Assessing the Utility of NIBRS Data American Society of Criminology 2017 Annual Meeting November 17, 2017 Philadelphia, PA Eman Abdu, Henry Gallo, Peter Shenkin and Doug Salane Center for Cybercrime Studies Mathematics & Computer Science Dept. John Jay College of Criminal Justice City University of New York
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Assessing the Utility of NIBRS DataAmerican Society of Criminology
2017 Annual MeetingNovember 17, 2017Philadelphia, PA
Eman Abdu, Henry Gallo, Peter Shenkin and Doug Salane
Center for Cybercrime StudiesMathematics & Computer Science Dept.
John Jay College of Criminal JusticeCity University of New York
Acknowledgements
Many students have contributed: Boris Bonderenko, Raul Cabrera and Henry Gallo
Inter‐university Consortium for Political and Social Research(ICPSR) and National Archive of Criminal Justice Data (NACJD)
FBI, Criminal Justice Information Services Division, UCR/NIBRS Groups
NSF, NASA and NIJ
GoalsProvide back ground on FBI’s National Incident‐Based Reporting
System (NIBRS)
Demonstrate utility of having NIBRS data in a relational data base (Oracle 12c)
Examine NIBRS data issues: nonresponse bias and extent of item missing data
Briefly discuss ongoing work
NIBRS Data Structure
• Group A offenses (46 crimes)– data on arrest, offense, offender, victim, property– data on incident (administrative)– 56 data elements in 6 main segments
• Group B offenses (11 crimes) – social crimes (victimless)– arrest data
NIBRS Data Structure• NIBRS Group A offenses – data in 6 major files or segments
• An incident can have multiple segments: victims, offenders, offenses, arrestees, property records
• Tied together by Agency Identifier (ORI) and incident number
• 13 Segment files 6 group A, 1 group B, 3 Windows files, 3 Batch Files
CODE DESCRIPTION • B Buying/Receiving • C Cultivating/Manufacturing/Publishing • D Distributing/Selling • E Exploiting Children • J Juvenile Gang Involvement • G Other Gang • N None/Unknown Gang Involvement • O Operating/Promoting/Assisting • P Possessing/Concealing • T Transporting/Transmitting/Importing • U Using/Consuming • I Intentional Abuse and Torture
Code Tables in NIBRS (Relationship Table)
CODE DESCRIPTION VO Victim was Offender
NA Not applicable AQ Victim was Acquaintance
SE Victim was Spouse FR Victim was Friend
CS Victim Common‐Law Spouse NE Victim was Neighbor
PA Victim was Parent BE Victim was Babysittee (the baby)
SB Victim was Sibling BG Victim was Boyfriend/Girlfriend
CH Victim was Child CF Victim was Child of Boyfriend / Girlfriend
GP Victim was Grandparent HR Homosexual Relationship
GC Victim was Grandchild XS Victim was Ex‐Spouse
IL Victim was In‐Law EE Victim was Employee
SP Victim was Stepparent ER Victim was Employer
SC Victim was Stepchild OK Victim was Otherwise Known
SS Victim was Stepsibling RU Relationship Unknown
OF Victim other family member ST Victim was Stranger
Code Tables in NIBRS (Bias Motivation)
• 11 Anti‐White • 12 Anti‐Black or African American • 13 Anti‐American Indian or Alaska Native • 14 Anti‐Asian • 15 Multi‐Racial Group • 21 Anti‐Jewish • 22 Anti‐Catholic • 23 Anti‐Protestant • 24 Anti‐Islamic (Moslem) • 25 Other Religion • 26 Multi‐Religious Group • 27 Atheism/Agnosticism • 31 Anti‐Arab • 32 Anti‐Hispanic or Latino • 33 Anti‐Not Hispanic or Latino • 41 Anti‐Male Homosexual (Gay)
Comparison of Larceny Details UCR and NIBRS 2014 Data
(offense counts)
UCR Data NIBRS Data
Larceny Type Counts percentages Counts percentagesPocket‐picking 27,465 0.54% 6,884 0.40%
Purse‐snatching 20,660 0.40% 5,653 .33%
shoplifting 1,097,444 21.47% 378,153 21.78%
From motor vehicles
(except accessories)
1,172,876 22.95% 358,120 20.62%
Motor vehicle
accessories
359,490 7.03% 79,794 4.60%
bicycles 184,575 3.61% 0 0%
From buildings 626,572 12.26% 225,598 12.98%
From coin‐operated
machines
11,728 .23% 3970 .23%
All others 1,610,734 31.51% 678,212 39.06%
Totals 5,111,544 100% 1,736,384 100%
Comparison of Larceny Details UCR and NIBRS 2015 Data
(offense counts)
UCR Data NIBRS Data Larceny Type Counts percentages Counts percentages
Pocket‐picking 28,532 0.5% 7,079 0.41%
Purse‐snatching 22,825 0.4% 5,433 .32%
shoplifting 1,273,656 22.32% 390,971 22.67%
From motor vehicles
(except accessories)
1,370,664 24.02% 372,031 21.58%
Motor vehicle accessories 399,444 7.0% 77,014 4.47%
bicycles 205,428 3.6% 0 0%
From buildings 663,648 11.63% 214,311 12.43%
From coin‐operated
machines
11,413 .2% 3,804 .22%
All others 1,730,735 30.33% 653,685 37.91%
Totals 5,706,345 100% 1,724,328 100%
Item Missing Data
• NIBRS has 53 data elements most of which are mandatory
• Data elements such as demographics of victim and offenders, relationships victim/offender and others are of interest to researchers and policy makers
• Compare rates of missing data in NIBRS and other sources such as SHR
2014 3596 11.43% 14.35% 12.26% 10.65% 8.79%2015 4234 13.51% 14.65% 14.27% 11.45% 9.54%1The unit of analysis is victims.
Ongoing Work
• Time series studies to examine NIBRS missing data, victim‐offender relationships, circumstances, location and weapon used
• Extract data for specific studies and make it available in Excell Pivot Tables or Data Cubes
• Examine effects of police reporting practices on the data, e.g., inaccurate incident times
• Prepare for additional NIBRS reporting. DOJ, OJP, BJS and FBI program to create a nationally representative crime sample and NIBRS compliant operational systems increasing NIBRS reporting. (Mainly an IT effort)
• Make the relational database publicly available through use of the Oracle Data Pump utility
Thank You
Eman Abdu, Henry Gallo, Doug Salane and Peter Shenkin