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

By Binbin Lu, Martin Charlton, A. Stewart Fotheringhama. 2011

98 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a33a6440d3a43c11be719a4f8a6b9a85a320afa4

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In Economics and Econometrics

1. Binbin Lu
2. Paul Harris
3. A. Stewart Fotheringham
4. Martin Charlton
5. Chris Brunsdon
6. Francky Fouedjio

In Global and Planetary Change

1. A. Stewart Fotheringham
2. Martin Charlton
3. Binbin Lu
4. Paul Harris

Citations in the corpus

• Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data (2014, methodology): “Further work could investigate the use of non-EDs for all of the listed spatial models, not just GWR. Observe that preliminary work on GWR with a ND metric (only) can be found in Lu et al. (2011), using a small subset of the London house price data of this study.”