@article{Lu2025, 
author = {Binbin Lu and Yigong Hu and Dongyang Yang and Yong Liu and Guangyu Ou and Paul Harris and Chris Brunsdon and Alexis Comber and Guanpeng Dong},
title = {GWmodelS: a standalone software to train geographically weighted models},
year = {2025},
journal = {Geo-Spatial Information Science},
volume = {28},
number = {2},
pages = {648-670},
keywords = {Spatial heterogeneity, spatial non-stationarity, visualization, high-performance, local techniques},
url = {https://www.sciopen.com/article/10.1080/10095020.2024.2343011},
doi = {10.1080/10095020.2024.2343011},
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.}
}