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Process / pipelinePredictive policing / crime forecasting

Crime Prediction Modeling

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.

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Kilder

  1. 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. DOI: 10.1198/jasa.2011.ap09546
  2. Perry, W. L., McInnis, B., Price, C. C., Smith, S. C., & Hollywood, J. S. (2013). Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. RAND Corporation. ISBN: 9780833081483

Sådan citerer du denne side

ScholarGate. (2026, June 22). Predictive Modeling of Crime Risk (Predictive Policing). ScholarGate. https://scholargate.app/da/criminology/crime-prediction-modeling

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ScholarGateCrime Prediction Modeling (Predictive Modeling of Crime Risk (Predictive Policing)). Hentet 2026-06-24 fra https://scholargate.app/da/criminology/crime-prediction-modeling · Datasæt: https://doi.org/10.5281/zenodo.20539026