方法对比

	Kernel Density Crime Mapping	空间时间核密度估计 (ST-KDE)
领域	Criminology	空间分析
方法族	Process / pipeline	Regression model
起源年份	2008	2010 (space-time extension); 1956 (KDE origin)
提出者	Bernard Silverman (KDE); Spencer Chainey (crime mapping application)	Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen
类型	Nonparametric density estimation for crime surfaces	Non-parametric density estimation
开创性文献	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.	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.
别名	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
相关	4	5
摘要	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.