This paper reviews the evolution of law enforcement use of Big Data in the United States and captures some key lessons learned.
Law enforcement methodology has evolved tremendously in the past few decades to embrace quantitative analysis and statistics with the rise of data-driven policing. Now, law enforcement strategy is poised to take another leap forward with Big Data and predictive policing. Already, departments facing rising crime but falling budgets have turned to Big Data analytics to supplement the instincts of their officers and make patrols more proactive rather than reactive. Though much work remains to be done, advances in predictive policing have the potential to let law enforcement agencies do more with less and prevent crime rather than simply respond to it.
Patrol is the backbone of law enforcement, the largest use of department personnel and resources as well as the source of the majority of officer interactions with the community and hence has the greatest potential to reduce crime. Despite the perception of the old-fashioned “beat cop,” patrol has repeatedly been redefined through new technology. The introduction of 911, police cars, and radios first marginalized foot patrol and, especially in more active departments, made patrol primarily responsive, driven by calls for service. When officers did get the chance to patrol freely, working to reduce crime rather than respond to it, their “beats” would be determined by administrative divisions, areas they could reasonably cover, and, at best, “primal policing” based on experience and instinct.