The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets

P. Harris*, Stewart Fotheringham, R. Crespo, M. Charlton

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    109 Citations (Scopus)

    Abstract

    Increasingly, the geographically weighted regression (GWR) model is being 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) traditional 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 provide 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 generating the data that are being predicted.

    Original languageEnglish
    Pages (from-to)657-680
    Number of pages24
    JournalMathematical geosciences
    Volume42
    Issue number6
    DOIs
    Publication statusPublished - Aug 2010

    Keywords

    • Spatial interpolation
    • Kriging
    • Relationship nonstationarity
    • Relationship heterogeneity
    • Tools
    • GWR
    • Nonstationarity

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