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

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400

350

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FIF 1 & 2 Combined100 200200 350200 400350 400

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400

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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;

novakk@umkc.eduAndrew Fox; 816-235-5955;

foxan@umkc.edu

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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