方法对比
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| 空间时间核密度估计 (ST-KDE)× | 局部核密度估计× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010 (space-time extension); 1956 (KDE origin) | 1985-1986 |
| 提出者≠ | Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen | Silverman, B. W.; Diggle, P. J. |
| 类型≠ | Non-parametric density estimation | Non-parametric density estimator |
| 开创性文献≠ | Nakaya, T., & Yano, K. (2010). Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS, 14(3), 223-239. DOI ↗ | Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. ISBN: 978-0412246203 |
| 别名 | ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation | Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation |
| 相关 | 5 | 5 |
| 摘要≠ | Space-Time Kernel Density Estimation extends classical KDE into three dimensions — two spatial and one temporal — to reveal how the intensity of point events (crimes, accidents, disease cases) varies continuously across both geographic space and time. It produces a smooth probabilistic surface that highlights where and when events concentrate most densely. | 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. |
| ScholarGate数据集 ↗ |
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