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Multiscale Geographically Weighted Regression (MGWR)

Build Status Documentation Status PyPI version

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.