Salīdzināt metodes
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| Lokālais telpiskās nobīdes modelis× | Ģeogrāfiski svērtā regresija (GWR)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1988 (global); 2000s (local extensions) | 2002 |
| Autors≠ | Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002) | Fotheringham, Brunsdon & Charlton |
| Tips≠ | Spatially varying regression model | Local spatial regression |
| Pirmavots≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737215 | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Citi nosaukumi | local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Local Spatial Lag Model extends the classical spatial lag model by allowing both the spatial autocorrelation parameter and the regression coefficients to vary across geographic locations. Instead of one global estimate of how neighboring outcomes influence each observation, the model fits location-specific parameters using kernel-weighted local estimation, revealing spatial heterogeneity in spatial dependence. | 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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