1) What are the benefits of intelligence-driven prosecution for crime fighters and the general public? 2) What problems does this approach to crime fighting pose? 3) What management, organization, and technology issues should be considered when setting up information systems for intelligence-driven prosecution?

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1) What are the benefits of intelligence-driven prosecution for crime fighters and the general public?

2) What problems does this approach to crime fighting pose?

3) What management, organization, and technology issues should be considered when setting up information systems for intelligence-driven prosecution?

256 Part Two Information Technology Infrastructure
INTERACTIVE SESSION: ORGANIZATIONS
Data-Driven Crime Fighting Goes Global
Nowhere have declining crime rates been as dra-
matic as in New York City. As reflected in the
reported rates of the most serious types of crime, the
city in 2015 was as safe as it had been since statistics
affiliation and type of crime. Police commanders
supply a list of each precinct's 25 worst offenders,
which is added to a searchable database that now
includes more than 9,000 chronic offenders. A large
percentage are recidivists who have been repeatedly
convicted of grand larceny, active gang members,
and other priority targets. These are the people
law enforcement wants to know about if they are
arrested.
This database is used for an arrest alert system.
have been kept. Crimes during the preceding few
years have also been historically low.
Why is this happening? Experts point to a num-
ber of factors, including demographic trends, the
proliferation of surveillance cameras, and increased
incarceration rates. But New York City would also
argue it is because of its proactive crime preven-
tion program along with district attorney and police
force willingness to aggressively deploy information
technology
There has been a revolution in the use of big data
for retailing and sports (think baseball and Money-
Ball) as well as for police work. New York City has
been at the forefront in intensively using data for
crime fighting, and its CompStat crime-mapping
program has been replicated by other cities.
CompStat features a comprehensive, citywide
database that records all reported crimes or com-
plaints, arrests, and summonses in each of the city's
76 precincts, including their time and location. The
CompStat system analyzes the data and produces a
weekly report on crime complaint and arrest activity
at the precinct, patrol borough, and citywide levels.
CompStat data can be displayed on maps showing
crime and arrest locations, crime hot spots, and other
relevant information to help precinct commanders
and NYPD's senior leadership quickly identify pat-
terns and trends and develop a targeted strategy for
fighting crime, such as dispatching more foot patrols
to high-crime neighborhoods.
Dealing with more than 105,000 cases per year
When someone considered a priority defendant is
picked up (even on a minor charge or parole viola-
tion) or arrested in another borough of the city, any
interested prosecutor, parole officer, or police intel-
ligence officer is automatically sent a detailed e-mail.
The system can use the database to send arrest alerts
for a particular defendant, a particular gang, or a
particular neighborhood or housing project, and the
database can be sorted to highlight patterns of crime
ranging from bicycle theft to homicide.
The alert system helps assistant district attor-
neys ensure that charging decisions, bail applica-
tions, and sentencing recommendations address
that defendant's impact on criminal activity in the
community. The information gathered by CSU and
disseminated through the arrest alert system differ-
entiates among those for whom incarceration is an
imperative from a community-safety standpoint and
those defendants for whom alternatives to incarcera-
tion are appropriate and will not negatively affect
overall community safety. If someone leaves a gang,
goes to prison for a long time, moves out of the city
or New York state, or dies, the data in the arrest alert
system are edited accordingly.
Information developed by CSU helped the city's
Violent Criminal Enterprises Unit break up the most
violent of Manhattan's 30 gangs. Since 2011, 17 gangs
in Manhattan, New York's district attorneys did not
have enough information to make fine-grained deci-
sions about charges, bail, pleas, or sentences. They
couldn't quickly separate minor delinquents from
serious offenders.
In 2010 New York created a Crime Strategies Unit
(CSU) to identify and address crime issues and target
priority offenders for aggressive prosecution. Rather
than information being left on thousands of legal
pads in the offices of hundreds of assistant district
attorneys, CSU gathers and maps crime data for
Manhattan's 22 precincts to visually depict criminal
activity based on multiple identifiers such as gang
have been dismantled.
Using Big Data and analytics to predict not only
where crime will occur, but who will likely commit
a crime, has spread to cities across the globe in the
UK, Germany, France, Singapore and elsewhere.
