The Minkowski approach for choosing the distance metric in geographically weighted regression

A - Papers appearing in refereed journals

Lu, B., Charlton, M., Brunsdon, C. and Harris, P. 2016. The Minkowski approach for choosing the distance metric in geographically weighted regression. International Journal Of Geographical Information Science. 30 (2), pp. 351-368.

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

In this study, the geographically weighted regression (GWR) model is adapted to benefit from a broad range of distance metrics, where it is demonstrated that a well-chosen distance metric can improve model performance. How to choose or define such a distance metric is key, and in this respect, a ‘Minkowski approach’ is proposed that enables the selection of an optimum distance metric for a given GWR model. This approach is evaluated within a simulation experiment consisting of three scenarios. The results are twofold: (1) a well-chosen distance metric can significantly improve the predictive accuracy of a GWR model; and (2) the approach allows a good approximation of the underlying ‘optimal distance metric’, which is considered useful when the ‘true’ distance metric is unknown.

KeywordsNon-stationarity; GW model; Minkowski distance; simulation experiment
Year of Publication2016
JournalInternational Journal Of Geographical Information Science
Journal citation30 (2), pp. 351-368
Digital Object Identifier (DOI)doi:10.1080/13658816.2015.1087001
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
Online25 Sep 2015
Publication process dates
Accepted23 Aug 2015
PublisherTaylor & Francis
Copyright licensePublisher copyright
ISSN1365-8816

Permalink - https://repository.rothamsted.ac.uk/item/8v166/the-minkowski-approach-for-choosing-the-distance-metric-in-geographically-weighted-regression

Restricted files

Publisher's version

Under embargo indefinitely

12 total views
0 total downloads
0 views this month
0 downloads this month