A Systematic Approach for Investigating
By Jennifer
A. Johnson, Ph.D., John David Reitzel, Ph.D., Bryan F. Norwood, David M. McCoy,
D. Brian Cummings, and Renee R. Tate
Social
network analysis (SNA) is often confused with social networking sites, such as
Facebook, when in fact, SNA is an analytical tool that can be used to map and
measure social relations. Through quantitative metrics and robust visual
displays, police can use SNA to discover, analyze, and visualize the social networks
of criminal suspects.
SNA, a
social science methodology, serves as a valuable tool for law enforcement.
While technologically sophisticated, SNA proves easy to employ. Using available
data, police departments structure the examination of an offender’s social
network in ways not previously possible.
Social
network analysis provides a systematic approach for investigating large amounts
of data on people and relationships.
Manual
examination of social networks tends to be difficult, time consuming, and
arbitrary, making it more prone to error. SNA provides a systematic approach
for investigating large amounts of data on people and relationships. It
improves law enforcement effectiveness and efficiency by using complex
information regarding individuals socially related to suspects. This often
leads to improved clearance rates for many crimes and development of better
crime prevention strategies.
SNA derives
its value from human organization and social interaction for criminal and
noncriminal purposes. Social networks sometimes promote illegal behavior (e.g.,
juvenile delinquency and gang-related crime) among related offenders across
criminal domains. They can provide a source for illicit drug and pornography
distribution and international terrorism.1 The networks may supply an essential
first condition for many serious criminal behaviors.
Social
networks that enable crime are not mutually exclusive from the networks of law-
abiding citizens. They are interspersed within these communities, drawing
support from residents and extracting significant costs from host
neighborhoods.2 The influence of social networks in producing criminal behavior
indicates that effective crime-fighting strategies are contingent upon law
enforcement’s ability to identify and respond appropriately to the networks
where the behavior is embedded.
Theory and
Method
SNA is a
theory about how humans organize and a method to examine such organization. The
approach indicates that actors are positioned in and influenced by a larger
social network. Methodologically, it provides a precise, quantitative tool
through which agencies can identify, map, and measure relationship patterns.
Three points
of data—two actors and the tie or link between them—comprise the basic unit of
analysis. Actors “nodes” are people, organizations, computers, or any other
entity that processes or exchanges information or resources. Relationships
“ties, connections, or edges” between nodes represent types of exchange, such
as drug transactions between a seller and buyer, phone calls between two
terrorists, or contacts between victims and offenders. SNA focuses on both
positive and negative relationships between sets of individuals.
This
analysis produces two forms of output, one visual and the other mathematical. The
visual consists of a map or rendering of the network, called a social network
diagram, which displays the nodes and relationships between them. In larger
networks, key nodes are more difficult to identify; therefore, the analysis
turns to the quantitative output of SNA.
The
centrality of nodes, such as those representing offenders, identifies the
prominence of persons to the overall functioning of the network. It indicates
their importance to the criminal system, role, level of activity, control over
the flow of information, and relationships. Basic centrality metrics provide
further details. “Degree” gauges how many connections a particular node
possesses, “betweenness” measures how important it is to the flow, and
“closeness” indicates how quickly the node accesses information from the
network. Nodes are rank ordered according to their centrality, with those at
the top playing the most prominent role. These measures cannot tell an analyst
what the structure should be, but they can elaborate on the actual makeup of
the network. The value and actionable intelligence of each of these metrics is
determined by the information the analyst wants.
Case Study
Social
networks that enable crime are not mutually exclusive from the networks of
law-abiding citizens.
In January
2008 a collaborative pilot project was launched to explore the viability of
incorporating SNA into the precinct-level crime analysis methodologies of the
Richmond, Virginia, Police Department (RPD). Participants included
representatives of RPD, a university sociologist, and a software designer. The
goal was for the research team, comprised of the sociologist and the software
designer, to use crime data to assess how constructive SNA would be in solving
the most prevalent crimes in the area and to determine the feasibility of
training the precinct-level analysts to incorporate it into their workflow.
Researchers
needed to determine what initiated violence between two groups of previously
friendly young males. Several persons of interest, at one time on good terms,
began to argue and assault one another. The source of the violence was not
clear, and police were looking for ways to respond. They wanted to know if SNA
could help them understand what sparked the violence and which strategies could
be developed using a network approach.
