High-performance solutions of geographically weighted regression in R

A - Papers appearing in refereed journals

Lu, B., Hu, Y., Murakami, D., Brunsdon, C., Comber, A., Charlton, M. and Harris, P. 2022. High-performance solutions of geographically weighted regression in R. Geo-spatial Information Science. pp. 1-14. https://doi.org/10.1080/10095020.2022.2064244

AuthorsLu, B., Hu, Y., Murakami, D., Brunsdon, C., Comber, A., Charlton, M. and Harris, P.
Abstract

As an established spatial analytical tool, Geographically Weighted Regression (GWR) has been applied across a variety of disciplines. However, its usage can be challenging for large datasets, which are increasingly prevalent in today’s digital world. In this study, we propose two highperformance
R solutions for GWR via Multi-core Parallel (MP) and Compute Unified Device Architecture (CUDA) techniques, respectively GWR-MP and GWR-CUDA. We compared GWRMP and GWR-CUDA with three existing solutions available in Geographically Weighted Models (GWmodel), Multi-scale GWR (MGWR) and Fast GWR (FastGWR). Results showed that all five solutions perform differently across varying sample sizes, with no single solution a clear winner in terms of computational efficiency. Specifically, solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size. For a large sample size, GWR-MP and FastGWR provided coherent solutions on a Personal
Computer (PC) with a common multi-core configuration, GWR-MP provided more efficient computing capacity for each core or thread than FastGWR. For cases when the sample size was
very large, and for these cases only, GWR-CUDA provided the most efficient solution, but should note its I/O cost with small samples. In summary, GWR-MP and GWR-CUDA provided
complementary high-performance R solutions to existing ones, where for certain data-rich GWR studies, they should be preferred.

KeywordsNon-stationarity; Big data; Parallel computing; Compute Unified Device Architecture (CUDA); Geographically Weighted models (GWmodel)
Year of Publication2022
JournalGeo-spatial Information Science
Journal citationpp. 1-14
Digital Object Identifier (DOI)https://doi.org/10.1080/10095020.2022.2064244
Web address (URL)https://doi.org/10.1080/10095020.2022.2064244
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Publisher's version
Accepted author manuscript
Output statusPublished
Publication dates
Online20 May 2022
Publication process dates
Accepted06 Apr 2022
PublisherSpringer-Verlag Berlin
ISSN1009-5020

Permalink - https://repository.rothamsted.ac.uk/item/988ww/high-performance-solutions-of-geographically-weighted-regression-in-r

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