This article was 1st published on our sister Site, The Internet Of All Things.
Fighting crime while enduring budget cuts isn’t how most police officers imagined their job when they were signing up for it. Their salaries are often low even in developed countries such as the US and Germany. Also, the number of police officers stagnates, and in the UK, it even dropped by around 20,000 in the last eight years alone. Yet, there’s no lack of crimes to solve. According to an FBI report, there were 17,284 murders across the US in 2017, while Brazil broke its own record as the number of murders last year hit 63,880. And the nature of crime is changing, too, as criminals turn to the internet to commit sophisticated crimes such as identity theft, cyber-attacks, and financial fraud.
Clearly, police needs to find a way to efficiently use its limited resources to solve and even prevent crimes. One way to go about that is to rely on artificial intelligence (AI) tools that use past data to predict where crime might occur and who might commit it – think Minority Report, but not as advanced. This would allow the police to act where it’s needed the most and not waste precious resources. But this approach is more complicated than it sounds, and predictive policing algorithms such as PredPol and the National Data Analytics Solution (NDAS) were met with both praise and criticism. Police chiefs praise the software’s efficiency, while analysts point out that this could put even more pressure on already heavily-policed neighborhoods and groups. Only AI tools such as VeriPol that discover false robbery reports managed to avoid harsh criticism.
Being one step ahead of criminals
A big screen that displays a city map and red boxes showing areas where the next crime might occur isn’t a scene from a Hollywood blockbuster. Rather, that’s how more than 50 police departments in the US operate after they’ve bought PredPol, an AI algorithm that attempts to predict where crime might happen. The software helps law enforcement agencies by analyzing and finding patterns in historical data such as crime type, crime location, and crime date and time, and pinpointing areas as small as 500×500 feet that are the most crime-prone. Police officers can then spend more time patrolling those neighborhoods and potentially preventing someone from breaking the law.
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