Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR)

descriptionPublicationkeyboard_double_arrow_right Article 01 Nov 2008 English Publisher:SAGE PublicationsJournal:Environment and Planning A: Economy and Space, volume 40, pages 2,751-2,769 (issn: 0308-518X, eissn: 1472-3409,

Copyright policy )

Authors: Daniel A Griffith;

doi: 10.1068/a38218

Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR)

- Summary
- Metrics

Abstract

Interaction terms are constructed with georeferenced attribute variables and spatial filter eigenvectors, and then used to compute geographically varying regression coefficients. These coefficients, which are analogous to geographically weighted regression (GWR) coefficients, display preferable properties, and this specification is used to critique selected features of GWR. Comparisons are illustrated with the Georgia data appearing in the standard GWR tutorial.

Related Organizations

The University of Texas at Dallas
United States

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	209
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%