Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor


Harris, Paul and Brunsdon, Chris and Fotheringham, A. Stewart (2010) Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor. Stochastic Environmental Research and Risk Assessment, 25 (2). pp. 123-128. ISSN 1436-3259

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Abstract

In this study, we link and compare the geographically weighted regression (GWR) model with the kriging with an external drift (KED) model of geostatistics. This includes empirical work where models are performance tested with respect to prediction and prediction uncertainty accuracy. In basic forms, GWR and KED (specified with local neighbourhoods) both cater for nonstationary correlations (i.e. the process is heteroskedastic with respect to relationships between the variable of interest and its covariates) and as such, can predict more accurately than models that do not. Furthermore, on specification of an additional heteroskedastic term to the same models (now with respect to a process variance), locallyaccurate measures of prediction uncertainty can result. These heteroskedastic extensions of GWR and KED can be preferred to basic constructions, whose measures of prediction uncertainty are only ever likely to be globallyaccurate. We evaluate both basic and heteroskedastic GWR and KED models using a case study data set, where data relationships are known to vary across space. Here GWR performs well with respect to the more involved KED model and as such, GWR is considered a viable alternative to the more established model in this particular comparison. Our study adds to a growing body of empirical evidence that GWR can be a worthy predictor; complementing its more usual guise as an exploratory technique for investigating relationships in multivariate spatial data sets.

Item Type: Article
Keywords: Heteroskedastic; Local uncertainty; Relationship nonstationarity;
Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
Item ID: 5865
Identification Number: 10.1007/s00477-010-0444-6
Depositing User: Prof. Chris Brunsdon
Date Deposited: 19 Feb 2015 10:02
Journal or Publication Title: Stochastic Environmental Research and Risk Assessment
Publisher: Springer Verlag
Refereed: Yes
URI:

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