“On to the next one:” Using social network data to inform police target prioritization

As part of the portfolio of strategies used to achieve crime reductions, law enforcement agencies routinely establish a list of offenders to be targeted as priorities. Rarely considered, however, is the fact that targets are embedded in larger social networks. These networks are a rich resource to be exploited as they facilitate: 1) efficient prioritization by understanding which offenders have access to more resources in the network, and 2) assessments of the impact of intervention strategies. Drawing from law enforcement data, the personal networks of two mutually connected police targets from a mid-size city in British Columbia, Canada were constructed. Results show that of the 101 associates in their combined network, 50 percent have a crime-affiliated attribute. The network further divides into seven distinct communities, ranging from four to 25 members. Membership to these communities suggests how opportunities, criminal and non-criminal, form and are more likely to occur within one’s immediate network of associates as opposed to the larger network. As such, seven key players that have the highest propensity to facilitate crime-like behaviours are identified via a measure of “network capital,” and located within the communities for informed target selection.