BIG DATA: SEIZING OPPORTUNITIES, PRESERVING VALUES
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federal, state, and local law enforcement can mean a faster and more effective response
to criminal activity. It can also increase the chances that justice is reliably served in
online crime, where criminals are among the earliest adopters of new technologies and
law enforcement needs to have timely access to digital evidence.
Beyond surveillance, predictive technologies offer the potential for law enforcement to be
better prepared to anticipate, intervene in, or outright prevent certain crimes. Some ana-
lytics software, such as one program in use by both the Los Angeles and Memphis po-
lice departments, employs predictive analytics to identify geographically-based
“hotspots.”
Many cities attribute meaningful declines in property crime to stepping up
police patrols in “hotspot” areas.
Controversially, predictive analytics can now be applied to analyze a person’s individual
propensity to criminal activity.
In response to an epidemic of gang-related murders, the
city of Chicago conducted a pilot that shifts the focus of predictive policing from geo-
graphical factors to identity. By drawing on police and other data and applying social
network analysis, the Chicago police department assembled a list of roughly 400 individ-
uals identified by certain factors as likely to be involved in violent crime. As a result, po-
lice have a heightened awareness of particular individuals that might reflect factors be-
yond charges and convictions that are part of the public record.
Predictive analytics are also being used in other areas of criminal justice. In Philadelph-
ia, police are using software designed to predict which parolees are more likely to com-
mit a crime after release from prison and thus should have greater supervision.
The
software uses about two dozen variables, including age, criminal history, and geographic
location.
These new techniques have come with considerable controversy about how and when
they should be deployed.
This technology can help more precisely allocate law en-
forcement and other public resources, which can lead to the prevention of harmful
The National Institute of Justice, the Department of Justice’s research, development, and evaluation
agency, provides detailed information on the use of predictive policing at law enforcement agencies. For
more information, visit www.nij.gov/topics/law-enforcement/strategies/predictive-policing.
Andree G. Ferguson, “Big Data and Predictive Reasonable Suspicion,” 163 University of Pennsylvania
Law Review, April 2014, http://ssrn.com/abstract=2394683.
The application of this particular predictive policing technology emerged out of a series of grants issued by
the National Institute of Justice the Chicago Police Department, most recently involving Miles Wernick as
technical investigator. For more information, see http://www.nij.gov/topics/law-
enforcement/strategies/predictive-policing/Pages/research.aspx.
For more information on government crime prediction using statistical methods, refer to Eric Holder, Mary
Lou Leary, and Greg Ridgeway, “Predicting Recidivism Risk: New Tool in Philadelphia Shows Great Prom-
ise,” National Criminal Justice Reference Service, February 2013, https://ncjrs.gov/pdffiles1/nij/240695.pdf.
Controversial aspects of the Chicago pilot’s methodology are captured by in Jay Stanley, “Chicago Police
‘Heat List’ Renews Old Fears About Government Flagging and Tagging,” American Civil Liberties Union,
February 2014, https://www.aclu.org/blog/technology-and-liberty/chicago-police-heat-list-renews-old-fears-
about-government-flagging-and; Whet Moser, The Small Social Networks at the Heart of Chicago Violence,”
Chicago Magazine, December 9, 2013, http://www.chicagomag.com/city-life/December-2013/The-Small-
Social-Networks-at-the-Heart-of-Chicago-Violence.