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| Crime Prediction Modeling× | Near-Repeat Analysis× | |
|---|---|---|
| Field | Criminology | Criminology |
| Family | Process / pipeline | Process / pipeline |
| Year of origin≠ | 2011 | 2003 |
| Originator≠ | George Mohler, Martin Short & colleagues (self-exciting point process) | Michael Townsley, Shane Johnson & Kate Bowers |
| Type≠ | Forecasting model for the space-time risk of crime | Space-time clustering test for crime contagion |
| Seminal source≠ | 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 ↗ | Townsley, M., Homel, R., & Chaseling, J. (2003). Infectious burglaries: A test of the near repeat hypothesis. British Journal of Criminology, 43(3), 615–633. DOI ↗ |
| Aliases | Predictive Policing, Crime Forecasting, Self-Exciting Point Process Crime Modeling, Predictive Crime Mapping | Near Repeat Calculator Method, Space-Time Near-Repeat Analysis, Near-Repeat Victimization, Contagion Crime Pattern Analysis |
| Related | 4 | 4 |
| Summary≠ | 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. | Near-repeat analysis tests whether crimes cluster in space and time beyond chance: after a crime occurs, are nearby locations at elevated risk for a short period? Developed in the early 2000s by Townsley, Johnson, Bowers and colleagues for burglary, it formalizes the 'contagion' or 'communicable disease' pattern of crime using a Knox space-time test against a Monte Carlo reference distribution. |
| ScholarGateDataset ↗ |
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