The Minkowski approach for choosing the distance metric in geographically weighted regression |
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Authors: | Binbin Lu Martin Charlton Chris Brunsdon Paul Harris |
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Institution: | 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China;2. National Centre for Geocomputation, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland;3. Sustainable Soils and Grassland Systems, Rothamsted Research, North Wyke, Okehampton, Devon, UK |
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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. |
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Keywords: | Non-stationarity GW model Minkowski distance simulation experiment |
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