Abstract
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.
Original language | English (US) |
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Pages (from-to) | 1378-1397 |
Number of pages | 20 |
Journal | International Journal of Geographical Information Science |
Volume | 34 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2 2020 |
Keywords
- Geographically weighted regression
- local modelling
- multiscale
- parallel computing
- spatial analysis
ASJC Scopus subject areas
- Information Systems
- Geography, Planning and Development
- Library and Information Sciences
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Computational improvements to multi-scale geographically weighted regression
Fotheringham, S. (Contributor) & Li, Z. (Contributor), figshare Academic Research System, Jul 2 2020
DOI: 10.6084/m9.figshare.11821449.v1, https://doi.org/10.6084%2Fm9.figshare.11821449.v1
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