Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Панельная пространственная автокорреляция× | Регрессия с географически взвешенными коэффициентами (GWR)× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1988–2003 | 2002 |
| Автор метода≠ | Anselin, L.; Elhorst, J. P. | Fotheringham, Brunsdon & Charlton |
| Тип≠ | Diagnostic test / exploratory statistic | Local spatial regression |
| Основополагающий источник≠ | Anselin, L. (2013). Spatial Econometrics: Methods and Models. Springer Netherlands. (Originally published 1988.) ISBN: 978-9401577991 | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Другие названия | spatial autocorrelation in panel data, panel spatial dependence, spatio-temporal autocorrelation, cross-sectional dependence in panels | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Связанные | 5 | 5 |
| Сводка≠ | Panel Spatial Autocorrelation measures whether observations that are geographically close also tend to have similar values across repeated time periods. It extends classic cross-sectional spatial autocorrelation statistics such as Moran's I to panel data, enabling researchers to detect spatial dependence consistently over time and to diagnose whether a panel regression model requires a spatial component. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
| ScholarGateНабор данных ↗ |
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