Lu, Binbin and Charlton, Martin and Brunsdon, Chris and Harris, Paul (2015) The Minkowski approach for choosing the distance metric in geographically weighted regression. International Journal of Geographical Information Science, 30 (2). pp. 1-18. ISSN 1365-8824

Preview

Download (2MB) | Preview

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.

Item Type:	Article
Keywords:	Non-stationarity; GW model; Minkowski distance; simulation experiment;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
Item ID:	7850
Identification Number:	https://doi.org/10.1080/13658816.2015.1087001
Depositing User:	Martin Charlton
Date Deposited:	01 Feb 2017 16:51
Journal or Publication Title:	International Journal of Geographical Information Science
Publisher:	Taylor & Francis
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

Repository Staff Only(login required)

Item control page

Downloads

Origin of downloads