方法对比
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| 面板核密度估计× | 空间时间核密度估计 (ST-KDE)× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1962 (KDE); panel extension: 1990s–2000s | 2010 (space-time extension); 1956 (KDE origin) |
| 提出者≠ | Parzen (1962); Silverman (1986); extended to panel contexts in spatial econometrics literature | Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen |
| 类型≠ | Nonparametric density estimation | Non-parametric density estimation |
| 开创性文献≠ | Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33(3), 1065-1076. DOI ↗ | 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 ↗ |
| 别名 | Panel KDE, longitudinal kernel density estimation, repeated-measures KDE, panel nonparametric density estimation | ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation |
| 相关 | 5 | 5 |
| 摘要≠ | Panel Kernel Density Estimation (Panel KDE) extends the standard kernel density estimator to panel (longitudinal) data, estimating smooth density surfaces for spatial or attribute variables observed across multiple units and time periods. It reveals how the distribution of a phenomenon shifts, concentrates, or disperses over time and across groups, making it a natural tool for tracking spatial patterns in repeated-measures or panel datasets. | 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. |
| ScholarGate数据集 ↗ |
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