Harris, Paul and Brunsdon, Chris and Fotheringham, 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
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.
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