What question did this study set out to answer?

The aim is to evaluate and compare the functionalities and performance of six MGWR software options.

March 26, 2026

Software for MGWR : A Comparison of Six Algorithms

Key Points

The aim is to evaluate and compare the functionalities and performance of six MGWR software options.
Comparison of six MGWR software packages based on functionalities and model specification options.
Analysis performed on both simulated and empirical data to assess performance attributes.
Evaluation of calibration results for consistency across different software.
Calibration results show inconsistency among the different MGWR software solutions.
Performance attributes vary significantly between the algorithms, suggesting careful selection is needed.
The results emphasize the importance of citing software used in local model calibrations.

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.

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Cite This Study

Kao et al. (Tue,) studied this question.

synapsesocial.com/papers/69c4cd05fdc3bde448918dab https://doi.org/https://doi.org/10.1111/tgis.70211

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