GWmodelS: a standalone software to train geographically weighted models

A - Papers appearing in refereed journals

Lua, B., Hu, Y., Yang, D., Liu, Y., Ou, G., Harris, P., Brunsdon, C., Comber, A. and Dong, G. 2024. GWmodelS: a standalone software to train geographically weighted models. Geo-spatial Information Science. https://doi.org/10.1080/10095020.2024.2343011

AuthorsLua, B., Hu, Y., Yang, D., Liu, Y., Ou, G., Harris, P., Brunsdon, C., Comber, A. and Dong, G.
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.

KeywordsSpatial heterogeneity; Spatial non-stationarity; Visualization; High-performance; Local techniques
Year of Publication2024
JournalGeo-spatial Information Science
Digital Object Identifier (DOI)https://doi.org/10.1080/10095020.2024.2343011
Open accessPublished as ‘gold’ (paid) open access
FunderNational Key Research and Development Program of China
National Natural Science Foundation of China
Publisher's version
Output statusPublished
Publication dates
Online01 May 2024
Publication process dates
Accepted09 Apr 2024
ISSN1009-5020
PublisherSpringer

Permalink - https://repository.rothamsted.ac.uk/item/99046/gwmodels-a-standalone-software-to-train-geographically-weighted-models

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