High-performance solutions of geographically weighted regression in R

Lu, B, Hu, Y, Murakami, D et al. (4 more authors) (2022) High-performance solutions of geographically weighted regression in R. Geo-spatial Information Science, 25 (4). pp. 536-549. ISSN 1001-4993



Copyright, Publisher and Additional Information: © 2022 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Non-stationarity, big data, parallel computing, Compute Unified Device Architecture (CUDA), Geographically Weighted models (GWmodel)
  • Accepted: 6 April 2022
  • Published (online): 20 May 2022
  • Published: 2 November 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 23 May 2022 13:10
Last Modified: 10 Jan 2023 11:04
Status: Published
Publisher: Taylor & Francis Open Access
Identification Number: https://doi.org/10.1080/10095020.2022.2064244