The Hammer Gang: An Exercise in the Spatial Analysis of an Armed Robbery Series using the Probability Grid Method

Overall, Chris; Day, Gregory

doi:10.1002/9780470987193.ch7

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…There has also been a large amount of work on crime forecasting (i.e. [7,12]) though historically, this work has relied on spatio-temporal modeling of criminal behavior [3,21] or was designed to identify suspects for specific crimes [26,15]. None of this previous work was designed to identify future violent offenders nor did it leverage social network structure.…”

Section: Related Workmentioning

confidence: 99%

Early Identification of Violent Criminal Gang Members

Shaabani

Aleali

Shakarian

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Gang violence is a major problem in the United States accounting for a large fraction of homicides and other violent crime. In this paper, we study the problem of early identification of violent gang members. Our approach relies on modified centrality measures that take into account additional data of the individuals in the social network of co-arrestees which together with other arrest metadata provide a rich set of features for a classification algorithm. We show our approach obtains high precision and recall (0.89 and 0.78 respectively) in the case where the entire network is known and out-performs current approaches used by lawenforcement to the problem in the case where the network is discovered overtime by virtue of new arrests -mimicking real-world law-enforcement operations. Operational issues are also discussed as we are preparing to leverage this method in an operational environment.

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