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.
Изворни запис
Цитирани радови су копирани дословно из изворног записа методе. Из њих се не изводи верификација на нивоу тврдње.
- 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
- 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
Куроване тврдње
Тврдње су сачуване у регистру доказа, свака са својом проценом.
Овај приказ не измишља процену тврдње када регистар нема ниједну.
Сродне методе
Генерисано из графа метода и приказано као машински предложене везе — не изводи се тврдња доказа.