Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Lokālais telpiskās nobīdes modelis× | Telpiskās nobīdes modelis (SAR / Telpiskais autoregresīvais)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1988 (global); 2000s (local extensions) | 1988 |
| Autors≠ | Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002) | Anselin (textbook formalisation); LeSage & Pace |
| Tips≠ | Spatially varying regression model | Spatial autoregressive regression |
| Pirmavots≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737215 | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ |
| Citi nosaukumi | local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model | SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag) |
| 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. | The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts. |
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