Article ; Online: High-performance solutions of geographically weighted regression in R
Geo-spatial Information Science, Pp 1-
2022 Volume 14
Abstract: ... which are increasingly prevalent in today’s digital world. In this study, we propose two high-performance R ... performance R solutions to existing ones, where for certain data-rich GWR studies, they should be preferred. ...
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 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. |
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Keywords | Non-stationarity ; big data ; parallel computing ; Compute Unified Device Architecture (CUDA) ; Geographically Weighted models (GWmodel) ; Mathematical geography. Cartography ; GA1-1776 ; Geodesy ; QB275-343 |
Subject code | 518 ; 519 |
Language | English |
Publishing date | 2022-05-01T00:00:00Z |
Publisher | Taylor & Francis Group |
Document type | Article ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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