Some notes on parametric significance tests for geographically weighted regression

Chris Brunsdon, Stewart Fotheringham, Martin Charlton

Research output: Contribution to journalArticle

203 Citations (Scopus)

Abstract

The technique of geographically weighted regression (GWR) is used to model spatial 'drift' in linear model coefficients. In this paper we extend the ideas of GWR in a number of ways. First, we introduce a set of analytically derived significance tests allowing a null hypothesis of no spatial parameter drift to be investigated. Second, we discuss 'mixed' GWR models where some parameters are fixed globally but others vary geographically. Again, models of this type may be assessed using significance tests. Finally, we consider a means of deciding the degree of parameter smoothing used in GWR based on the Mallows Cp statistic. To complete the paper, we analyze an example data set based on house prices in Kent in the U.K. using the techniques introduced.

Original languageEnglish (US)
Pages (from-to)497-524
Number of pages28
JournalJournal of Regional Science
Volume39
Issue number3
StatePublished - Aug 1999
Externally publishedYes

Fingerprint

significance test
regression
linear model
smoothing
statistics
test
parameter

ASJC Scopus subject areas

  • Environmental Science (miscellaneous)
  • Development

Cite this

Some notes on parametric significance tests for geographically weighted regression. / Brunsdon, Chris; Fotheringham, Stewart; Charlton, Martin.

In: Journal of Regional Science, Vol. 39, No. 3, 08.1999, p. 497-524.

Research output: Contribution to journalArticle

@article{18a37f283f0740c48e43dcd9f6b7ba7e,
title = "Some notes on parametric significance tests for geographically weighted regression",
abstract = "The technique of geographically weighted regression (GWR) is used to model spatial 'drift' in linear model coefficients. In this paper we extend the ideas of GWR in a number of ways. First, we introduce a set of analytically derived significance tests allowing a null hypothesis of no spatial parameter drift to be investigated. Second, we discuss 'mixed' GWR models where some parameters are fixed globally but others vary geographically. Again, models of this type may be assessed using significance tests. Finally, we consider a means of deciding the degree of parameter smoothing used in GWR based on the Mallows Cp statistic. To complete the paper, we analyze an example data set based on house prices in Kent in the U.K. using the techniques introduced.",
author = "Chris Brunsdon and Stewart Fotheringham and Martin Charlton",
year = "1999",
month = "8",
language = "English (US)",
volume = "39",
pages = "497--524",
journal = "Journal of Regional Science",
issn = "0022-4146",
publisher = "Wiley-Blackwell",
number = "3",

}

TY - JOUR

T1 - Some notes on parametric significance tests for geographically weighted regression

AU - Brunsdon, Chris

AU - Fotheringham, Stewart

AU - Charlton, Martin

PY - 1999/8

Y1 - 1999/8

N2 - The technique of geographically weighted regression (GWR) is used to model spatial 'drift' in linear model coefficients. In this paper we extend the ideas of GWR in a number of ways. First, we introduce a set of analytically derived significance tests allowing a null hypothesis of no spatial parameter drift to be investigated. Second, we discuss 'mixed' GWR models where some parameters are fixed globally but others vary geographically. Again, models of this type may be assessed using significance tests. Finally, we consider a means of deciding the degree of parameter smoothing used in GWR based on the Mallows Cp statistic. To complete the paper, we analyze an example data set based on house prices in Kent in the U.K. using the techniques introduced.

AB - The technique of geographically weighted regression (GWR) is used to model spatial 'drift' in linear model coefficients. In this paper we extend the ideas of GWR in a number of ways. First, we introduce a set of analytically derived significance tests allowing a null hypothesis of no spatial parameter drift to be investigated. Second, we discuss 'mixed' GWR models where some parameters are fixed globally but others vary geographically. Again, models of this type may be assessed using significance tests. Finally, we consider a means of deciding the degree of parameter smoothing used in GWR based on the Mallows Cp statistic. To complete the paper, we analyze an example data set based on house prices in Kent in the U.K. using the techniques introduced.

UR - http://www.scopus.com/inward/record.url?scp=0000722130&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0000722130&partnerID=8YFLogxK

M3 - Article

VL - 39

SP - 497

EP - 524

JO - Journal of Regional Science

JF - Journal of Regional Science

SN - 0022-4146

IS - 3

ER -