00 Arrest Networks and the Spread of Violent Victimization
- 1. Arrest Networks and the Spread of Violent Victimization DANIEL T. RAGAN UNIVERSITY OF NEW MEXICO
- 2. Other Team Members Lisa Broidy University of New Mexico Brian Soller University of Maryland, Baltimore County
- 3. Background Violent victimization: ‣ As a health outcome ‣ Not randomly distributed ‣ Importance of social ties
- 4. Prior Research Violent victimization: Predicted by arrest with other victims ‣ Exposure to similar risk factors Gunshot and homicide victimization ‣ Generalizability to more common types of violence? Chicago, IL; Boston, MA; Newark, NJ ‣ Generalizability to different geographic locations?
- 5. Our Goal: Network Approach to Violent Victimization Primary data ‣ Law enforcement Supplementary data ‣ Criminal courts ‣ State criminal history databases ‣ Emergency room visits ‣ U.S. Census
- 6. Pilot Project Violent victimization in criminal networks ‣ Approximately three years of crime-related data ‣ Information on both offenders and victims ‣ Albuquerque, New Mexico
- 7. Current Goals 1) What can we learn about violence in criminal networks? ‣ Do offending networks link violent offenders and victims? 2) Assess feasibility of a larger project ‣ Do these data support a network approach?
- 8. Data: Overview ‣ Criminal event data ‣ Case numbers ‣ Suspects, arrestees, and victims: Unique identifiers ‣ Approximately three years of coverage ‣ 2011 into 2014 ‣ Albuquerque, New Mexico
- 9. Albuquerque ‣ Population size ‣ Mobility ‣ City borders & Law enforcement jurisdiction
- 12. Data at a Glance: Cases Records (case/person combination): 155,906 ‣ Cases: 120,675 ‣ Single offender: 45,348 ‣ Multiple offenders: 4,150
- 13. Data at a Glance: People Unique person identifiers: 120,345 ‣ Offenders (suspects/arrestees): 40,858 ‣ Multiple cases: 8,441 ‣ Victims: 86,677 ‣ Both offender and victim: 7,190
- 14. Violent Offenses: Examples Battery/Aggravated battery/Aggravated assault ‣ 8,946 cases Robbery/Commercial armed robbery/Auto car jacking ‣ 552 cases Forcible rape/Attempted rape/Sexual assault with an object ‣ 490 cases Murder and nonnegligent manslaughter/Justifiable homicide ‣ 83 cases
- 15. Building the Network Offenders: Linked through crimes ‣ One-mode projection from two-mode data ‣ Assumption: Co-offending signifies social connection ‣ Case removal: > 10 offenders ‣ Case removal: Individual is both offender and victim ‣ Time dimension collapsed: Cross-sectional
- 16. Offender Network: Offenders Offenders: 40,499 ‣ Isolates: 32,637 ‣ Connected offenders: 7,862
- 17. Offender Network: Ties Ties: 5,990 ‣ Average ties: 0.30 ‣ Average ties, excluding isolates: 1.52 ‣ Degree distribution: 1 2 3 4 5 6 7 8 - 18 5,476 1,444 534 219 81 67 27 14
- 18. Offender Network: Groups Components, excluding isolates: 3,151 • Average component size, excluding isolates: 2.50 • Component distribution: 2 3 4 5 6 7 8 9 - 53 2,364 488 151 54 34 22 18 20
- 20. How are different offender-types connected? Do victims of violence have ties to violent offenders? ‣ Do links within offending network suggest “spread”?
- 21. How are different offender-types connected? Non-victim Victim Non-violent offender Non-violent offender, non-victim1 Non-violent offender, victim3 Violent offender Violent offender, non-victim2 Violent offender, victim4
- 22. Non-violent offender, non-victim1 Violent offender, non-victim2 Non-violent offender, victim3 Violent offender, victim4 Non-violent offender, non-victim1 001001 001102 001013 001114 Violent offender, non-victim2 102001 102102 102013 102114 Non-violent offender, victim3 013001 013102 013013 013114 Violent offender, victim4 114001 114102 114013 114114
- 23. Non-violent offender, non-victim1 Violent offender, non-victim2 Non-violent offender, victim3 Violent offender, victim4 Non-violent offender, non-victim1 0 Violent offender, non-victim2 0 0 Non-violent offender, victim3 0 013102 0 Violent offender, victim4 0 114102 114013 114114
- 26. Exponential Random Graph Models Y: Random variable for state of network g(y): Vector of network statistics for model 𝜃: Vector of parameters k: Normalizing constant Conditional log-odds of a single tie between two actors: 𝑝 𝑌 = 𝑦 = 𝑒𝑥𝑝(𝜃′ 𝑔 𝑦 ) 𝑘 𝜃 𝑙𝑜𝑔𝑖𝑡(𝑌𝑖𝑗 = 1|𝑦𝑖𝑗 𝑐 ) = 𝜃′𝛿(𝑦𝑖𝑗)
- 27. Exponential Random Graph Models Parameter Estimate Std. Error Edges -9.891*** 0.032 Isolates 1.695*** 0.027 Actor covariate: Number offenses 0.159*** 0.004 Attributes mixes: Offender type Non-violent offender, victim3 x Violent offender, victim4 0.205 0.147 Violent offender, non-victim2 x Non-violent offender, victim3 -0.626*** 0.111 Violent offender, non-victim2 x Violent offender, victim4 0.617*** 0.070 Violent offender, victim4 x Violent offender, victim4 1.138*** 0.146
- 28. Non-violent offender, non-victim1 Violent offender, non-victim2 Non-violent offender, victim3 Violent offender, victim4 Non-violent offender, non-victim1 0 Violent offender, non-victim2 0 0 Non-violent offender, victim3 0 — 0 Violent offender, victim4 0 + n.s. +
- 29. Conclusions 1) Presence of offender network ‣ Ties: Sparse but not insignificant 2) Clustering of violence within network ‣ Violent offenders and victim ties: Victims are violent ‣ “Selection” vs. “Spread”
- 30. Next Steps 1) Continuing work: ERGMs ‣ Model specification & Goodness of fit 2) Pilot project: Future directions ‣ Projection of crime network 3) Larger project feasibility ‣ Current mood: “Cautiously optimistic”