Predicting Violence in Merseyside: a Network-Based Approach Using No Demographic Information

Abstract

Abstract: Purpose: We explore how we can best predict violent attacks with injury using a limited set of information on (a) previous violence, (b) previous knife and weapon carrying, and (c) violence-related behaviour of known associates, without analysing any demographic characteristics. Data: Our initial data set consists of 63,022 individuals involved in 375,599 events that police recorded in Merseyside (UK) from 1 January 2015 to 18 October 2018. Methods: We split our data into two periods: T1 (initial 2 years) and T2 (the remaining period). We predict “violence with injury” at time T2 as defined by Merseyside Police using the following individual-level predictors at time T1: violence with injury; involvement in a knife incident and involvement in a weapon incident. Furthermore, we relied on social network analysis to reconstruct the network of associates at time T1 (co-offending network) for those individuals who have committed violence at T2, and built three additional network-based predictors (associates’ violence; associates’ knife incident; associates’ weapon incident). Finally, we tackled the issue of predicting violence (a) through a series of robust logistic regression models using a bootstrapping method and (b) through a specificity/sensitivity analysis. Findings: We found that 7720 individuals committed violence with injury at T2. Of those, 2004 were also present at T1 (27.7%) and co-offended with a total of 7202 individuals. Regression models suggest that previous violence at time T1 is the strongest predictor of future violence (with an increase in odds never smaller than 123%), knife incidents and weapon incidents at the individual level have some predictive power (but only when no information on previous violence is considered), and the behaviour of one’s associates matters. Prior association with a violent individual and prior association with a knife-flagged individual were the two strongest network predictors, with a slightly stronger effect for knife flags. The best performing regressors are (a) individual past violence (36% of future violence cases correctly identified); (b) associates’ past violence (25%); and (c) associates’ knife involvement (14%). All regressors are characterised by a very high level of specificity in predicting who will not commit violence (80% or more). Conclusions: Network-based indicators add to the explanation of future violence, especially prior association with a knife-flagged individual and association with a violent individual. Information about the knife involvement of associates appears to be more informative than a subject’s own prior knife involvement.