Linganisha mbinu

	Local Spatial Durbin Model	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	2002–2009	2017
Mwanzilishi	LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Aina	Spatially varying regression model	Local spatial regression
Chanzo asilia	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	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	local SDM, geographically weighted Spatial Durbin Model, GW-SDM, spatially varying Durbin model	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Zinazohusiana	5	5
Muhtasari	The Local Spatial Durbin Model (Local SDM) extends the global Spatial Durbin Model by allowing regression coefficients to vary across geographic space. It combines the SDM's ability to capture both spatial lag of the dependent variable and spatial lags of covariates with a geographically weighted estimation framework, producing location-specific direct and indirect spillover effects.	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.