The Minkowski approach for choosing the distance metric in geographically weighted regression. Issue 2 (1st February 2016)

• Record Type: Journal Article
• Authors: Lu, Binbin; Charlton, Martin; Brunsdon, Chris; Harris, Paul
• Is Part Of: International journal of geographical information science. Volume 30:Issue 2(2016)
• Page Start: 351
• Page End: 368
• Publication Date: 2016-02-01
• Subjects: Non-stationarity -- GW model -- Minkowski distance -- simulation experiment; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2015.1087001
• Languages: English
• ISSNs: 1365-8816

Abstract

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.