Spatiotemporal Weighted Regression
This module provides functionality to calibrate STWR as well as traditional GWR and MGWR 2.0.2 (https://github.com/pysal/mgwr). It is built upon the sparse generalized linear modeling (spglm) module.
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STWR model calibration via a new spatiotemporal kernel. And it can use data observed at different past time stages to make the model better fit the latest observation points. A highlight of STWR is a new temporal kernel function, in which the method for temporal weighting is based on the degree of impact from each observed point to a regression point. The degree of impact, in turn, is based on the rate of value variation of the nearby observed point during the time interval. The updated spatiotemporal kernel function is based on a weighted combination of the temporal kernel with a commonly used spatial kernel (Gaussian or bi-square) by specifying a linear function of spatial bandwidth versus time.
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GWR model calibration via iteratively weighted least squares for Gaussian, Poisson, and binomial probability models.
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GWR bandwidth selection via golden section search or equal interval search
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GWR-specific model diagnostics, including a multiple hypothesis test correction and local collinearity
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Monte Carlo test for spatial variability of parameter estimate surfaces
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GWR-based spatial prediction
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MGWR model calibration via GAM iterative backfitting for Gaussian model
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MGWR covariate-specific inference, including a multiple hypothesis test correction and local collinearity
Related articles: 1 https://doi.org/10.5194/gmd-2019-292