWR) is a method of spatial statistical analysis 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.