Linganisha mbinu

	Uchambuzi wa Anga-Wakati wa Msingi wa Mtandao	Usuli wa Kawaida wa Kijiografia (GWR)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1970–2000s	2002
Mwanzilishi	Torsten Hägerstrand (time-geography foundation); extended by Harvey J. Miller and others for network contexts	Fotheringham, Brunsdon & Charlton
Aina	Spatiotemporal network model	Local spatial regression
Chanzo asilia	Hägerstrand, T. (1970). What about people in regional science? Papers of the Regional Science Association, 24(1), 7–21.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Majina mbadala	ST-NBA, space-time network analysis, spatiotemporal network analysis, network-based space-time analysis	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Zinazohusiana	2	5
Muhtasari	Space-Time Network-Based Spatial Analysis integrates network topology with temporal constraints to model how people, goods, or phenomena move through geographic networks over time. Rooted in Hägerstrand's time-geography, it evaluates accessibility, interaction potential, and movement patterns along real-world infrastructure networks while respecting both spatial distance and time budgets.	Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.