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    Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data


    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

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    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.

    Item Type: Article
    Keywords: local regression; non-stationarity; road network distance; travel time; real estate;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 7852
    Identification Number: https://doi.org/10.1080/13658816.2013.865739
    Depositing User: Martin Charlton
    Date Deposited: 01 Feb 2017 17:19
    Journal or Publication Title: International Journal of Geographical Information Science
    Publisher: Taylor & Francis
    Refereed: Yes
    URI:

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