Social Network Analysis in Predictive Policing: Concepts, by Mohammad A. Tayebi, Uwe Glässer PDF

Social Network Analysis in Predictive Policing: Concepts, by Mohammad A. Tayebi, Uwe Glässer PDF

By Mohammad A. Tayebi, Uwe Glässer

This booklet makes a speciality of functions of social community research in predictive policing. facts technological know-how is used to spot capability criminality by means of reading the relationships among offenders to completely comprehend felony collaboration styles. Co-offending networks―networks of offenders who've dedicated crimes together―have lengthy been famous by means of legislations enforcement and intelligence corporations as a significant factor within the layout of crime prevention and intervention thoughts. regardless of the significance of co-offending community research for public defense, computational equipment for studying large-scale legal networks are quite untimely. This e-book widely and systematically reports co-offending community research as potent software for predictive policing. The formal illustration of criminological strategies awarded the following permit laptop scientists to contemplate algorithmic and computational suggestions to difficulties lengthy mentioned within the criminology literature. for every of the studied difficulties, we begin with well-founded strategies and theories in criminology, then suggest a computational approach and at last supply a radical experimental review, in addition to a dialogue of the consequences. during this method, the reader could be capable of research the total strategy of fixing real-world multidisciplinary problems.

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Additional resources for Social Network Analysis in Predictive Policing: Concepts, Models and Methods

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The specific characteristic of a group is that there is 24 3 Structure of Co-offending Networks a higher degree of connectivity inside the group than entities outside the group. Nowadays, studying the behavior of criminal groups becomes more important. In the last decade there have been more and more experimental studies into criminal activities that need specific forms of collaboration and organization [7]. For detecting these types of collaboration we need to mathematically formalize concepts such as offender group, gang, organized crime, and corporate crime and then design efficient algorithms for this purpose.

Clique is an example for these types of communities, which works based on complete mutuality in the group. Clique is a maximal complete subgraph of nodes in which every two nodes are connected by an edge. It is NP-complete to identify the maximum clique in a graph. One brute-force approach to find the maximal clique is to scan all nodes in the network. Then, for each node the maximal clique including that node is detected. Each node is removed from the network when it is traversed once. This method works for smallscale networks, but it is impractical for large-scale networks.

In contrast, actors with low degree are peripheral in the network and these actors are not active in the connection process. 7) where |Γv | is the number of direct neighbors of v, and N is the number of actors in the network. Closeness Centrality The main idea behind the closeness centrality is that actors that can quickly contact other actors in the network take the central position. The closeness centrality of an actor in a social network is the inverse of the average shortest path distance from the actor to any other actor in the network.

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