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


    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

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    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:

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