Linganisha mbinu

	Uchanganuzi wa Regresheni yenye Uzito wa Kijiografia wa Mizani Mingi (MGWR)	Regressioni Angani za Kienyeji
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	2017	1996
Mwanzilishi	A. Stewart Fotheringham, Wei Yang, and Wei Kang	Brunsdon, Fotheringham & Charlton
Aina	Local spatial regression	Spatially varying coefficient regression
Chanzo asilia	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Majina mbadala	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression
Zinazohusiana	5	6
Muhtasari	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.	Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number.