方法对比

	Crime Prediction Modeling	Crime Mapping
领域	Criminology	Criminology
方法族	Process / pipeline	Process / pipeline
起源年份	2011	2005
提出者	George Mohler, Martin Short & colleagues (self-exciting point process)	Rachel Boba Santos, Spencer Chainey & Jerry Ratcliffe (modern synthesis)
类型	Forecasting model for the space-time risk of crime	Geographic information analysis of crime locations
开创性文献	Mohler, G. O., Short, M. B., Brantingham, P. J., Schoenberg, F. P., & Tita, G. E. (2011). Self-exciting point process modeling of crime. Journal of the American Statistical Association, 106(493), 100–108.	Boba Santos, R. (2017). Crime Analysis with Crime Mapping (4th ed.). SAGE Publications. ISBN: 9781506331034
别名	Predictive Policing, Crime Forecasting, Self-Exciting Point Process Crime Modeling, Predictive Crime Mapping	Geographic Crime Analysis, Crime Cartography, GIS Crime Mapping, Spatial Crime Analysis
相关	4	4
摘要	Crime prediction modeling forecasts where and when crime is most likely to occur next, so that limited resources can be directed before incidents happen rather than after. It spans simple historical hot-spot extrapolation, statistical self-exciting point processes that treat crimes as triggering further crimes, and modern machine-learning models that blend spatial, temporal, and environmental features. The statistical foundation was sharpened by Mohler and colleagues' 2011 demonstration that earthquake-style self-exciting (Hawkes) point processes — in which each crime raises the short-term risk of nearby crimes — forecast urban crime more accurately than conventional hot-spot maps.	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.