Multiscale Geographically Weighted Regression

MGWR (Multi-scale Geographically Weighted Regression) is a new release of a Microsoft Windows & MacOS based application software for calibrating multi-scale geographically weighted regression (GWR) models, which can be used to explore geographically varying relationships between dependent/response variables and independent/explanatory variables. It incorporates the widely used approach to modeling process spatial heterogeneity - Geographically Weighted Regression (GWR) as well as the newly proposed approach - Multiscale GWR (MGWR) which relaxes the assumption that all of the processes being modeled operate at the same spatial scale. A GWR model can be considered a type of regression model with geographically varying parameters.

A most remarkable feature of this release is the function to fit semiparametric GWR models, which allow you to mix globally fixed terms and locally varying terms of explanatory variables simultaneously. The function can be applied to popular types of generalized linear modelling including Gaussian, Poisson, and logistic regressions.

As an open source project, MGWR expects users to cite use of the software. The following is the citation information for the software:

• Oshan, T.M., Z. Li, W. Kang, L. J. Wolf and A. S. Fotheringham “mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale” ISPRS International Journal of Geo-Information, 8(6) 269 2019. doi:10.3390/ijgi8060269 https://www.mdpi.com/2220-9964/8/6/269/pdf

To learn more about the open-source python package mgwr please visit our git repository at https://github.com/pysal/mgwr .