Comparer des méthodes

	Régression Géographiquement Pondérée sur Données de Panel (Panel GWR)	Régression Géographiquement Pondérée Locale (GWR)
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2000s–2010s	1996
Auteur d'origine	Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature	Brunsdon, Fotheringham & Charlton
Type	Local spatial regression with panel structure	Spatially varying coefficient regression
Source fondatrice	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Alias	Panel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regression	GWR, geographically weighted regression, local spatial regression, spatially varying coefficient model
Apparentées	4	5
Résumé	Panel 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.	Local Geographically Weighted Regression (GWR) estimates a separate regression model at each location in the study area, allowing every coefficient to vary spatially. By weighting nearby observations more heavily than distant ones, GWR reveals how predictor-outcome relationships shift across geographic space rather than forcing a single global estimate on heterogeneous data.