Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Lokālais telpiskās nobīdes modelis× | Telpiskā autokorelācija× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1988 (global); 2000s (local extensions) | 1950 |
| Autors≠ | Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002) | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| Tips≠ | Spatially varying regression model | Spatial statistic / exploratory spatial data analysis |
| Pirmavots≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737215 | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Citi nosaukumi | local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| 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. | Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations. |
| ScholarGateDatu kopa ↗ |
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