Predictive policing represents the next step in law enforcement.
Today’s police departments are facing unprecedented new challenges. On the one hand, they’re under increasing public scrutiny, while on the other they’re expected to deal with a growing array of threats even as budgets continue to shrink. Enter: predictive policing, which allows law enforcement officers to make the most of their limited resources by allocating them more effectively.
Police forces in the United States have been using large volumes of crime data since 1929, when the Uniform Crime Report was first developed. In the 1960’s, St. Louis police used an algorithm called Law Enforcement Manpower Resource Allocation System (“LEMRAS”) to distribute police squad cars according to historical crime data. The increasing affordability of data storage made it possible to ramp up this strategy in the 80’s and 90’s, when “crime mapping” became a popular approach to local policing.
What is Predictive Policing?
Predictive policing strategies use data-driven analytics to prevent criminal activity by predicting it before it happens. While law enforcement has nearly always made policing decisions based on historical data, it was only in 1996 that it became cheaper to store data digitally than on paper. Today, over 2.5 quintillion bytes of digital data are created each day, and police forces across the country are putting it to use.
Greater data storage capabilities and far more advanced analytics (including machine learning and AI) have allowed modern police officers to understand where and when crime is likely to occur, and deploy their assets accordingly. But data can do more than that — historical information can be used to develop real-time response protocols, cutting down on emergency response times and fixing the disconnect between alert and action.
Better Analytics, Better Policing
Officers have long relied on predictive models to direct them to where they are needed most. Today, however, these models are built on top of infinitely larger volumes of data. This data can, for example, enable police departments to understand which officers are outperforming, and which are underperforming. With more data comes more accurate forecasting — and ultimately better, more effective policing.
On the level of the individual officer, the benefits of predictive policing are fairly intuitive — the closer a police officer is to the scene of a crime, the better positioned they are to respond. But these positive effects tend to accumulate and contribute to larger, more significant benefits. For example, if police are able to send squad cars only to locations where they’re actually needed, they can save money by not expending resources on areas where crime is unlikely to happen. This enables police forces to invest in additional officers, more advanced technology, and initiatives designed to prevent crime before police must be called upon to stop it.
Much like the analytics technology that is becoming increasingly popular in other industries, predictive analytics in policing can allow for both a more proactive approach and smarter, more efficient spending in law enforcement. More efficient use of police resources translates to safer neighborhoods, higher property values, and lower taxes for everyday citizens.
Stopping Threats in Real-Time
Intelligence indicating where crime might happen in the future is one thing, but real-time updates about where it’s happening around you is quite another. Until recently, sending up-to-the-second updates to officers on the move was near-impossible. The use of mobile networks in public safety applications has changed all that, making it possible to empower law enforcement officers at the tactical edge with real-time information on criminal activity.
With situational awareness platforms, police departments can put the location and status of any given distress call at the fingertips of those in the position to respond. Officers at headquarters and on the street can gain access to the same operational picture of their jurisdiction, provided that the data systems used to predict crime can be visualized and delivered to those who can act on it.
To make this possible, a robust situational awareness platform can translate diverse data streams into useful insights for officers in the field. Police might use anything from security footage, to IoT sensors, to communication with fellow officers to improve their emergency response times. Consolidating all this information and rendering it useful to each individual officer can make truly proactive security solutions possible.
The policing paradigm of the future is within our grasp, but it will take more than just analytics to make it happen. Law enforcement organizations will need to consider how to translate predictive policing insights into real-time crime prevention. To put all this computing power in the hands of officers on the ground, forward-looking predictive insights must be paired with situational awareness tools that empower officers to act.
