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    The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets


    Harris, Paul and Fotheringham, Stewart and Crespo, R. and Charlton, Martin (2010) The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets. Mathematical Geosciences, 42 (6). pp. 657-680. ISSN 1874-8961

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    Abstract

    Increasingly, the geographically weighted regression (GWR) model is be- ing used for spatial prediction rather than for inference. Our study compares GWR as a predictor to (a) its global counterpart of multiple linear regression (MLR); (b) tra- ditional geostatistical models such as ordinary kriging (OK) and universal kriging (UK), with MLR as a mean component; and (c) hybrids, where kriging models are specified with GWR as a mean component. For this purpose, we test the performance of each model on data simulated with differing levels of spatial heterogeneity (with respect to data relationships in the mean process) and spatial autocorrelation (in the residual process). Our results demonstrate that kriging (in a UK form) should be the preferred predictor, reflecting its optimal statistical properties. However the GWR- kriging hybrids perform with merit and, as such, a predictor of this form may pro- vide a worthy alternative to UK for particular (non-stationary relationship) situations when UK models cannot be reliably calibrated. GWR predictors tend to perform more poorly than their more complex GWR-kriging counterparts, but both GWR-based models are useful in that they provide extra information on the spatial processes gen- erating the data that are being predicted.

    Item Type: Article
    Additional Information: Erratum included: Vol. 43 (3) 399, 2010.
    Keywords: Relationship nonstationarity; Relationship heterogeneity; GWR; Kriging; Spatial interpolation;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 5764
    Identification Number: https://doi.org/10.1007/s11004-010-9284-7
    Depositing User: Martin Charlton
    Date Deposited: 03 Feb 2015 15:33
    Journal or Publication Title: Mathematical Geosciences
    Publisher: Springer Verlag
    Refereed: Yes
    URI:

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