Reference no: EM131052784

CASE

On a Saturday afternoon last summer, Mark Rasch took his son to his baseball game at a park in Georgetown, Maryland. The ballpark is located in an area that has zone parking with a two-hour limit. Rasch was forced to park in a spot that was a bit of a hike from the ball field. He later eyed an opening closer to the park and moved his car there. The game ended. Rasch packed up and was ready to pull away when he noticed a parking enforcement officer writing tickets. "I'm OK, right?" he asked, assuming that because he had moved his car she wouldn't know he'd been parked in the zone for more than two hours. Wrong. The officer not only knew that he had moved his car but when and how long he'd been parked within the zone. Fortunately, she didn't write him a ticket as he was about to pull out. But the encounter left Rasch, who is a lawyer and a cyber-security consultant, a little spooked at the realization of just how much information law enforcement is generating. If there was a time when law enforcement agencies suffered from an information deficit, it has passed. Of the more than 18,000 law enforcement agencies across the United States, the vast majority has some form of technology for collecting crime-related data in digital form. The biggest city agencies have sophisticated data warehouses, and even the most provincial are database savvy. So it's not surprising that law enforcement and criminal justice agencies are running into the same data-related problems that CIOs have been experiencing for years: ensuring data quality and accessibility, developing and enforcing standards for interoperability, and exploiting those digital resources in the most effective manner. The era of data-driven law enforcement began in the early 1990s in New York City. There, police chief William Bratton sought to impress newly elected mayor Rudolph Giuliani with a radical approach to policing that came to be known as Comp Stat. Comp Stat put an emphasis on leveraging data-accurate, detailed, and timely-to optimize police work. "Police departments are powerful collectors of data," says Michael Berkow, president of Altegrity Security Consulting (ASC), a newly launched division of security firm Altegrity. Before joining ASC last month, Berkow was chief of the Savannah-Chatham police department, and before that he was second in command to Bratton in Los Angeles after Bratton left New York to be chief of the Los Angeles Police Department. Police departments were motivated to implement or upgrade IT systems by the Y2K frenzy, Berkow says. "By 2000-2001, everybody had some level of digital information," he says. That and Comp Stat led to a movement known by the initials ILP, which stand for "information-led policing" or, according to some, "intelligence-led policing." The concept is simple: Leverage data to help position limited police resources where they can do the most good. It's an effort to be more proactive, to "change the environment," Berkow says, from the reactive, response-oriented methods of the past. To a great extent, data are about the context of criminal behavior. "We know that the same small group of criminals is responsible for a disproportionate amount of crime," says Berkow. Police refer to that group as PPOs: persistent prolific offenders. Past criminal behavior, such as domestic violence, can be a strong indicator of potential future problems. When Berkow was chief in Savannah, his department went through data on recent homicide cases and noticed an interesting data point: Of about 20 arrests for homicide, 18 of those people had prior arrests for possession of firearms. "We started this very detailed review of every aspect of our gun arrests," he says. Law enforcement officials often refer to the need for actionable information. One of the first ways police agencies used incident-report data in digital form was in conjunction with geographical information systems, in support of what's known as electronic crime mapping, or hot-spot analysis. Police in the city of Edmonton, Alberta, brought in data analysis technology from business intelligence vendor Cognos (now part of IBM) a few years ago. In their first project, police officials concentrated on using the reporting tool in conjunction with a new geographic-based resource deployment model being implemented by the agency. "Our business analytics reports became a key component of how we deployed policemen around the city," says John Warden, staff sergeant in the business performance section of the Edmonton Police Service. Now the agency is using the data to plot criminal activity according to both geographic area and comparative history. "We're really delving into those analytics in terms of place and time," says Warden. The holy grail of information-led policing is what's referred to as predictive policing: being able to predict where and when crimes may occur. That's where Chicago wants to go. The Chicago Police Department operates what Jonathan Lewin, commander of information services, refers to as "the largest police transaction database in the United States." Costing $35 million, Chicago's Citizen and Law Enforcement Analysis and Reporting (CLEAR) system processes "all the arrests for all the departments in Cook county-about 120-in real time," Lewin says, and 450 local, state, and federal law enforcement agencies have query access to it. Lewin's IT shop has about 100 staffers and employs between 10 and 20 contract workers from Oracle, whose database technology the system is based on. Chicago's police department is working with the Illinois Institute of Technology (IIT), by way of a $200,000 grant from the National Institute of Justice, on an "initial exploration" of a predictive policing model. The grant was awarded partly on the basis of work done by Dr. Miles Wernick of IIT in the area of medical imaging and pattern recognition, and the project involves exploring "nontraditional disciplines" and how they might apply to crime projection. "We're going to be using all the data in the CLEAR system," Lewin says, including arrests, incidents, calls for service, street-gang activity, as well as weather data and community concerns such as reports of streetlights out. "This model will seek to use all these variables in attempting to model future patterns of criminal activity," he says. SPSS is a name often associated with predictive policing. The statistical-analysis software developer, recently acquired by IBM, has customer histories that tout the success of its tools in the criminal justice environment, such as the Memphis, Tennessee, police force, which SPSS says reduced robberies by 80 percent by identifying a particular hot spot and proactively deploying resources there. But can software really predict crime? "It's not a binary yes or no; it's more of an assessment of risk-how probable something is," says Bill Haffey, technical director for the public sector at SPSS. he private sector is also doing its part. Cargo Net, the first-ever national database of truck theft information, is a joint project from insurance data provider ISO and the National Insurance Crime Bureau (NICB). Cargo Net will collect up to 257 fields of data detailing everything from destination, plate number, and carrier; to the time, data, and location of the theft; to serial numbers and other identifying details on the stolen goods. Refreshed several times per day, Cargo Net is expected to track more than 10,000 events per year, driving both a national alerting system and a corresponding truck stop watch program. Truck theft happens mostly on weekends, and it's rife around the Los Angeles basin, Atlanta, Miami, Dallas/Ft. Worth, and Memphis, Tennessee. Trucks and trailers typically slip away in the dark of night from truck stops, rest areas, distribution centers, and transfer points. The goods most often hit are consumer electronics, food, wine and spirits, clothing, and other items easily sold on the street. These historical patterns are well known, but cops on the beat need up-to-the-minute information on the latest truck stops and distribution centers hit, the time of day perpetrators strike, and the type of goods stolen. Carriers and manufacturers want fresh, nationwide information so they can change the timing of deliveries and avoid specific truck stops and routes. Insurers want a single source of data so they can get a better gauge risk and bring the problem under control nationwide. All this collecting, warehousing, and mining crime related data begs the question: How much is too much? The Georgetown incident still bothers Rasch. "What it meant was that D.C. was keeping a database of people who are legally parked," says Rasch, which, from a privacy standpoint, is "more intrusive than chalking the tires." Pertinent questions include: How long do they hold onto that data? And with whom do they share it? It's an important discussion to have, both in terms of privacy and effective police methods. After all, as Rasch points out, it was a parking ticket that led to the arrest of serial killer Son of Sam.

CASE STUDY QUESTIONS

1. What are some of the most important benefits derived by the law enforcement agencies mentioned in the case? How do these technologies allow them to better fight crime? Provide several examples.

2. How are the data-related issues faced by law enforcement similar to those that could be found in companies? How are they different? Where do these problems come from? Explain.

3. Imagine that you had access to the same crime-related information as that managed by police departments. How would you analyze this information, and what actions would you take as a result?