andy-esch / mgwr

Multiscale Geographically Weighted Regression (MGWR) module

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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
  • MGWR covariate-specific inference, including a multiple hypothesis test correction and local collinearity

About

Multiscale Geographically Weighted Regression (MGWR) module

License:BSD 2-Clause "Simplified" License


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Language:Python 96.7%Language:Jupyter Notebook 3.3%