เปรียบเทียบวิธี

	Multiscale Geographically Weighted Regression	Spatial Lag Model
สาขาวิชา	การวิเคราะห์เชิงพื้นที่	การวิเคราะห์เชิงพื้นที่
ตระกูล	Regression model	Regression model
ปีกำเนิด	2017	1988
ผู้ริเริ่ม	A. Stewart Fotheringham, Wei Yang, and Wei Kang	Anselin (textbook formalisation); LeSage & Pace
ประเภท	Local spatial regression	Spatial autoregressive regression
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
ที่เกี่ยวข้อง	5	5
สรุป	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 Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.