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Multiscale Geographically Weighted Regression (MGWR)×Model priestorového oneskorenia (SAR / priestorovo autoregresný)×
OdborPriestorová analýzaPriestorová analýza
RodinaRegression modelRegression model
Rok vzniku20171988
TvorcaA. Stewart Fotheringham, Wei Yang, and Wei KangAnselin (textbook formalisation); LeSage & Pace
TypLocal spatial regressionSpatial autoregressive regression
Pôvodný zdrojFotheringham, 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 ↗
Ďalšie názvyMGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWRSAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Príbuzné55
ZhrnutieMultiscale 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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ScholarGatePorovnať metódy: Multiscale Geographically Weighted Regression · Spatial Lag Model. Získané 2026-06-18 z https://scholargate.app/sk/compare