using nibrs (and other data) for understanding the role of offender’s criminal history in violence...
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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
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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)
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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
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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
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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.
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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
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Current Project
Routine Activities / Criminal Opportunity Theoretical Model
Multi-level hierarchical data structure block group Incidents Individual offenders and victims
within the incident
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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
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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.
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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.
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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.
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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.
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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.
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Data Sources
MA NIBRS data for years 2006 – 2008 U.S. Census Bureau data for the year
2000. MA - Offender arrest history
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Dependent Measure
Police officers assaulted in the line of duty during 2006 to 2008 from MA jurisdictions reporting through the MA IBR system.
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The proposed research will use a three-tiered hierarchical data structure examining interactions between individuals within incidents within communities.
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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.
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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.
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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.
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The Dependent Variable is Law Enforcement Officers Killed or Assaulted in the line of Duty
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Within the proposed theoretical framework the project objectives are to develop a hierarchical statistical model to address the following research questions:
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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?
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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?
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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?
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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?
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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?
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0 5 10 15 20
leoka_incident
0
100
200
300
400
500F
req
ue
nc
y
Mean = 1.7608Std. Dev. = 3.23225N = 627
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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
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