Multiscale Geographically Weighted Regression (MGWR)

This module provides functionality to calibrate multiscale (M)GWR as well as traditional GWR. It is built upon the sparse generalized linear modeling (spglm) module.

Features

• GWR model calibration via iteratively weighted least squares for Gaussian, Poisson, and binomial probability models.
• GWR bandwidth selection via golden section search or equal interval search
• GWR-specific model diagnostics, including a multiple hypothesis test correction and local collinearity
• Monte Carlo test for spatial variability of parameter estimate surfaces
• GWR-based spatial prediction
• MGWR model calibration via GAM iterative backfitting for Gaussian model
• Parallel computing for GWR and MGWR
• MGWR covariate-specific inference, including a multiple hypothesis test correction and local collinearity
• Bandwidth confidence intervals for GWR and MGWR

Citation

Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). 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.