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Prostorno-vremenska mrežna prostorna analiza×Geographically Weighted Regression (GWR)×
PodručjeProstorna analizaProstorna analiza
ObiteljRegression modelRegression model
Godina nastanka1970–2000s2002
TvoracTorsten Hägerstrand (time-geography foundation); extended by Harvey J. Miller and others for network contextsFotheringham, Brunsdon & Charlton
VrstaSpatiotemporal network modelLocal spatial regression
Temeljni izvorHägerstrand, T. (1970). What about people in regional science? Papers of the Regional Science Association, 24(1), 7–21. DOI ↗Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Drugi naziviST-NBA, space-time network analysis, spatiotemporal network analysis, network-based space-time analysisGWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Srodne25
SažetakSpace-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.
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ScholarGateUsporedite metode: Space-Time Network-Based Spatial Analysis · Geographically Weighted Regression. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare