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Multiscale Geographically Weighted Regression (MGWR)×Model prostornog kašnjenja (SAR / Spatial Autoregressive)×
OblastProstorna analizaProstorna analiza
PorodicaRegression modelRegression model
Godina nastanka20171988
TvoracA. Stewart Fotheringham, Wei Yang, and Wei KangAnselin (textbook formalisation); LeSage & Pace
TipLocal spatial regressionSpatial autoregressive regression
Temeljni izvorFotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI ↗Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗
Drugi naziviMGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWRSAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Srodne55
SažetakMultiscale 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.
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ScholarGateUporedite metode: Multiscale Geographically Weighted Regression · Spatial Lag Model. Preuzeto 2026-06-18 sa https://scholargate.app/sr/compare