In the UK. Kent Police have been using "pre-crime"
software beginning in 2015. The proprietary soft-
ware, called PredPol, analyzes a historical database
of crimes using date, place, time, and category of
offense. PredPol then generates daily schedules for
the deployment of police to the most crime-prone
Chapter
Foundations of Business Intelligence: Databases and Information Management 257
areas of the city. PredPol does not predict who will
likely commit a crime, but instead where the crimes
are likely to happen based on past data. Using
decades worth of crime reports, the PredPol system
identified areas with high probabilities of various
legalizes a global web and telecommunications sur-
veillance system, and a government database that
stores the web history of every citizen. This data
and analysis could be used to identify people who
are most likely to commit a crime or plot a terrorist
attack. Civil liberties groups around the globe are
concerned that these systems operate without judi-
cial or public oversight, and can easily be abused by
types of crime, and creates maps of the city with
color coded boxes indicating the areas to focus on.
It's just a short step to predicting who is most
likely to commit a crime, or a terrorist act. Predict-
ing who will commit a crime requires even bigger
Big Data than criminal records and crime locations.
Law enforcement systems being developed now
parallel those used by large hotel chains who collect
detailed data on their customers personal prefer-
authorities.
ences, and even their facial images. Using surveil-
lance cameras throughout a city, along with real time
analytics, will allow police to identify where former,
or suspected, criminals are located and traveling.
These tracking data will be combined with surveil-
lance of social media interactions of the persons
Sources: "The UK Now Wields Unprecedented Surveillance Powers-
Here's What it Means," by James Vincent, The Verge.com, Novem-
ber 29, 2016; "Predictive Policing and the Automated Suppression
of Dissent," by Lena Dencik, LSE Media Projects Blog, April 2016;
"Prosecution Gets Smart" and "Intelligence-Driven Prosecution/
Crime Strategies Unit," www.manhattanda.org, accessed March 4,
2016; Pervaiz Shallwani and Mark Morales, "NYC Officials Tout New
Low in Crime, but Homicide, Rape, Robbery Rose," Wall Street Jour-
nal, January 4, 2016; "The New Surveillance Discretion: Automated
Suspicion, Big Data, and Policing." by Elizabeth Joh, Harvard Law &
Policy Review, December 14, 2015; "British Police Roll Out New Pre-
crime' Software to Catch Would-Be Criminals," 21st Century Wire,
March 13, 2015; and Chip Brown, "The Data D.A.", New York Times
Magazine, December 7, 2014.
involved. The idea is to allocate police to those areas
where "crime prone" people are located. In 2016
the UK adopted the Investigatory Powers Bill which
CASE STUDY QUESTIONS
1. What are the benefits of intelligence-driven prose-
3. What management, organization, and technology
issues should be considered when setting up infor-
mation systems for intelligence-driven
prosecution?
cution for crime fighters and the general public?
2. What problems does this approach to crime fight-
ing pose?
Transcribed Image Text:256 Part Two Information Technology Infrastructure INTERACTIVE SESSION: ORGANIZATIONS Data-Driven Crime Fighting Goes Global Nowhere have declining crime rates been as dra- matic as in New York City. As reflected in the reported rates of the most serious types of crime, the city in 2015 was as safe as it had been since statistics affiliation and type of crime. Police commanders supply a list of each precinct's 25 worst offenders, which is added to a searchable database that now includes more than 9,000 chronic offenders. A large percentage are recidivists who have been repeatedly convicted of grand larceny, active gang members, and other priority targets. These are the people law enforcement wants to know about if they are arrested. This database is used for an arrest alert system. have been kept. Crimes during the preceding few years have also been historically low. Why is this happening? Experts point to a num- ber of factors, including demographic trends, the proliferation of surveillance cameras, and increased incarceration rates. But New York City would also argue it is because of its proactive crime preven- tion program along with district attorney and police force willingness to aggressively deploy information technology There has been a revolution in the use of big data for retailing and sports (think baseball and Money- Ball) as well as for police work. New York City has been at the forefront in intensively using data for crime fighting, and its CompStat crime-mapping program has been replicated by other cities. CompStat features a comprehensive, citywide database that records all reported crimes or com- plaints, arrests, and summonses in each of the city's 76 precincts, including their time and location. The CompStat system analyzes the data and produces a weekly report on crime complaint and arrest activity at the precinct, patrol borough, and citywide levels. CompStat data can be displayed on maps showing crime and arrest locations, crime hot spots, and other relevant information to help precinct commanders and NYPD's senior leadership quickly identify pat- terns and trends and develop a targeted strategy for fighting crime, such as dispatching more foot patrols to high-crime neighborhoods. Dealing with more than 105,000 