Geographically Weighted Regression using a non-Euclidean distance metric with a study on London house price data

Binbin Lu, Martin Charlton, Stewart Fotheringham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProcedia Environmental Sciences
Pages92-97
Number of pages6
Volume7
DOIs
StatePublished - 2011
Externally publishedYes
Event1st International Conference on Spatial Statistics 2011 - Enschede, Netherlands
Duration: Mar 23 2011Mar 25 2011

Other

Other1st International Conference on Spatial Statistics 2011
CountryNetherlands
CityEnschede
Period3/23/113/25/11

Keywords

  • Geographically Weighted Regression
  • House price data
  • Network distance
  • Non-Euclidean distance

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Environmental Science(all)
  • Geography, Planning and Development
  • Earth and Planetary Sciences(all)

Fingerprint Dive into the research topics of 'Geographically Weighted Regression using a non-Euclidean distance metric with a study on London house price data'. Together they form a unique fingerprint.

Cite this