Geographically weighted regression with parameter-specific distance metrics

A - Papers appearing in refereed journals

Lu, B., Charlton, M., Brunsdon, C. and Harris, P. 2016. Geographically weighted regression with parameter-specific distance metrics. International Journal Of Geographical Information Science. 31 (5), pp. 982-998.

AuthorsLu, B., Charlton, M., Brunsdon, C. and Harris, P.
Abstract

Geographically weighted regression (GWR) is an important local technique to model spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is generally used to calibrate a standard GWR model. However, variations in spatial relationships within a GWR model might also vary in intensity with respect to location and direction. This assertion has led to extensions of the standard GWR model to mixed (or semiparametric) GWR and to flexible bandwidth GWR models. In this article, we present a strongly related extension in fitting a GWR model with parameter-specific distance metrics (PSDM GWR). As with mixed and flexible bandwidth GWR models, a back-fitting algorithm is used for the calibration of the PSDM GWR model. The value of this new GWR model is demonstrated using a London house price data set as a case study. The results indicate that the PSDM GWR model can clearly improve the model calibration in terms of both goodness of fit and prediction accuracy, in contrast to the model fits when only one metric is singly used. Moreover, the PSDM GWR model provides added value in understanding how a regression model’s relationships may vary at different spatial scales, according to the bandwidths and distance metrics selected. PSDM GWR deals with spatial heterogeneities in data relationships in a general way, although questions remain on its model diagnostics, distance metric specification, and computational efficiency, providing options for further research.

KeywordsGWR; GWmodel; local regression; spatial heterogeneity; model anisotropy
Year of Publication2016
JournalInternational Journal Of Geographical Information Science
Journal citation31 (5), pp. 982-998
Digital Object Identifier (DOI)doi:10.1080/13658816.2016.1263731
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeThe North Wyke Farm Platform [2012-2017]
Output statusPublished
Publication dates
Online28 Nov 2016
Publication process dates
Accepted16 Nov 2016
ISSN13658816
1365-8816
PublisherTaylor & Francis
Copyright licensePublisher copyright

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