Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Локальная пространственная модель Дурбина× | Локальная модель пространственного лага× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2002–2009 | 1988 (global); 2000s (local extensions) |
| Автор метода≠ | LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework | Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002) |
| Тип | Spatially varying regression model | Spatially varying regression model |
| Основополагающий источник≠ | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737215 |
| Другие названия | local SDM, geographically weighted Spatial Durbin Model, GW-SDM, spatially varying Durbin model | local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model |
| Связанные | 5 | 5 |
| Сводка≠ | The Local Spatial Durbin Model (Local SDM) extends the global Spatial Durbin Model by allowing regression coefficients to vary across geographic space. It combines the SDM's ability to capture both spatial lag of the dependent variable and spatial lags of covariates with a geographically weighted estimation framework, producing location-specific direct and indirect spillover effects. | 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. |
| ScholarGateНабор данных ↗ |
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