Work

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

Year: 2011

Type: article

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.

Source: Procedia Environmental Sciences

DOI: 10.1016/j.proenv.2011.07.017

Authors: Binbin Lu, Martin Charlton, A. Stewart Fotheringhama

Institution: National University of Ireland, Maynooth

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: diamond

Funders: Science Foundation Ireland, China Scholarship Council