Geographically weighted regression - Modelling spatial non-stationarity

Chris Brunsdont, Stewart Fotheringham, Martin Charlton

Research output: Contribution to journalArticle

472 Citations (Scopus)

Abstract

In regression models where the cases are geographical locations, sometimes regression coefficients do not remain fixed over space. A technique for exploring this phenomenon, geographically weighted regression is introduced. A related Monte Carlo significance test for spatial non-stationarity is also considered. Finally, an example of the method is given, using limiting long-term illness data from the 1991 UK census.

Original languageEnglish (US)
Pages (from-to)431-443
Number of pages13
JournalJournal of the Royal Statistical Society Series D: The Statistician
Volume47
Issue number3
StatePublished - 1998
Externally publishedYes

Fingerprint

Monte Carlo Test
Spatial Modeling
Significance Test
Nonstationarity
Census
Regression Coefficient
Regression Model
Limiting
Regression
regression
significance test
census
illness
Regression model
Coefficients
Geographically weighted regression
Limiting long-term illness
Significance test
Spatial modeling

Keywords

  • Exploratory data analysis
  • Geographically weighted regression
  • Monte Carlo testing
  • Regression
  • Spatial non-stationarity

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Geographically weighted regression - Modelling spatial non-stationarity. / Brunsdont, Chris; Fotheringham, Stewart; Charlton, Martin.

In: Journal of the Royal Statistical Society Series D: The Statistician, Vol. 47, No. 3, 1998, p. 431-443.

Research output: Contribution to journalArticle

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