In recent years, with the intent to shed a light on contextual factors that correlate with the presence of specific crime categories, there has been a growing interest in the development of techniques that use spatial analysis programs to identify the areas in which crime occurs. One of these is certainly the methodology called 'Risk Terrain Modeling' (RTM) (Caplan et al., 2010), oriented to a strategic analysis of the context within which future offences could happen, integrating conceptual elements coming from the environmental criminology such as the ‘criminogenic triggers', to identify the areas of greatest concentration and diffusion of crime. In this regard, the present study aimed to investigate the predictive efficacy of the RTM through a real case study: the burglaries in the city of Ancona. In support of the pre-existing literature, the results of this research showed that the places where drug dealing, prostitution and finally the ATMs are concentrated make it possible to forecast up to 72.5% of burglaries in the first four months of 2018, identifying 87% of the prospectively vulnerable urban areas. Furthermore, this study shows that even in a confined space, the same risk factors can be combined in different ways, giving rise to areas of variable risk over time. In addition, these results provide a rather effective set of information to be potentially used by both the local community and the police forces to develop countermeasures aimed at tackling urban crime including burglaries, robberies, drug dealing and so on. A similar approach could also provide operators, policy-makers and local administrators with significant support to understand and counterattack other forms of criminal behavior committed by gangs or antisocial groups. In fact, it would guarantee the application of the RTM as a tool for a better predictive policing strategy aimed at both a deeper crime analysis level and a risk assessment that could be fundamental to forecast the areas with the highest risk of criminal conducts in the entire city.
|Titolo tradotto del contributo||[Autom. eng. transl.] Predictive Policing: predicting home thefts in the city of Ancona (IT) through the Risk Terrain Modeling Software (RTMDx)|
|Numero di pagine||17|
|Rivista||SICUREZZA, TERRORISMO E SOCIETÀ|
|Stato di pubblicazione||Pubblicato - 2018|
- Crime Prevention
- Prevenzione del Crimine
- Risk Terrain Modelling