Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Ruimte-tijdnetwerk-gebaseerde ruimtelijke analyse× | Geografisch Gewogen Regressie (GWR)× | |
|---|---|---|
| Vakgebied | Ruimtelijke analyse | Ruimtelijke analyse |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1970–2000s | 2002 |
| Grondlegger≠ | Torsten Hägerstrand (time-geography foundation); extended by Harvey J. Miller and others for network contexts | Fotheringham, Brunsdon & Charlton |
| Type≠ | Spatiotemporal network model | Local spatial regression |
| Oorspronkelijke bron≠ | Hägerstrand, 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 |
| Aliassen | 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) |
| Verwant≠ | 2 | 5 |
| Samenvatting≠ | 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. |
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