Measurement of repeat effects in Chicago’s criminal social network

• Main Authors: Paul Kump, David Haro Alonso, Yongyi Yang, Joseph Candella, Jonathan Lewin, Miles N. Wernick
• Format: Article
• Language: English
• Published: Emerald Publishing 2016-07-01
• Series: Applied Computing and Informatics
• Subjects: Crime analysis; Social network analysis; Violent crime; Predictive policing
• Online Access: http://www.sciencedirect.com/science/article/pii/S221083271600003X

The “near-repeat” effect is a well-known criminological phenomenon in which the occurrence of a crime incident gives rise to a temporary elevation of crime risk within close physical proximity to an initial incident. Adopting a social network perspective, we instead define a near repeat in terms of geodesic distance within a criminal social network, rather than spatial distance. Specifically, we report a statistical analysis of repeat effects in arrest data for Chicago during the years 2003–2012. We divide the arrest data into two sets (violent crimes and other crimes) and, for each set, we compare the distributions of time intervals between repeat incidents to theoretical distributions in which repeat incidents occur only by chance. We first consider the case of the same arrestee participating in repeat incidents (“exact repeats”) and then extend the analysis to evaluate repeat risks of those arrestees near one another in the social network. We observe repeat effects that diminish as a function of geodesic distance and time interval, and we estimate typical time scales for repeat crimes in Chicago.