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| Kernel Density Crime Mapping× | Пространствено-времева оценка на плътността чрез ядро (ST-KDE)× | |
|---|---|---|
| Област≠ | Criminology | Пространствен анализ |
| Семейство≠ | Process / pipeline | Regression model |
| Година на възникване≠ | 2008 | 2010 (space-time extension); 1956 (KDE origin) |
| Създател≠ | Bernard Silverman (KDE); Spencer Chainey (crime mapping application) | Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen |
| Тип≠ | Nonparametric density estimation for crime surfaces | Non-parametric density estimation |
| Основополагащ източник≠ | Chainey, S., Tompson, L., & Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21(1–2), 4–28. 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 ↗ |
| Други названия | KDE Crime Mapping, Crime Density Surface Mapping, Hot Spot Density Mapping, Kernel Smoothing of Crime Events | ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation |
| Свързани≠ | 4 | 5 |
| Резюме≠ | Kernel density crime mapping turns a scatter of geocoded crime points into a smooth, continuous surface that shows where incidents concentrate. Each event is spread out over a small neighborhood by a kernel function, and the overlapping contributions are summed across a fine grid so that areas with many nearby crimes glow as peaks. Chainey, Tompson, and Uhlig (2008) showed that, among common hot-spot mapping techniques, kernel density estimation is one of the most accurate at predicting where future crime will occur, which is why it became the default crime-mapping surface in policing. | 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. |
| ScholarGateНабор от данни ↗ |
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