Data Analytics Transforms Policing

“You can’t manage what you can’t measure.” This age-old maxim of the business and management world has driven a wider adoption of data analytics tools and techniques. But unbeknownst to many Americans, it’s a truism that’s also been applied on a large scale in law enforcement agencies around the country.

Many believe that advanced data collection and analysis has enabled better policing and planning, with lower crime rates observed since the adoption of these strategies. While there’s no proof yet of a direct link between higher data utilization and lower crime rates, law enforcement agencies are only getting better at using all available information to make the right decisions.

A brief history of policing data

One of the first examples of the application of data analytics to policing is known as CompStat, a computer program and overarching management philosophy still in use today in several U.S. cities. The idea behind CompStat is simple yet effective: Police departments meticulously track and categorize crime reports based on location and time. From this data, commanders can shift resources into areas where crime appears to be spiking, while continuously gathering more data to measure progress and tweak tactics.

While no causal link has been proven and there were many factors at play, some regard the introduction of CompStat and data-driven policing in general as a key reason for the precipitous decline in the rate of violent crimes reported at the national and local level. The crime wave that gripped several U.S. cities through much of the 1980s and 1990s was essentially reversed. According to the FBI, the national violent crime rate (reports per 100,000 individuals) went from 636.6 in 1996 to just 372.6 by 2015.

New dimensions of crime fighting

Data analytics in crime prevention has come a long way since CompStat, so much so that many agencies are struggling to make sense of all the information being gathered and the many ways to utilize it. According to Accenture, police departments in 2017 need a solution for dealing with crime data that solves several problems at once, including:

  • Fragmentation of data: In many cases, useful crime statistics are scattered throughout different systems, recorded in nonstandard formats and, in the hands of departments, separated by location.
  • Inadequate audit trail: Agencies lack the processes and management structures to know if their analytics efforts are actually effective.
  • Inaccurate data: As a result of the above, too much crime data may not be accurate, which undermines law enforcement officers’ confidence and willingness to use it.
  • Lack of timely data: All the information in the world is hardly useful if it’s outdated. Timeliness is particularly crucial in crime prevention, where the success or failure of an investigation can be decided in a matter of minutes.

To solve these challenges and push toward the ideal of truly predictive policing, a number of new tactics are emerging in rapid succession. Wired Magazine reported on a few new applications in the field of crime analytics that solve some of the aforementioned problems by gathering and analyzing data that is both pertinent and easily accessible.

One such program combines the crime data tracking of CompStat with precise mapping. This allows police to hone in on crimes reported at bars or nightclubs to examine any patterns that may emerge. Another developing application gathers data from social media websites like Twitter and Facebook. By analyzing chatter between known gang members, for example, agents may then be able to quickly develop leads on crimes that have already occurred or even those that are still in the planning stages.

As police find new methods of tracking and solving crime, their needs and priorities in a data analytics strategy are bound to continuously evolve.