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 high- performance R solutions for GWR via Multi-core Parallel (MP) and Compute Unified Device Architecture (CUDA) techniques, respectively GWR-MP and GWR-CUDA. We compared GWR- MP 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.

Binbin Lu;Yigong Hu;Daisuke Murakami;Chris Brunsdon;Alexis Comber;Martin Charlton;Paul Harris

School of Remote Sensing and Information Engineering,Wuhan University,Wuhan,China;Department of Data Science,Institute of Mathematical Statistics,Tokyo,Japan;National Centre for Geocomputation,Maynooth University,Maynooth,Ireland;School of Geography,University of Leeds,Leeds,UK;Sustainable Agriculture Sciences North Wyke,Rothamsted Research,Okehampton,UK

地球空间信息科学学报（英文版）

[1]Binbin Lu;Yigong Hu;Daisuke Murakami;Chris Brunsdon;Alexis Comber;Martin Charlton;Paul Harris-.High-performance solutions of geographically weighted regression in R)[J].地球空间信息科学学报（英文版）,2022(04):536-549

GWmodel,FastGWR

High,performance,solutions,geographically,weighted,regression,established,spatial,analytical,tool,Geographically,Weighted,Regression,has,been,applied,across,variety,disciplines,However,its,usage,can,challenging,large,datasets,which,increasingly,prevalent,today,digital,world,In,this,study,propose,two,high,via,Multi,core,Parallel,MP,Unified,Device,Architecture,CUDA,techniques,respectively,compared,three,existing,available,Models,scale,MGWR,Results,showed,that,five,differently,varying,sizes,single,clear,winner,terms,computational,efficiency,Specifically,given,provided,acceptable,costs,studies,relatively,small,For,coherent,Personal,Computer,common,multi,configuration,more,efficient,computing,capacity,each,thread,than,cases,when,was,very,these,only,most,but,should,note,samples,summary,complementary,ones,where,certain,rich,they,preferred

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