Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Глобальный индекс Морана× | Локальный индекс пространственной автокорреляции (LISA)× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1950 | 1995 |
| Автор метода≠ | Patrick Alfred Pierce Moran | Luc Anselin |
| Тип≠ | Global spatial autocorrelation test / index | Local spatial autocorrelation statistic |
| Основополагающий источник≠ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Другие названия | Moran's I, global spatial autocorrelation index, Moran index, GMI | Local Indicator of Spatial Association, LISA statistic, Anselin Local Moran, local spatial autocorrelation index |
| Связанные | 6 | 6 |
| Сводка≠ | Global Moran's I is the most widely used single-number summary of spatial autocorrelation across an entire study area. It compares the attribute value at each location with values at neighbouring locations using a spatial weights matrix, and returns a statistic ranging from −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering). A significance test determines whether the observed pattern is stronger than random chance. | Local Moran's I, introduced by Luc Anselin in 1995, is a Local Indicator of Spatial Association (LISA) that decomposes global spatial autocorrelation into location-specific contributions. For every observation it produces a signed statistic and a significance value, enabling researchers to identify spatial clusters (high-high, low-low) and spatial outliers (high-low, low-high) on a map. |
| ScholarGateНабор данных ↗ |
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