Linganisha mbinu

	Urejeshaji wa Kijiografia Wenye Uzito wa Paneli (Panel GWR)	Usanifu wa Jiografia Wenye Uzani Mahali (GWR)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	2000s–2010s	1996
Mwanzilishi	Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature	Brunsdon, Fotheringham & Charlton
Aina	Local spatial regression with panel structure	Spatially varying coefficient regression
Chanzo asilia	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
Majina mbadala	Panel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regression	GWR, geographically weighted regression, local spatial regression, spatially varying coefficient model
Zinazohusiana	4	5
Muhtasari	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.