Targeting Offenders and Network Analysis
Presentation at the 62nd Annual SPIAA Training Conference
July 23, 2013
Ken Novak, Ph.D.Andrew Fox, Ph.D.
University of Missouri – Kansas City
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Overview of presentation
Social Network Analysis (SNA)1.What is it?2.Why does it matter?3.How do you do that?4.How can it help?
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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
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Why does SNA matter?
• Theoretical support• Provides ability to focus scarce
resources– Effectiveness– Efficiency– Equity
• Aid in developing intervention on violent groups
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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’
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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
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Factors for learning crime*
1. Whom does a person associate?2. Balance between individuals in the
network3. Transference of deviant norms within
network4. Quality/strength of relationships
This makes connections within social networks important to understand
*Learning / Differential Association
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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’8
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
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What’s the point?
• Converting data into intelligence
DATAMODEL
-ING INTELLIGENCE
Input Output
Analysis
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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– National Integrated Ballistic Information
Network
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Data (a word of caution)
• Intelligence will only be as good as the data used
• Flawed, incomplete, stale, cursory data yield similar output
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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.
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SNA for Dummies
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Node
SNA: Sociogram
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TieNode
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
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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/ 19
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Advantages of Using SNA
1. Layout optimization» No lines on top of each other, clear layout» Space on the page to equal social distance
2. Identifying key players» Centrality as a measure of importance
3. Free software (Pajek and Excel)
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Field Interview
FIF 1100 Andrew Fox200 Ken Novak
Edge
100 200 100 200
Network Representation
FIF 2200 Ken Novak350 Joe McHale400 Tiffany Gillespie
200 350200 400350 400
200
400
350
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FIF 1 & 2 Combined100 200200 350200 400350 400
200
400
350
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
Step 2 – Individuals who were mentioned in the Step 1 FI Cards who had not previously been mentioned
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04/21/23
Network of gang members and associates (n= 288)
Who is most central in the network?
Three types of centrality:
1. Degree Centrality
2. Betweenness Centrality
3. Eigenvector Centrality
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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.
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Betweenness Centrality – Those who are the intersection on many
paths between others.
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Eigenvector Centrality – Those who are connected to many connected
people
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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
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Key: Varrio Sixty First = Red; West Side Grandel = Blue; Varrio Clavalito Park = Green
Figure 4.9: 2007 network with clique affiliations
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KC Gang network sociogram
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ATF / NIBIN IntelligenceNational Integrated Ballistic Information Network
(these dots indicate linked gun crimes, yellow dots indicate cases involving homicides)
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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.
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Thank You
Questions?
Contact information:Ken Novak; 816-235-1599;
[email protected] Fox; 816-235-5955;
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Networks and geography
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Center for Violence Prevention and Community Safety
Center for Violence Prevention and Community Safety
Center for Violence Prevention and Community Safety
Degree centrality
Center for Violence Prevention and Community Safety
Betweenness centrality