方法对比

	Kernel Density Crime Mapping	Crime Mapping
领域	Criminology	Criminology
方法族	Process / pipeline	Process / pipeline
起源年份	2008	2005
提出者	Bernard Silverman (KDE); Spencer Chainey (crime mapping application)	Rachel Boba Santos, Spencer Chainey & Jerry Ratcliffe (modern synthesis)
类型	Nonparametric density estimation for crime surfaces	Geographic information analysis of crime locations
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.	Boba Santos, R. (2017). Crime Analysis with Crime Mapping (4th ed.). SAGE Publications. ISBN: 9781506331034
别名	KDE Crime Mapping, Crime Density Surface Mapping, Hot Spot Density Mapping, Kernel Smoothing of Crime Events	Geographic Crime Analysis, Crime Cartography, GIS Crime Mapping, Spatial Crime Analysis
相关	4	4
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.	Crime mapping is the practice of geocoding crime incidents to their locations and using geographic information systems (GIS) to visualize and analyze where crime concentrates. It spans simple pin maps, area-based choropleth maps, and continuous density surfaces, and underpins the geographic side of modern crime analysis — from CompStat briefings to problem-oriented policing.