Linganisha mbinu

	Uchanganuzi wa Regresheni yenye Uzito wa Kijiografia wa Mizani Mingi (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	A. Stewart Fotheringham, Wei Yang, and Wei Kang	Anselin
Aina	Local spatial 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	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR	SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error)
Zinazohusiana	5	5
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.	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.