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| Μοντέλο Τοπικού Χωρικού Υστέρησης× | Χωρική Αυτοσυσχέτιση× | |
|---|---|---|
| Πεδίο | Χωρική Ανάλυση | Χωρική Ανάλυση |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 1988 (global); 2000s (local extensions) | 1950 |
| Δημιουργός≠ | 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) |
| Τύπος≠ | Spatially varying regression model | Spatial statistic / exploratory spatial data analysis |
| Θεμελιώδης πηγή≠ | 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 ↗ |
| Εναλλακτικές ονομασίες | local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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