Software for <scp>MGWR</scp> : A Comparison of Six Algorithms

ABSTRACT Regression‐based local modeling through multiscale geographically weighted regression (MGWR) is popular in studies of spatial processes across a wide array of disciplines, and six major software packages exist for this purpose: MGWR 2.2.1; mgwr‐plugin 1.1 in QGIS; MGWR in ArcGIS Pro 3.4.3; GWmodel 2.4.1 in R; GWmodelS 1.0.4; and mgwrsar 1.1 in R. We compare major functionalities, model specification options, and performance attributes of these algorithms using both simulated and empirical data and show that the calibration results are not always consistent. The results provide an important reminder that the software used in calibrating local models should always be cited.

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Publication Details

Journal
Transactions in GIS
Published
2026-03-24
DOI
https://doi.org/10.1111/tgis.70211
Primary Topic
Spatial and Panel Data Analysis
Type
article
Field-Weighted Citation Impact
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article

Software for MGWR : A Comparison of Six Algorithms

Transactions in GIS
Spatial and Panel Data Analysis
article

Software for MGWR : A Comparison of Six Algorithms

article en

Abstract

ABSTRACT Regression‐based local modeling through multiscale geographically weighted regression (MGWR) is popular in studies of spatial processes across a wide array of disciplines, and six major software packages exist for this purpose: MGWR 2.2.1; mgwr‐plugin 1.1 in QGIS; MGWR in ArcGIS Pro 3.4.3; GWmodel 2.4.1 in R; GWmodelS 1.0.4; and mgwrsar 1.1 in R. We compare major functionalities, model specification options, and performance attributes of these algorithms using both simulated and empirical data and show that the calibration results are not always consistent. The results provide an important reminder that the software used in calibrating local models should always be cited.

Transactions in GISVol. 30(2)
Florida State University (US), Florida A&M University - Florida State University College of Engineering (US)
Openalex Percentile: Top 15%
Spatial and Panel Data Analysis
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