Police forces across the globe are constantly under pressure to respond swiftly to critical incidents while working with increasingly limited resources. Law enforcement organizations are turning to technology to help solve this conundrum by finding ways to not just fight crime, but prevent it. At the forefront of this adoption is predictive policing—a technique that has acquired newfound importance with the advancement of big data analytics.
In an attempt to harness the power of big data, organizations are using advanced analytics programs to assimilate vast quantities of information to predict, identify, and prevent crime. The Chicago Police Department, for instance, has applied a predictive computing model to generate a ‘heat list’ of approximately 400 people most likely to be involved in crime, as victims or offenders. Elsewhere, in San Mateo County, California, police are putting their data to work to predict potential crime scenes, utilizing an in-house data analyst to analyze reports and identify patterns of interest. The same department also observed that there was a pattern to the activity of a local burglar. Having identified the area the suspect was most likely to strike next, officers were deployed to the location and soon apprehended the alleged car thief. Another interesting example is that of the Santa Cruz Police Department, which used an earthquake aftershock algorithm to analyze crime data. The results were used to determine potential future offenses in 500-square feet areas.
From social media information to traffic reports, and public records to informant tips, the law enforcement industry has access to a substantial amount of data. Spread over disparate and legacy systems, this information can be hard to organize, but can generate invaluable information. Key to utilizing this intelligence are easy-to-operate data capture tools and clear, standardized information security policies.
Mass adoption of predictive policing using big data, however, is not without its challenges. The intelligence generated for using this approach is only as useful as the data it comes from. This makes it important for organizations to gather relevant, specific data constantly yet unobtrusively— a rather cumbersome task. And while optimizing cities for data capture can be difficult, even more so in developing countries, equally daunting is the prospect of raising citizen suspicion through a fear of constantly being watched.
Herein lies the opportunity for solution providers to create an infrastructure primed to gather information alongside solutions that efficiently assimilate this data to generate intelligence. For organizations, centralized data systems, intelligent use of the information already available, and multi-faceted analytics programs will accelerate the growth of predictive policing.
Have you come across any interesting applications of technology in law enforcement? Share them with us in the comments section below.