AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Geo-Spatial Information Science Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Article | Open Access

GWmodelS: a standalone software to train geographically weighted models

Binbin Lua , Yigong Hub , Dongyang Yangc , Yong Liuc, Guangyu Oua, Paul Harrisd, Chris Brunsdone , Alexis Comberf , Guanpeng Dongc,g,h ( )
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
School of Geographical Sciences, University of Bristol, Bristol, UK
Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng, China
Net Zero and Resilient Farming, Rothamsted Research, North Wyke, UK
National Centre for Geocomputation, Maynooth University, Maynooth, Ireland
School of Geography, University of Leeds, Leeds, UK
Collaborative Innovation Center on Yellow River Civilization Jointly Built By Henan Province and Ministry of Education, Henan University, Kaifeng, China
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng, China
Show Author Information

Abstract

With the recent increase in studies on spatial heterogeneity, geographically weighted (GW) models have become an essential set of local techniques, attracting a wide range of users from different domains. In this study, we demonstrate a newly developed standalone GW software, GWmodelS using a community-level house price data set for Wuhan, China. In detail, a number of fundamental GW models are illustrated, including GW descriptive statistics, basic and multiscale GW regression, and GW principle component analysis. Additionally, functionality in spatial data management and batch mapping are presented as essential supplementary activities for GW modeling. The software provides significant advantages in terms of a user-friendly graphical user interface, operational efficiency, and accessibility, which facilitate its usage for users from a wide range of domains.

Keywords

Spatial heterogeneityspatial non-stationarityvisualizationhigh-performancelocal techniques

References

【1】
【1】
 
 
Crossref Google Scholar
Geo-Spatial Information Science
Volume 28 Issue 2,
April 2025
Pages 648-670
DOI: 10.1080/10095020.2024.2343011

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Cite this Report
Close
Close
Cite this article:
Lu B, Hu Y, Yang D, et al. GWmodelS: a standalone software to train geographically weighted models. Geo-Spatial Information Science, 2025, 28(2): 648-670. https://doi.org/10.1080/10095020.2024.2343011

314

Views

10

Crossref

11

Web of Science

12

Scopus

0

CSCD
Received: 26 November 2023
Accepted: 09 April 2024
Published: 01 May 2024
© 2024 Wuhan University.

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. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.