Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Crime Prediction Modeling× | Risk Terrain Modeling (Criminology)× | |
|---|---|---|
| Fagområde | Criminology | Criminology |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår | 2011 | 2011 |
| Ophavsperson≠ | George Mohler, Martin Short & colleagues (self-exciting point process) | Joel Caplan & Leslie Kennedy |
| Type≠ | Forecasting model for the space-time risk of crime | Spatial risk-factor aggregation model for crime forecasting |
| Oprindelig kilde≠ | 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 ↗ | Caplan, J. M., Kennedy, L. W., & Miller, J. (2011). Risk terrain modeling: Brokering criminological theory and GIS methods for crime forecasting. Justice Quarterly, 28(2), 360–381. DOI ↗ |
| Aliasser | Predictive Policing, Crime Forecasting, Self-Exciting Point Process Crime Modeling, Predictive Crime Mapping | RTM, Risk Terrain Analysis, Environmental Risk Factor Modeling, Spatial Risk Factor Modeling |
| Relaterede | 4 | 4 |
| Resumé≠ | 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. | Risk Terrain Modeling (RTM) represents crime risk as a function of the environment: it identifies the features of a landscape — bars, bus stops, vacant lots, pawn shops, schools — that attract or generate crime, maps each one's spatial influence as a separate risk layer, and combines those layers onto a raster of place to produce a relative risk surface. Introduced by Joel Caplan and Leslie Kennedy around 2011, RTM 'brokers' environmental criminology theory and GIS methods so that crime forecasting rests on the qualities of places rather than on the history of crime alone. |
| ScholarGateDatasæt ↗ |
|
|