Since the 1930s, IBM has been involved in helping equip law enforcement officials around the world with the tools to fight crime faster, more safely and more effectively. This application of technology to the business of fighting crime has been most successful in the efficient allocation of resources—enabling law enforcement officials to spend less time searching for information or filling out paperwork and more time on the streets, keeping communities safe.
In 1963, New York City’s Police Chief Robert Gallati enlisted the help of an
From a technology standpoint, the best way to allocate resources in a policing organization is to move from a reactive stance to a more predictive posture—to get officers where they need to be, when they need to be there, and with the right information for them to act quickly and decisively. Only within the last two decades, however, has technology made the necessary advances to help assume this predictive posture.
In the 1980s, the law enforcement community saw a simple yet significant advancement in the way it thought about crime. New York City Transit Police Lieutenant Jack Maple was tired of responding to crime—he wanted to stop it before it happened. On 55 feet of wall space, Maple mapped every train and train station in New York City. “Then,” as Maple recalled, “I used crayons to mark every violent crime, robbery and grand larceny that occurred. I mapped the solved vs. the unsolved." By 1990, Maple had mapped New York’s complete crime situation through a system of colored pins. He called his maps “Charts of the Future,” and between 1990 and 1992 they helped cut felonies in the subway systems by 27 percent and robberies by a third.
Sticking pins into a map may sound crude or simple, but it was the first time police officers and city officials had an at-a-glance picture of the city’s recent criminal activity. Using these maps, police officers knew which neighborhoods were being hit by what crimes, and could more efficiently patrol and assist those areas. It was an important first step toward consolidating police data into a tool for crime analysis and prevention.
In 1994, the momentum behind Charts of the Future gave rise to CompStat (Comparative Statistics), a data-based policing approach where precinct commanders were held accountable for police performance in their area. Because CompStat was based on hard data, IBM was a natural collaborator—soon bringing its knowledge of analytics to the new world of crime fighting in the form of the IBM Crime Information Warehouse (CIW) Solution.
As the natural evolution of CompStat, the CIW essentially digitizes those pins on the map used in previous years, and incorporates advanced Web-enabled tools like geographic imaging software and live video feeds for a detailed view of an enforcement area. Rather than reacting, departments can use this instant information to redeploy and reconfigure resources in response to crime trends. Users can access any information stored within the warehouse to report, analyze and understand crime statistics based on any number of different factors.
But in order to fully move from a reactive position to a predictive posture, law enforcement officials need a comprehensive view of their enforcement area—they need more information both around and between those digitized pins. IBM combines the CIW with predictive analytics and a real-time crime center approach to provide the information links, or connective tissue, between the pins. From Chicago to London to Incheon, South Korea, municipalities are using the techniques of real-time information gathering (data and video) and predictive analytics to take the Crime Information Warehouse technologies to the next level. These solutions apply statistical data exploration and machine-learning techniques to historical information in order to help agencies uncover hidden patterns, associations, correlations and trends—even in large, complex datasets that account for seemingly innocuous variables such as weather conditions. This includes vast amounts of textual or unstructured data such as emails, videos and chat room interactions. With repositories of structured and unstructured information, deep analytics, advanced dashboards and data visualization, agencies can better predict hot spots and crime trends, and deploy resources more efficiently.
Today, cities and governments around the world are turning to real-time data collection and predictive analytics to help create smarter cities with more integrated law enforcement agencies. The adoption of technologies that can deliver accurate information in an actionable timeframe will only continue to grow as we move closer to a smarter planet. Saving time can save lives—and keep our communities safer.