Linganisha mbinu

	Urejeshaji wa Kijiografia Wenye Uzito wa Paneli (Panel GWR)	Uchanganuzi wa Regresheni yenye Uzito wa Kijiografia wa Mizani Mingi (MGWR)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	2000s–2010s	2017
Mwanzilishi	Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Aina	Local spatial regression with panel structure	Local spatial 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., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Majina mbadala	Panel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regression	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
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.	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.