Conference Publication Details
Mandatory Fields
Lu, BB;Charlton, M;Fotheringham, AS
SPATIAL STATISTICS 2011: MAPPING GLOBAL CHANGE
Geographically Weighted Regression Using a Non-Euclidean Distance Metric with a Study on London House Price Data
2011
January
Published
1
7 ()
Optional Fields
VARYING COEFFICIENT MODELS GEOSTATISTICS
92
97
Geographically Weighted Regression (GWR) is a local modelling technique to estimate regression models with spatially varying relationships. Generally, the Euclidean distance is the default metric for calibrating a GWR model in previous research and applications; however, it may not always be the most reasonable choice due to a partition by some natural or man-made features. Thus, we attempt to use a non-Euclidean distance metric in GWR. In this study, a GWR model is established to explore spatially varying relationships between house price and floor area with sampled house prices in London. To calibrate this GWR model, network distance is adopted. Compared with the other results from calibrations with Euclidean distance or adaptive kernels, the output using network distance with a fixed kernel makes a significant improvement, and the river Thames has a clear cut-off effect on the parameter estimations. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Spatial Statistics 2011
10.1016/j.proenv.2011.07.017
Grant Details