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What problems does this approach to crime fighting pose?

What problems does this approach to crime fighting pose?

Understanding Business
Understanding Business
12th Edition
ISBN:9781259929434
Author:William Nickels
Publisher:William Nickels
Chapter1: Taking Risks And Making Profits Within The Dynamic Business Environment
Section: Chapter Questions
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What problems does this approach to crime fighting pose?

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 affiliation and type of crime. Police commanders supply a list of each precinct's 25 worst offenders, reported rates of the most serious types of crime, the which is added to a searchable database that now city in 2015 was as safe as it had been since statistics 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 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 arrested. This database is used for an arrest alert system. 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 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 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 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 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
Transcribed Image Text: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 affiliation and type of crime. Police commanders supply a list of each precinct's 25 worst offenders, reported rates of the most serious types of crime, the which is added to a searchable database that now city in 2015 was as safe as it had been since statistics 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 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 arrested. This database is used for an arrest alert system. 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 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 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 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 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
Chapter 6 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 types of crime, and creates maps of the city with 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 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 authorities. 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- 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 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 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. CASE STUDY QUESTIONS 1. What are the benefits of intelligence-driven prose- cution for crime fighters and the general public? 3. What management, organization, and technology issues should be considered when setting up infor- mation systems for intelligence-driven prosecution? 2. What problems does this approach to crime fight- ing pose?
Transcribed Image Text:Chapter 6 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 types of crime, and creates maps of the city with 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 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 authorities. 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- 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 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 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. CASE STUDY QUESTIONS 1. What are the benefits of intelligence-driven prose- cution for crime fighters and the general public? 3. What management, organization, and technology issues should be considered when setting up infor- mation systems for intelligence-driven prosecution? 2. What problems does this approach to crime fight- ing pose?
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