The research
team received access to RPD’s records management system to obtain information
on criminal occurrences, arrests, criminal associates, demographics, and
victim/offender relationships. The police provided no other background
information on the individuals. The research team did not meet or discuss the
ongoing investigation with the detectives. Analysis was done off-site, and the
only recurring contact was with the police manager to extract the data in
relational form.
Using 24
persons of interest labeled by a gang unit detective as “seeds”—starting
points, or initially identified persons—the records management system extracted
all connections among the seeds from 2007 through October 2008, proceeding four
layers out and including any interconnections among the seed and nodes in each
step. The connections were categorized by incident type—common incident
participation, victim/offender, gang memberships, field contacts, involved
others, common locations, and positive or negative connections.
Positive
ties included a cooperative relationship between individuals, such as having
family connections, robbing a store together, or hanging out. Negative ties
indicated hostile relationships, such as those between a victim and offender.
Individuals could have multiple and varying connections. Four networks resulted
from the sampling, one for each layer out from the seeds. The networks included
the seeds, the relationships among them, people directly connected to them, and
those related to their associates. This involved 434 individuals and 1,711
ties. Several weak spots existed where a single node connected regions of the
network and indicated dense areas of heavy interconnectivity.
Using SNA
software, an analyst quickly produced a visual representation, including names,
to assess the structure of the group or reference to whom a person of interest
was connected. Through visual analysis and examination of the metric of
betweenness, analysts located the source of the disagreement. The metric
pointed to critical junctures in the network that revealed interpersonal
tensions among males revolving around their relationships with females.
Two powerful
male gang members reportedly had a positive relationship in October of 2007;
however, in April 2008, one victimized a female friend of the other. During the
same incident, this male also victimized the female friend of another male.
Throughout the episode, a pattern emerged involving situations where a dominant
male engaged with a female associate of another strong male. In other words,
boys were fighting over girls.
Quantitative
metrics provided additional information identifying the powerful players in
this network. By rank ordering the individuals according to their centrality
measures, the analysis confirmed that the gang unit was watching the right
people and using community resources effectively. The metrics also helped unit
members further analyze the importance of the seed nodes. Many of the nodes
targeted by the unit ranked as powerful in the network based on an SNA metric.
The quantitative metrics indicated six other vital players, including one
critical to the flow of the network.
Results
Using social
network analysis (SNA) software, an analyst can produce a visual representation
to assess the structure of a group or reference to whom a person of interest is
connected.
Unanticipated
administrative processes delayed the timeline of the pilot project, making
these results and recommendations too late to be actionable. The police already
had solved the conflict. The knowledge of the detectives, which the research
team was not privy to, validated the results. Officers confirmed that the
answer the research team had discerned from the data—boys fighting over
girls—was the cause of the conflict.
Detectives
acknowledged that they would have solved the case more quickly and easily if
they had this analysis available to guide their strategies. This feedback
validated the worth of the approach and the usefulness of SNA and moved the
project into the next phase. Precinct-level crime analysts received training in
SNA through a 36-hour, in-house seminar. Through lectures and hands-on
training, crime analysts from police and federal agencies used data from their
own projects to learn to incorporate SNA to meet their needs. Within 2 weeks of
completing the course, the analysts used SNA in several cases, including an
aggravated assault/ shooting and several convenience store robberies.
In the
shooting incident, the analyst used SNA to provide data on an associate of the
suspect who previously was not noticed by the detective working the case. The
analyst provided that information to the detective who used it to locate and
interview that individual, which put additional pressure on the suspect, who
was attempting to elude capture. This, combined with other social and financial
pressures, caused the suspect to surrender.
Another case
involved a string of convenience store robberies. Using an SNA map of a
separate case, an analyst noticed a connection between a person of interest in
the robberies in one precinct and a member of the network under investigation.
Using the two names as seeds, the analyst extracted another previously unknown
network. The analyst and a colleague identified one of the seed names as a
person of interest in robberies involving multiple juveniles. Through
cooperation and an SNA social diagram, they pieced together robberies not
previously thought to be connected and identified a suspect involved in other
robberies. The chart provided a source where they quickly, easily, and
effectively could share observations with investigative personnel.
