Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастные методы пространственной автокорреляции× | Локальные индикаторы пространственной ассоциации (LISA)× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1981–1995 | 1995 |
| Автор метода≠ | Cliff & Ord; extended by Anselin and colleagues | Luc Anselin |
| Тип≠ | Spatial dependence test (robust variant) | Local spatial statistic |
| Основополагающий источник≠ | Anselin, L., & Florax, R. J. G. M. (1995). Small sample properties of tests for spatial dependence in regression models: some further results. In Anselin, L. & Florax, R. J. G. M. (Eds.), New Directions in Spatial Econometrics. Springer, Berlin. link ↗ | Anselin, L. (1995). Local Indicators of Spatial Association — LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Другие названия | robust Moran's I, robust spatial dependence test, outlier-resistant spatial autocorrelation, RSA | LISA, local spatial autocorrelation statistics, local Moran's I, Anselin LISA |
| Связанные≠ | 5 | 6 |
| Сводка≠ | Robust spatial autocorrelation methods measure the degree to which nearby geographic units share similar values, while explicitly controlling for the distorting influence of spatial outliers and extreme observations. They extend classical statistics such as Moran's I by down-weighting or trimming observations that would otherwise inflate or deflate the autocorrelation signal. | LISA, introduced by Luc Anselin in 1995, decomposes a global spatial autocorrelation index into a location-specific statistic for every observation. It identifies where statistically significant spatial clusters and outliers occur on a map, enabling researchers to move beyond a single global summary and pinpoint the geographic sources of spatial dependence. |
| ScholarGateНабор данных ↗ |
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