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.
