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Panel Geographically Weighted Regression (Panel GWR)×Multiscale Geographically Weighted Regression (MGWR)×
OblastProstorna analizaProstorna analiza
PorodicaRegression modelRegression model
Godina nastanka2000s–2010s2017
TvoracFotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literatureA. Stewart Fotheringham, Wei Yang, and Wei Kang
TipLocal spatial regression with panel structureLocal spatial regression
Temeljni izvorFotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI ↗
Drugi naziviPanel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regressionMGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Srodne45
SažetakPanel Geographically Weighted Regression (Panel GWR) extends the standard GWR framework to panel data, allowing regression coefficients to vary both across geographic locations and over time. It captures spatially non-stationary relationships in longitudinal or repeated-measures spatial datasets, combining local spatial estimation with panel-data controls for unit-specific heterogeneity.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.
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ScholarGateUporedite metode: Panel Geographically Weighted Regression · Multiscale Geographically Weighted Regression. Preuzeto 2026-06-18 sa https://scholargate.app/sr/compare