Ever-growing security threats coupled with shrinking budgets are putting enormous pressure on law enforcement agencies worldwide to improve their efficiency with limited resources. This is why many of these agencies are turning to big data to help them make the most of their resources and to address all the pressing issues within their respective communities.
Before going into the power of big data analytics, it’s important to briefly understand what big data is all about.
What is Big Data?
Simply put, it is the collection and analysis of data from different sources with an aim to identify meaningful patterns present among the disparate data sets. In other words, data from different points such as sensors, cell phone, social media and the Internet are scanned together to know more about a particular person or a problem. The key aspect here is to collate different data formats, that include both structured and unstructured ones, to identify what you want.
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Now that you have a basic understanding, let us explore some big data applications that are being used to improve the working of law enforcement agencies.
Law Enforcement Analytics (LEA)
Law Enforcement Analytics (LEA) are a set of systems based on the principles of big data, and are used to create actionable information for law enforcement officers. These systems combine all the existing information, establish relationships between seemingly unrelated data and present it all in a simplified format through dashboards or screens, so that it is easy for law enforcement officers to act on it. The best part – officers no longer have to connect to multiple systems to get all the information they want, and this saves valuable time and effort.
Predictive Analysis
I asked a friend who’s a business lawyer this question: How can law enforcement officers cut crimes without increasing costs?
Is it by persuading people to stop breaking the law? Or by pushing for the legislation of stiffer punishments for offenders? Or by getting more officers on board and procuring more comfortable and light UA tactical boots and uniforms? Or by procuring stronger arms and ammunitions?
“Definitely no! The way out is to just predict them ahead of time,” he said.
This is exactly what big data-based predictive analysis systems do. These systems combine data from varied sources and use complex algorithms to predict the nature and place of the next crime. This information makes it easy for officers to get to the crime scene at the right time to prevent it. Such a system saves time and cost, as agencies can avoid futile chases and still, prevent a crime.
A case in point is the PredPol software used by the Los Angeles Police Department (LAPD). This software uses three data points, namely, place, time and nature of crime, to predict future crimes and their location. This tool has been effective, as the LAPD’s Foothill Division saw a 20 percent drop in crime rates between January 2013 to January 2014, and they even had a crime-free day on February 13, 2014.
Enforcing Regulations
Police officers spend a good part of their time checking for DUI, seat belt enforcements and car speeds with an aim to reduce the number of road accidents. Despite these efforts, the number of road accidents are on the rise because the existing officers are not enough to patrol all the highways and to check for violations.
This is why many enforcement agencies such as the Tennessee Highway Patrol (THP) turned to big data systems. A traffic accidents system created by IBM identified the correlation that exists between incidents such as DUI and crashes with external information such as location, date, time, weather conditions and other pertinent factors to predict future incidents. Based on this system, THP focused only on the areas and times that had the highest propensity for accidents, and was able to reduce car accidents, with 2014 being the lowest traffic fatality year since 1963 in this state. Such is the power of big data.
Intelligence Sharing
Traditionally, data was siloed in different departments, and any agency wanting information had to go through an elaborate process to access them. This inability to share also affected the efficiency of different agencies as they did not have the complete picture of a problem. Big data has changed all this, as data that is existing in different formats can be brought together and analyzed easily now. As a result, intelligence is more accurate and can be shared among different enforcement agencies at the click of a mouse.
While the above applications are not a substitute for the experience and intuition of law enforcement officers, they are nevertheless great tools that help them to stop crime before it happens, and also helps the agencies to make the best use of their existing resources.
