Computational improvements to multi-scale geographically weighted regression

Li, Z. and Fotheringham, A. S. (2020) Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science, 34(7), pp. 1378-1397. (doi: 10.1080/13658816.2020.1720692)

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Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different bandwidths for each covariate. However, the multiscale property of MGWR brings additional computation costs. The calibration process of MGWR involves iterative back-fitting under the additive model (AM) framework. Currently, MGWR can only be applied on small datasets within a tolerable time and is prohibitively time-consuming to run with moderately large datasets (greater than 5,000 observations). In this paper, we propose a parallel implementation that has crucial computational improvements to the MGWR calibration. This improved computational method reduces both memory footprint and runtime to allow MGWR modelling to be applied to moderate-to-large datasets (up to 100,000 observations). These improvements are integrated into the mgwr python package and the MGWR 2.0 software, both of which are freely available to download.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Li, Dr Ziqi
Authors: Li, Z., and Fotheringham, A. S.
College/School:College of Science and Engineering > School of Geographical and Earth Sciences
Journal Name:International Journal of Geographical Information Science
Publisher:Taylor & Francis
ISSN (Online):1365-8824
Published Online:06 February 2020

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