Ken Novak, Ph.D. Andrew Fox, Ph.D. University of Missouri – Kansas City

1. Targeting Offenders and Network AnalysisPresentation at the 62nd Annual SPIAA Training ConferenceJuly 23, 2013 Ken Novak, Ph.D. Andrew Fox, Ph.D. University of Missouri – Kansas City

2. Overview of presentation Social Network Analysis (SNA) • What is it? • Why does it matter? • How do you do that? • How can it help?

3. What is SNA? • Analysis of social relationships • Beyond individual attributes • Map relationships between individuals • Information and goods flow between people, so the structure of relationships matters • Through SNA we can identify important individuals based on their social position

4. Why does SNA matter? • Theoretical support • Provides ability to focus scarce resources • Effectiveness • Efficiency • Equity • Aid in developing intervention on violent groups

5. Why does theory matter? • Most effective policies are informed by theory • Theory-guided practice increases effectiveness • Understanding why something works/doesn’t work • Why does a strategy work here but not there? • Ensures application of strategy is tailored to environment • Effective crime prevention is not ‘off the shelf’

6. Behavior and control • Crime is concentrated among individuals • These individuals frequently interact with each other • Crime and attitudes toward crime are learned in intimate groups • Peer influence • Justification for offending • Peer association matters

7. Factors for learning crime* • Whom does a person associate? • Balance between individuals in the network • Transference of deviant norms within network • Quality/strength of relationships This makes connections within social networks important to understand *Learning / Differential Association

8. Behavior and control • Groups have the ability to regulate behavior • Groups have norms for behavior, and the ability to reward and sanction • Social control • Formal – police, courts, corrections • Informal – Peers, parents, community, clergy • Goal: identifying social networks and convincing them to ‘police themselves’

9. Analysis • Challenge: Identification of violent networks • Approach: Social Network Analysis (SNA) • Examination of social relationships • Understand flow of information • Identification of which individuals are most important in a network • “Leveraging” influence of these individual • Post-hoc investigations

10. What’s the point? • Converting data into intelligence DATA MODEL-ING INTELLIGENCE Input Analysis Output

11. Data (input) • Information that connects or informs the relationship between 2+ people • Field Interrogation Forms • Arrest Reports • Car/Traffic Stops • “Street intel” • Gang intelligence reports • NationalIntegrated Ballistic Information Network

12. Data (a word of caution) • Intelligence will only be as good as the data used • Flawed, incomplete, stale, cursory data yield similar output

13. Terms • Sociogram: A picture in which are represented as points in two-dimensional space. The relationships between two people are represented by a line or an arrow. Sociograms are also referred to as graphs or network maps. • Node: In a graph, nodes represent the actors or people and are generally represented by a circle. • Tie: The link between two nodes in a sociogram is referred to as a tie.

14. SNA for Dummies Node

15. SNA: Sociogram Node Tie

16. Understanding group dynamics… • Focus resources • Deterrence, levers to pull • Holding members accountable for each other’s actions • Understanding informal social control • Network structure, properties SNA is a tool to graphically display group dynamics

17. Colorado Springs Sexual Contact Network SOURCE: James Moody. http://www.soc.sbs.ohio-state.edu/jwm/

18. The 9-11 Hijacker Network SOURCE: Valdis Krebs http://www.orgnet.com/

20. Advantages of Using SNA • Layout optimization • No lines on top of each other, clear layout • Space on the page to equal social distance • Identifying key players • Centrality as a measure of importance • Free software (Pajek and Excel)

21. Field Interview FIF 1 100 Andrew Fox 200 Ken Novak Edge 100 200 Network Representation 200 100 FIF 2 200 Ken Novak 350 Joe McHale 400 Tiffany Gillespie 350 200 200 350 200 400 350 400 400 FIF 1 & 2 Combined 100 200 200 350 200 400 350 400 200 100 350 400

22. Step 2 – Individuals who were mentioned in the Step 1 FI Cards who had not previously been mentioned Step 0- Gang Member Information Cards (GMIC) FI Cards and GMIC Step 1 – Individuals who were mentioned in the Step 0 GMIC or FI Cards

23. Network of gang members and associates (n= 288)

24. Who is most central in the network? Three types of centrality: • Degree Centrality • 2. Betweenness Centrality • 3. Eigenvector Centrality

25. Degree Centrality – Simply the number of ties a node has in the network. Degree centrality suggests that those who have the most ties are the most central to the network.

26. Betweenness Centrality – Those who are the intersection on many paths between others.

27. Eigenvector Centrality – Those who are connected to many connected people

28. Major Findings • Social network analysis using FI cards confirms findings about gangs and offers new insights about gang social structure • High turnover of gang networks (80% less than 1 year) • The line between cliques is fuzzy, might be more hybrid gangs than previously thought • Betweenness centrality identifies those most likely to be arrested

29. Figure 4.9: 2007 network with clique affiliations Key: Varrio Sixty First = Red; West Side Grandel = Blue; Varrio Clavalito Park = Green

30. KC Gang network sociogram

31. ATF / NIBIN IntelligenceNationalIntegrated Ballistic Information Network(these dots indicate linked gun crimes, yellow dots indicate cases involving homicides)

32. Training • Finding the right crime analysts • Giving them time and space to learn • Need to fully understand PD data systems and how to extract large amounts of data from those systems • Need to understand the concepts, not just the technique.

33. Thank YouQuestions?Contact information:Ken Novak; 816-235-1599; novakk@umkc.eduAndrew Fox; 816-235-5955; foxan@umkc.edu

34. Networks and geography

35. Center for Violence Prevention and Community Safety

36. Center for Violence Prevention and Community Safety

37. Degree centrality Center for Violence Prevention and Community Safety

38. Betweenness centrality Center for Violence Prevention and Community Safety