Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Локальная оценка плотности ядра× | Пространственная автокорреляция× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1985-1986 | 1950 |
| Автор метода≠ | Silverman, B. W.; Diggle, P. J. | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| Тип≠ | Non-parametric density estimator | Spatial statistic / exploratory spatial data analysis |
| Основополагающий источник≠ | Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. ISBN: 978-0412246203 | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Другие названия | Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Связанные | 5 | 5 |
| 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. | Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations. |
| ScholarGateНабор данных ↗ |
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