social network analysis

This webinar will present the Milwaukee, Wisconsin Police Department’s novel and integrated Networks of Criminals (NOC) approach. This analytic approach integrates criminal history data, threat intelligence, and social network analysis to identify youth offenders that are believed to be substantially impacting motor vehicle and robbery trends. This unique, three-dimensional approach is a foundation for collaboration between crime analysts and investigative officers.

This webinar will discuss two social network analysis (SNA) projects that the Philadelphia, Pennsylvania Police Department (PD) undertook. The first project examined the extent of shared connections among shooting victims through network analysis; in particular, the analysis examined cross-divisional connections by combining the network analysis and geographic information systems (GIS). The second project applied SNA to understand connections among gangs at the group-level across the city.

Crime and violence are highly socially connected. As criminal justice practitioners continue to learn about the small percent of the population responsible for the majority of violence, they have to use data analysis tools to focus resources (prevention, intervention, and enforcement) on the small world of people at high risk for being involved in violence, either as offenders or victims. This webinar will examine how social network analysis (SNA) can be applied to criminal justice data to better understand the small world of violence.

The Networks of Criminals (NOC) Youth Offender Program is an innovative offender-based strategy and intelligence process that utilizes an algorithmic methodology to identify youth offenders and their social networks currently impacting Milwaukee’s motor vehicle theft and robbery trends. NOC offenders are identified every week using a unique methodology for the comparative analysis of up to 500 emerging or perpetual like-offenders based on their 1) criminal history, 2) threat assessment, and 3) social network analysis.