Lu, Binbin and Charlton, Martin and Harris, Paul and Fotheringham, Stewart
(2014)
Geographically weighted regression with a non-Euclidean distance
metric: a case study using hedonic house price data.
International Journal of Geographical Information Science, 28 (4).
pp. 660-681.
ISSN 1365-8816
Abstract
Geographically weighted regression (GWR) is an important local technique for exploring
spatial heterogeneity in data relationships. In fitting with Tobler’s first law of
geography, each local regression of GWR is estimated with data whose influence
decays with distance, distances that are commonly defined as straight line or
Euclidean. However, the complexity of our real world ensures that the scope of
possible distance metrics is far larger than the traditional Euclidean choice. Thus in
this article, the GWR model is investigated by applying it with alternative, non-
Euclidean distance (non-ED) metrics. Here we use as a case study, a London house
price data set coupled with hedonic independent variables, where GWR models are
calibrated with Euclidean distance (ED), road network distance and travel time metrics.
The results indicate that GWR calibrated with a non-Euclidean metric can not only
improve model fit, but also provide additional and useful insights into the nature of
varying relationships within the house price data set.
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