Social
Network Diagrams
Social
network diagrams have become a method for RPD to use social relationships among
offenders and their associates. Renowned for its technological innovation and
policing strategies, RPD has found SNA effective in facilitating better
communication between crime analysts and investigators. SNA enabled the
department to significantly increase crime clearance rates and reduce violence.
Prior to the
SNA training, analysts conceptualized a series of “star” networks with an “ego”
at the center and immediate connections radiating out. To understand the
network, analysts identified the immediate connections of a person of interest.
To identify one of the people connected to the original person of interest, a
second ego network was constructed. In the end, the analyst faced a series of
networks, leaving out the interconnections between them. Through training,
analysts began to interpret the effectiveness of the larger network environment
using diagrams as social maps to orient themselves and officers.
In the
shooting case, the detective used the network analysis to apply pressure to the
suspect by interviewing an associate whose relationship with the offender
previously was unknown. Mounting social and financial pressures, ultimately,
led the individual to surrender. In the convenience store robberies, an SNA
diagram provided vital clues that allowed multiple analysts to share
information and identify previously unknown connections between individuals,
which led to a possible suspect. If SNA had been available to analysts in other
jurisdictions, a connection may have been discovered earlier.
Analysis
The cases
described illustrate the success of SNA in developing law enforcement
strategies and interdiction techniques. The pilot project demonstrated how SNA
can help answer sophisticated questions regarding motivations for a crime—an
area previously underdeveloped in crime analysis processes.3 The research team
was asked to determine why violence occurred among groups who previously were
amicable. Using visual analysis and without any subject matter knowledge,
investigators used SNA to reveal behavioral motivation rooted in complex
interpersonal relationships. The project provided confirmation of the
effectiveness of the current resource allocation of the gang unit and indicated
new avenues of policing, which have the potential to produce a high return on
investment.
These two
cases produced actionable results, illustrating how SNA can facilitate a
productive working relationship between crime analysts and detectives. The
academic research on policing indicated that one of the biggest hurdles in
establishing effective communication is finding a common language between the
analytics of numbers and the immediate pressures of reality.4 Each case
described illustrates how SNA and social network diagrams function as a common
ground. Analysts used the charts visually to depict their analysis, which
resonated with detectives because it reflected their reality. The analysts
provided something new to the detectives, thus, aiding each investigation.
The visual
and quantitative output of SNA helps solve institutional memory issues
associated with analysts’ longevity and attrition, as well as new hires. By
producing a current overview, SNA allows new analysts to grasp the present
status of the network. It assists experienced analysts in maintaining an
understanding of the network by chronicling growth and development as members
and connections appear and disappear.
Law
enforcement agencies, such as RPD, benefit from having access to structured,
relational, and temporal data. Analysts reliably map changes in the network
using an automated extraction process. Through this dynamic procedure,
experienced analysts appear less likely to develop data analysis blind spots.
Conclusion
Law
enforcement agencies have come a long way from pinpoint mapping. The
technological advancements in recent years can provide personnel more
confidence to handle complex crime problems confronting departments around the
country. Social network analysis demonstrated its utility and effectiveness as
a means of solving crimes or determining persons of interest and bridging the
gap between crime analysts and police officers in the field. With the support
of robust technology, SNA becomes reliable across time, data, analysts, and
networks and quickly produces actionable results inside any operational law enforcement
environment.
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The authors
commend and recognize the Richmond, Virginia, Police Department’s Crime
Analysis Unit for its critical role and ongoing cooperation in the research and
writing of this article.
Dr. Johnson
is an associate professor of sociology at Virginia Commonwealth University in
Richmond.
Dr. Reitzel
is an assistant professor of criminal justice at Virginia Commonwealth
University in Richmond.
Mr. Norwood
retired as chief of the Richmond, Virginia, Police Department.
Chief McCoy
serves as the associate vice president of public safety and chief of police
with the University of Richmond Police Department, Richmond, Virginia.
Mr. Cummings
manages the planning division of the Richmond, Virginia, Police Department.
Ms. Tate is
the crime analysis supervisor for the Richmond, Virginia, Police Department.