cases per year When someone considered a priority defendant is picked up (even on a minor charge or parole viola- tion) or arrested in another borough of the city, any interested prosecutor, parole officer, or police intel- ligence officer is automatically sent a detailed e-mail. The system can use the database to send arrest alerts for a particular defendant, a particular gang, or a particular neighborhood or housing project, and the database can be sorted to highlight patterns of crime ranging from bicycle theft to homicide. The alert system helps assistant district attor- neys ensure that charging decisions, bail applica- tions, and sentencing recommendations address that defendant's impact on criminal activity in the community. The information gathered by CSU and disseminated through the arrest alert system differ- entiates among those for whom incarceration is an imperative from a community-safety standpoint and those defendants for whom alternatives to incarcera- tion are appropriate and will not negatively affect overall community safety. If someone leaves a gang, goes to prison for a long time, moves out of the city or New York state, or dies, the data in the arrest alert system are edited accordingly. Information developed by CSU helped the city's Violent Criminal Enterprises Unit break up the most violent of Manhattan's 30 gangs. Since 2011, 17 gangs in Manhattan, New York's district attorneys did not have enough information to make fine-grained deci- sions about charges, bail, pleas, or sentences. They couldn't quickly separate minor delinquents from serious offenders. In 2010 New York created a Crime Strategies Unit (CSU) to identify and address crime issues and target priority offenders for aggressive prosecution. Rather than information being left on thousands of legal pads in the offices of hundreds of assistant district attorneys, CSU gathers and maps crime data for Manhattan's 22 precincts to visually depict criminal activity based on multiple identifiers such as gang have been dismantled. Using Big Data and analytics to predict not only where crime will occur, but who will likely commit a crime, has spread to cities across the globe in the UK, Germany, France, Singapore and elsewhere. In the UK. Kent Police have been using "pre-crime" software beginning in 2015. The proprietary soft- ware, called PredPol, analyzes a historical database of crimes using date, place, time, and category of offense. PredPol then generates daily schedules for the deployment of police to the most crime-prone Chapter Foundations of Business Intelligence: Databases and Information Management 257 areas of the city. PredPol does not predict who will likely commit a crime, but instead where the crimes are likely to happen based on past data. Using decades worth of crime reports, the PredPol system identified areas with high probabilities of various legalizes a global web and telecommunications sur- veillance system, and a government database that stores the web history of every citizen. This data and analysis could be used to identify people who are most likely to commit a crime or plot a terrorist attack. Civil liberties groups around the globe are concerned that these systems operate without judi- cial or public oversight, and can easily be abused by types of crime, and creates maps of the city with color coded boxes indicating the areas to focus on. It's just a short step to predicting who is most likely to commit a crime, or a terrorist act. Predict- ing who will commit a crime requires even bigger Big Data than criminal records and crime locations. Law enforcement systems being developed now parallel those used by large hotel chains who collect detailed data on their customers personal prefer- authorities. ences, and even their facial images. Using surveil- lance cameras throughout a city, along with real time analytics, will allow police to identify where former, or suspected, criminals are located and traveling. These tracking data will be combined with surveil- lance of social media interactions of the persons Sources: "The UK Now Wields Unprecedented Surveillance Powers- Here's What it Means," by James Vincent, The Verge.com, Novem- ber 29, 2016; "Predictive Policing and the Automated Suppression of Dissent," by Lena Dencik, LSE Media Projects Blog, April 2016; "Prosecution Gets Smart" and "Intelligence-Driven Prosecution/ Crime Strategies Unit," www.manhattanda.org, accessed March 4, 2016; Pervaiz Shallwani and Mark Morales, "NYC Officials Tout New Low in Crime, but Homicide, Rape, Robbery Rose," Wall Street Jour- nal, January 4, 2016; "The New Surveillance Discretion: Automated Suspicion, Big Data, and Policing." by Elizabeth Joh, Harvard Law & Policy Review, December 14, 2015; "British Police Roll Out New Pre- crime' Software to Catch Would-Be Criminals," 21st Century Wire, March 13, 2015; and Chip Brown, "The Data D.A.", New York Times Magazine, December 7, 2014. involved. The idea is to allocate police to those areas where "crime prone" people are located. In 2016 the UK adopted the Investigatory Powers Bill which CASE STUDY QUESTIONS 1. What are the benefits of intelligence-driven prose- 3. What management, organization, and technology issues should be considered when setting up infor- mation systems for intelligence-driven prosecution? cution for crime fighters and the general public? 2. What problems does this approach to crime fight- ing pose?
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