Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Interrupted Time Series in Crime Analysis× | Crime Prediction Modeling× | |
|---|---|---|
| Área | Criminology | Criminology |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1980 | 2011 |
| Autor original≠ | George E. P. Box & George C. Tiao (intervention analysis); David McDowall, Richard McCleary, and colleagues (criminological text) | George Mohler, Martin Short & colleagues (self-exciting point process) |
| Tipo≠ | Quasi-experimental evaluation of a policy effect on a time series | Forecasting model for the space-time risk of crime |
| Fonte seminal≠ | McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted Time Series Analysis. Sage Publications. ISBN: 9780803914933 | 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 ↗ |
| Outros nomes | Crime Intervention Analysis, ITS Crime Evaluation, Quasi-Experimental Time Series for Crime, Pre-Post Crime Trend Analysis | Predictive Policing, Crime Forecasting, Self-Exciting Point Process Crime Modeling, Predictive Crime Mapping |
| Relacionados | 4 | 4 |
| Resumo≠ | Interrupted time series (ITS) analysis evaluates whether a law, policy, or intervention changed the course of a crime series. By modeling the level and slope of crime before and after a dated 'interruption' — a gun-control law, a policing crackdown, a sentencing reform — it tests whether the series jumped or bent at that moment relative to its prior trend. Box and Tiao formalized intervention analysis in 1975, and McDowall, McCleary, and colleagues brought the method to criminology in their widely used 1980 monograph. | 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. |
| ScholarGateConjunto de dados ↗ |
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