Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Локальная оценка плотности ядра× | Локальный индекс пространственной автокорреляции (LISA)× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1985-1986 | 1995 |
| Автор метода≠ | Silverman, B. W.; Diggle, P. J. | Luc Anselin |
| Тип≠ | Non-parametric density estimator | Local spatial autocorrelation statistic |
| Основополагающий источник≠ | Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. ISBN: 978-0412246203 | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Другие названия | Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation | Local Indicator of Spatial Association, LISA statistic, Anselin Local Moran, local spatial autocorrelation index |
| Связанные≠ | 5 | 6 |
| Сводка≠ | Local Kernel Density Estimation (Local KDE) is a non-parametric spatial method that estimates the density of point events at each location by applying a kernel function with a spatially adaptive bandwidth. Unlike global KDE, which uses a fixed bandwidth across the entire study area, Local KDE adjusts the smoothing window according to local data density, capturing fine-scale clustering where events are sparse or concentrated. | 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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