Improving the Creation of Hot Spot Policing Patrol Routes: Comparing Cognitive Heuristic Performance to an Automated Spatial Computation Approach

Hot spot policing involves the deployment of police patrols to places where high levels of crime have previously concentrated. The creation of patrol routes in these hot spots is mainly a manual process that involves using the results from an analysis of spatial patterns of crime to identify the areas and draw the routes that police officers are required to patrol. In this article we introduce a computational approach for automating the creation of hot spot policing patrol routes. The computational techniques we introduce created patrol routes that covered areas of higher levels of crime than an equivalent manual approach for creating hot spot policing patrol routes, and were more efficient in how they covered crime hot spots. Although the evidence on hot spot policing interventions shows they are effective in decreasing crime, the findings from the current research suggest that the impact of these interventions can potentially be greater when using the computational approaches that we introduce for creating hot spot policing patrol routes.

Fuzzy Geographical Model for Visualizing Crimes Hot Spots

Crimes Hot spots are areas that have a greater than average number of criminal or disorder events. Recently, many researchers pay more attentions for detecting crime hot spots to allow police personnel to plan effectively for emergency response, determine mitigation priorities, analyze historical events, and predict future events. This paper introduces a fuzzy geographical model for detecting crimes hot spots. The proposed model has three main phases which are: (1) Pre-processing, (2) Fuzzification, and (3) Visualization. In pre-processing phase, the model uses statistical methods and cleansing techniques to clean the raw dataset. In Fuzzification phase, the number of crimes converts into linguistic value according to the hybrid (triangular and trapezoidal) membership function. In visualization phase, the results are visualized on GIS map with different colors based on the density of crime hot spot. This paper aims to rank the hotspot crime places in the country, so the decision-makers can be knowing accurately. Our dataset collected from Cairo crimes at year 2016 and the results of our approach suitable and has a good manner for the decision maker with high accuracy.

Does Collective Efficacy Matter at the Micro Geographic Level?: Findings from a Study Of Street Segments

Many scholars argue that collective efficacy is not relevant to understanding crime at the microgeographic level. We examine variation in collective efficacy across streets with different levels of crime in Baltimore City, MD, and, then, employ multilevel modelling to assess this relationship. We find that people who live in crime hot spots have much lower levels of collective efficacy than people who live in non-hot spot streets and that this relationship persists when controlling for a large number of potential confounders both at the street and community levels. These findings suggest the importance of collective efficacy both in understanding and controlling crime at microgeographic units.