Law enforcement has many highly sophisticated tools available for helping to pin down those who commit crimes. Cyber criminals are more difficult to catch than those who commit theft and assault, and still more difficult is preventing crime before it happens. Big data is proving very useful for filling in these gaps in law enforcement technology, providing insights and detecting anomalies that can help officials reduce crime. Since the potential applications of big data in this field are extensive, lets take a look at some of the ways the technology is already being used to help control crime.
Facial Recognition
In 2013, facial recognition software did not catch the Tsarnaev brothers following the Boston Marathon bombing. However, after the Paris terrorist attacks in 2015, the technology had become advanced enough to help officials find suspects in the case. The hope is that as facial recognition improves, it can be used to help prevent crime before it occurs, identifying individuals like known terrorists as they approach public places. Baltimore police are also using facial recognition in their work, comparing them with photos in the states vehicle records. At this time, its unclear as to how this is being used in law enforcement, but facial recognition has the potential to both help prevent and solve crimes.
Crime Mapping
Lets be honest: many law enforcement officials (and individuals) have inherent biases about where crime occurs the most. Edmonton, Alberta, used big data to gain more concrete insight about what sorts of factors might be associated with crime in a given area. The results surprised many: mapping data showed that a low density of picnic areas were a factor in increased property crime. Data like this has allowed Edmonton to reduce crime, improve the relationship between law enforcement and citizens, helped them to allocate resources and come up with new programs, as well as monitor maintenance efforts throughout the city. An added benefit? Edmonton has seen a return of $1.60 for every dollar spent on this program.
Moving away from law enforcement is the RedZone app, which is designed for everyday users. The app provides real-time crime information in as little as 24 hours and for up to 60 days on a live map, which helps users gain awareness about the crime in their neighborhoods and destinations, identify safer routes and spot crime trends in about 1250 U.S. cities that could inform travel decisions. Since coming out of beta, the app has broken into the top 5 map apps in the Apple store, showing that theres demand for crowdsourced crime data.
Big Data in Fraud Detection
Fraudsters move quickly, and sometimes they can be done and gone within 24 hours. While rules-based systems that analysts traditionally use to detect cyber crime can be slow and result in additional losses after the fraud is found. Machine learning, combined with visualizations can quickly detect and shut down fraud, finding anomalies efficiently and providing context for each situation. As more devices come online and fraud gets even more sophisticated, big data will be crucial in keeping up with the criminals and shutting them down quickly.
Privacy Concerns & The Future of Big Data in Law Enforcement
Of course, though it has proven helpful for fighting crime, big data in law enforcement is also sparking some concern. Many are worried about the privacy implications of the technology, if not now, then in the future, as technology becomes more advanced. While data-based surveillance does help law enforcement stay on top of crime, there are legitimate concerns about the ability to use this technology responsibly. Communication and transparency on how the data will be used, and the surveillance limitations will be key to gaining public trust in these programs. A major concern is that big data tools will be used to disproportionately target minorities and minority neighborhoods, yet another reason that surveillance accountability will be necessary as the technology progresses. As big data and machine learning become more advanced, new policies will be necessary to help ensure that everyday citizens are not subject to intrusive surveillance, while still leveraging our data effectively to keep communities safer.
