Linganisha mbinu

	Urekebishaji wa Kijiografia wa Bayesian (BGWR)	Uchanganuzi wa Regresheni yenye Uzito wa Kijiografia wa Mizani Mingi (MGWR)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	2007	2017
Mwanzilishi	Wheeler & Calder (2007); Finley (2011)	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Aina	Bayesian spatially varying coefficient regression	Local spatial regression
Chanzo asilia	Finley, A. O. (2011). Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods in Ecology and Evolution, 2(2), 143-154.	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Majina mbadala	BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Zinazohusiana	5	5
Muhtasari	Bayesian Geographically Weighted Regression combines the spatially varying coefficient framework of GWR with Bayesian inference, placing Gaussian process priors on the locally varying regression coefficients. This yields full posterior distributions over each coefficient at every location, providing principled uncertainty quantification rather than only point estimates.	Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply.