Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Panel Local Indicators of Spatial Association× | Панельная пространственная автокорреляция× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1995 (LISA); panel extension 2000s–2010s | 1988–2003 |
| Автор метода≠ | Anselin (1995), panel extension developed through spatial econometrics literature | Anselin, L.; Elhorst, J. P. |
| Тип≠ | Local spatial autocorrelation statistic | Diagnostic test / exploratory statistic |
| Основополагающий источник≠ | Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ | Anselin, L. (2013). Spatial Econometrics: Methods and Models. Springer Netherlands. (Originally published 1988.) ISBN: 978-9401577991 |
| Другие названия | Panel LISA, spatiotemporal LISA, panel local spatial autocorrelation, LISA panel extension | spatial autocorrelation in panel data, panel spatial dependence, spatio-temporal autocorrelation, cross-sectional dependence in panels |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Panel Local Indicators of Spatial Association extends Anselin's LISA statistics — most commonly Local Moran's I — to panel datasets, identifying spatial clusters and outliers at each location across multiple time periods. By applying local autocorrelation measures repeatedly over time, researchers can detect whether spatial concentration patterns emerge, persist, or dissolve, giving a richer spatiotemporal picture than a single cross-section allows. | 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. |
| ScholarGateНабор данных ↗ |
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