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    Geographically Weighted Regression Using a Non-Euclidean Distance Metric with a Study on London House Price Data

    Lu, Binbin and Charlton, Martin and Fotheringham, Stewart (2011) Geographically Weighted Regression Using a Non-Euclidean Distance Metric with a Study on London House Price Data. Procedia Environmental Sciences, 7. pp. 92-97. ISSN 1878-0296

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    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.

    Item Type: Article
    Additional Information: Selection and peer-review under responsibility of Spatial Statistics 2011 (1st Conference on Spatial Statistics 2011). Open access under a CC BY-NC-ND license:
    Keywords: Geographically Weighted Regression; Non-Euclidean distance; Network distance; House price data;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 5756
    Identification Number:
    Depositing User: Martin Charlton
    Date Deposited: 02 Feb 2015 17:03
    Journal or Publication Title: Procedia Environmental Sciences
    Publisher: Elsevier
    Refereed: Yes

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