The Predictive Policing Challenges of Near Repeat Armed Street Robberies

Abstract

New research methodologies like the near repeat phenomenon provide police with a potentially powerful predictive technique, if law enforcement possesses the capacity to capitalize on identified patterns in time. The current study examines armed street robbery data from Philadelphia in order to identify and quantify the existence of multiple-event near repeat chains. The impact of near repeat chains on the temporal stability of micro-level armed street robbery hot spots is also explored. The findings demonstrate that near repeat armed street robbery chains tend to be relatively short in terms of chain length, and the number of days between the initiation and termination of a chain is rarely longer than 7 days. These results suggest that if police are to proactively address short-term crime event predictions, a range of complex organizational and analytical capacities have to be in place. Furthermore, despite the fact that a number of hot spots were found to be primarily derived of near repeat events, the results show that the temporal stability of armed street robbery hot spots is not associated with the proportion of near repeat events within the hot spots, a finding supportive of long-term opportunity reduction measures.

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