Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Локальна просторова регресія× | Локальна модель просторового лагу× | |
|---|---|---|
| Галузь | Просторовий аналіз | Просторовий аналіз |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1996 | 1988 (global); 2000s (local extensions) |
| Автор методу≠ | Brunsdon, Fotheringham & Charlton | Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002) |
| Тип≠ | Spatially varying coefficient regression | Spatially varying regression model |
| Основоположне джерело≠ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737215 |
| Інші назви | locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression | local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number. | 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. |
| ScholarGateНабір даних ↗ |
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