Using NIBRS (and other data) for Understanding the role of offender’s criminal history in violence against

the policeDonald Faggiani, University of Wisconsin Oshkosh

Daniel Bibel, MASS State Police

Overview of the Problem

Workplace Violence: Accounts for 18 percent of all violent violations (DOJ 1999)

The rate of killings and assaults of law enforcement while on the job is 3.7 times higher than for the next highest category (mental health employees) (Duhart 2001)

Annually, Based on 10 year average:

An average of 53 police officers were killed and 58,692 officers were assaulted each year from 1999 to 2008, making law enforcement the most victimized occupation in the United States

(Faggiani and Bibel, 2009; Fridell, et al. 2006; Duhart, 2001).

Overview of the Problem

The FBI estimates that the rate of assaults against law enforcement in the US is 11.9 per 100

Law enforcement is the most victimized occupation in the United States

(Fridell, et al. 2006; Duhart, 2001).

Overview of the Problem

Prior research on LEOKA

Focus only on murder of the police officer. Approximately 0.096% of all violence against the police

In general the analysis has been at the city, county, state, and national level

Only a few attempts to examine inter-city level, block group level.

Prior research on LEOKA

These previous studies miss the impact of inter-jurisdictional variations (such as variations in block group and neighborhood levels of analysis)

Quality and substance of available data Lack of Methodological rigor

Current Project

Routine Activities / Criminal Opportunity Theoretical Model

Multi-level hierarchical data structure block group Incidents Individual offenders and victims

within the incident

Project Data Using State (MA) level data

Selected 4 jurisdictions based upon population size, length of NIBRS reporting and consistency in NIBRS reporting

Includes address specific details

Collected Year 2000 Census data at the Block Group level

Collected arrest history data for offenders arrested

Objective

This research examines a critical and previously untested assumption about an offender’s criminal history as a motivating factor in the assault of a law enforcement officer.

Current Project

A key assumption in all prior research on LEOKA is that an offender’s criminal history plays a role in their actions during an arrest.

Current Project

If faced with a threat to their freedom (seen as the increased potential for arrest) the rational choice to some offenders may be to strike out against that threat to protect their freedom.

Current Project

The potential threat to their freedom, as the result of an increase in the police presence, may change the “potential yield” of the officer in the eyes of a motivated offender;

making the police a more attractive target.

Routine Activities Theory

Exposure to motivated offenders Participation in dangerous activities Proximity to areas of high rates of

offending Lack of effective guardianship Suitable target of some value to an

offender

Certain lifestyle risk factors can increase the likelihood of victimization, such as:

Cohen and Felson, 1979; Cohen, Kluegel and Land, 1981; Wilcox, Land and Hunt, 2003.

Data Sources

MA NIBRS data for years 2006 – 2008 U.S. Census Bureau data for the year

2000. MA - Offender arrest history

Dependent Measure

Police officers assaulted in the line of duty during 2006 to 2008 from MA jurisdictions reporting through the MA IBR system.

The proposed research will use a three-tiered hierarchical data structure examining interactions between individuals within incidents within communities.

Block Groups – will define the context within which the incidents occur. US Census data will be used to define the structural /contextual correlates of the

communities.

The inclusion of the middle tier, the incident details, can be critical for

understanding how the dynamics of the incident situation (current offense, time of day, weapon use and other factors)

might influence the opportunities associated with assaults against police.

Target and Control populationsPersons –

Samples of both LEOKA offenders and non-LEOKA offenders were selected.

Criminal history information on both groups has be collected.

In addition, demographic details such as age, gender, and race has also been

collected.

The Dependent Variable is Law Enforcement Officers Killed or Assaulted in the line of Duty

Within the proposed theoretical framework the project objectives are to develop a hierarchical statistical model to address the following research questions:

Q1: Within the context of a block group can variations in assaults of law enforcement officers be explained by variations in the contextual and structural dynamics of these block groups?

Q2: Within the dynamics of a criminal incident can variations in the characteristics of that incident, such as criminal offense, time of day, day of week, weapon use, number of offenders and other incident details account for variations in the likelihood of an officer being assaulted?

Q3: Within the individual interactions between an offender and a police officer can variations in offender demographics (age, race, gender) and an individual’s criminal history (number, type and seriousness of prior arrests) help to explain variation in the likelihood of assaults against law enforcement?

Q4: Within the individual interactions between an offender and a police officer can variations in offender demographics (age, race, gender) and an individual’s criminal history (number, type and seriousness of prior arrests) help to explain variations in the seriousness of the injuries caused by these assaults?

Q5: Within the context of a block group is the seriousness and extent of the criminal histories of LEOKA offenders different from other non-LEOKA assault offenders?

0 5 10 15 20

leoka_incident

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200

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500F

req

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Mean = 1.7608Std. Dev. = 3.23225N = 627

LEOKA and Non-LEOKA Assault Prior Arrests

Count of all prior incidents against offender

Mean Count of N Std. DeviationPrior Offenses

Target group 4.17 477 4.098Control group 3.86 429 3.722Total 4.02 906 3.925