Left Hand, Right Hand
--Data science
is helping law enforcement
by Jia Wang, Minseok Song
In 2005’s
report of the European Union research, European research suggests, “increased use of crime prevention measures
may indeed be the common factor behind the near universal decrease in overall
levels of crime in the Western world”(Wikipedia). Data science is acting
as a useful hand to improve policing operation and enforcement. In this
session, we will discuss about who are the active users of data science in law
enforcement, what the problems they are addressing and what the challenges they
are facing.
WHO
When seeing the power of data science, the
law enforcement departments who have collected piles of data over the years are
keen to grasp this useful hand to help them enforce the law. Most western government and police departments are very active dealing with this. With the alarm of
IS attack, the European Union is also trying to apply big data technology to
analysis airline passenger data to track any more terrorist crime and prevent
heartbreaking tragedy (Europe, 2016).
Collaborating with government departments,
vendors and developers are providing predictive tools and financial tool. These
vendors and developers are usually IT companies or university research groups.
As one of the world largest IT companies, IBM starts at a very early stage to
help law enforcement institutions to fight with crime. Many world
well-known universities such as university of Michigan, Georgetown University,
MIT and so on are also enthusiasm in providing law enforcement solutions.
Problems
With the help of data science, law
enforcement departments are mainly trying to address two problems:
vaccinate crime and prevent official violent enforcement.
As we mentioned last week, one of the data
science in law enforcement fronts is predictive policing. Governments try to
reduce crime by collecting data, analyzing data by using predictive tools and
operating officially. The effect is obvious. One example from an article is
that the crime reduced 15%-30% in Santa Cruz, Calif. after they implemented a
predictive policing tool called PredPol (Alexis C. Madrigal, 2016). Through the
approach, predictive policing has been formed a valuable cycle as follows:
Predictive policing
business/process cycle
(Image from: https://curiousmatic.com/predictive-policing-could-infusing-law-enforcement-with-data-science-stop-crime-in-its-tracks/)
The governments are also trying to avoid
unnecessary dead encounters made by police officers. In America, the white
house lunched the Police Data Initiative (Megan Smith, Roy L.Austin,
JR., 2015) project to prevent violent enforcement of
police officers. About 21 state police departments have participated in this
project, mainly from the coastal regions, such as PA, NC and CA. The Police Data
Initiative is particularly aimed to address officers who may behave violently
and take improper action towards crime under certain situations. Supervisors
will get warning in advance by the intervention system and take actions on
these officers.
Police Data Initiative
participants distribution
Challenges
Although a high technology the data science
is, it could not be perfect. Followings are some challenges that data science
in law enforcement may face:
1.
Narrow predicted crime area. It
is easy to find probable hot spots with big range of geography, but hard to
narrow the police operation in a small particular area.
2.
Data Quality. When dealing with
multiple systems, aggregating data from these systems may be challenging. Also,
data may be biased. Some criminal actions may not happen exactly on what it is
reported due to some lag of witness time (Clarence Wardell, 2015).
3.
Budget. No matter doing what
kind of projects, financial investment is important. Policing is big in certain
situation. Financial is hard to limit.
4.
Privacy. Some crime data
involves victims and teenager commitments. Sharing these kind of data with
public in some cases (for example, share with university groups) is really an
issue of respect and privacy rights.
Although data science in law enforcement is
facing challenges, the effect of that in crime reduction and regulate police
officers is significant. Data science is the left hand and policing operation
is the right hand. They are bundled together to make the world.
References
Larry Greenemeier.(2015, 7 22). Can Police Use
Data Science to Prevent Deadly Encounters? Retrieved 5 27, 2016, from
Scientific American: http://www.scientificamerican.com/article/can-police-use-data-science-to-prevent-deadly-encounters/
Jennifer Markert.(2015, 3 30). Predictive
Policing: Could Infusing Law Enforcement With Data Science Stop Crime In Its
Tracks?Retrieved 5 27, 2016, from Curiousmatic: https://curiousmatic.com/predictive-policing-could-infusing-law-enforcement-with-data-science-stop-crime-in-its-tracks/
Walter L. Perry, Brian McInnis, Carter C.
Price, Susan C. Smith, John S. Hollywood
. Predictive Policing. Retrieved 5
27, 2016, from: http://www.rand.org/content/dam/rand/pubs/research_reports/RR200/RR233/RAND_RR233.sum.pdf
Alexis C. Madrigal.(2016, 3 28).The Future Of Crime-Fighting Or The Future Of Racial Profiling?: Inside The Effects Of Predictive Policing. Retrieved 5 27, 2016, from TheWorldPost: http://www.huffingtonpost.com/entry/predictive-policing-video_us_56f898c9e4b0a372181a42ef
Clarence Wardell.(2015, 5 25).OpenGov Voices: Challenges
and solutions to collecting law enforcement data.Retrieved 5 27,2016,
from Sunlight Foundation: https://sunlightfoundation.com/blog/2015/05/25/opengov-voices-challenges-and-solutions-to-collecting-law-enforcement-data/
Megan Smith, Roy L.Austin, JR.(2015, 5 18).Launching the
Police Data Initiative.Retrieved 5 27,2016, from the White House: https://www.whitehouse.gov/blog/2015/05/18/launching-police-data-initiative
Europe.(2016, 4 13).Passenger Name Record: EU to harvest more data to stop crime
.Retrieved 5 27,2016, from BBC News: http://www.bbc.com/news/world-europe-36035698
Wikipedia
https://en.wikipedia.org/wiki/Main_Page
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