Linganisha mbinu

	Usanifu wa Usajili wa Kawaida wa Kijiografia wa Kiwango-Nyingi (MGWR)	Kielelezo cha Hitilafu za Kina (SEM)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	2017	1988
Mwanzilishi	Fotheringham, Yang & Kang	Anselin
Aina	Spatially varying coefficient regression	Spatial regression (spatially autocorrelated errors)
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.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Majina mbadala	multiscale GWR, multi-scale geographically weighted regression, Çok Ölçekli Coğrafi Ağırlıklı Regresyon (MGWR)	SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error)
Zinazohusiana	5	5
Muhtasari	Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can act locally while others act almost globally.	The Spatial Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move together, and it is estimated by maximum likelihood or GMM rather than ordinary least squares.