Predictive analytics in policing: Weighing up the pros and cons

Driving community engagement and crime prevention

Such tools can also pinpoint loci for community engagement to fight challenges such as Dubai’s illegal car racing and certain types of youth-focused crime. In such cases, the data can empower municipality working with police to devise prevention strategies.

“Predictions can drive that behaviour from the city as a whole, rather than just by policing alone. People think of police on patrol: where is the cop on the beat? But just having a chat with the locals, building relations in communities, can all be driven by predictive policing technologies,” Kar says.

Rob Hankin, chief technology officer of data analytics consultant Cybit, says the Strategic policing partnership board’s policing vision 2030 strategy emphasises the potential of data analytics to drive trust, security and active policing.

“I hear the negative side a lot. But over the years, we’ve worked with West Yorkshire, Northumbria, Lincolnshire, Wiltshire, Northamptonshire police,” he says, noting that predictive policing really can cover anything from automating reports or other basic activities to “more strategic” work. “We proved this really can work.”

For example, Cybit worked with Northumbria Police on an initiative targeting serious violence, including knife crime and acid attacks. Home Office funding for that went to extra policing resources, including equipment such as body-cams as well as data analytics with a predictive AI element, and Cybit looked at chat and analysis around hot spots.

Data can drive cross-station or cross-force cooperation to understand dynamic patterns of crime and design preventive measures, and it can be used to improve victim updates, reduce task numbers, and assist monitoring or management.

Developing a better understanding of crime

Hankin adds: “When we worked with Lincolnshire police, policing information was very localised to station level. Using predictive analytics meant we could show where actual commonality, clustering and outliers took place, to be able to deliver information that suggested a particular cluster of burglaries could be potentially related.”

A detective-inspector shopped the analysis around other police stations, which confirmed the burglaries were being understood only as isolated events. Potential connections surfaced by the data meant police could deploy into the right areas at the right times.

Data can counter bias too. A good data-driven analysis can expose and connect facts that enable them to hit upon a correct solution. In this case, burglaries were in areas beside a stretch of motorway. An undercover team might have been deployed based on whoever was doing overtime, but the data revealed both that the burglaries were clustered and that they happened on a particular evening at certain times.

“They deployed resource much more tactically than they would have done,” Hankin says.

Helen Kimber, data scientist for justice and public safety at security solutions and services provider Genetec, agrees. “The idea is that much written information about crimes, particularly burglaries, is really difficult for analysts to bring together,” she says. “For instance, there’s a big difference between a burglar who comes with a tool or is methodical, versus someone more opportunistic.”

That said, many projects today are not yet themselves predictive but about organising and making sense of troves of related data on offences and their context, such as where and when they were previously committed. Resulting data clusters and correlating metadata will ultimately go into developing sound predictive analytics for policing.

Transparency is key to reduce bias risk – so Kimber focuses on building explainable artificial intelligence (AI), so police can testify in court and explain how an algorithm helps them to reach a particular conclusion. Kimber points out that this is one reason humans should make the final policing decisions, taking potential biases into account, based on that data.

James Nahon, head of consulting, public safety and defence at NTT Data UK and Ireland, agrees on the need for “proper governance” – including full accountability, fairness, transparency, explainability, contestability and redress.

Predictive policing, however, can genuinely help tackle safeguarding risks – for example, are missing persons 10 years later at greater risk of a given threat? And shared data can help councils to manage problems in their area, such as anti-social behaviours.

“You can look at a regression model to do predictive policing, reducing administrative burdens and looking at efficiencies, rather than a human having to look through an entire ‘missing persons’ dataset,” Nahon says.

NTT says that, done right, there is potential for predictive AI and analytics approaches that drive public wellbeing. Although, like with anything, there is typically always going to be room for improvement, which is part of why a system with transparency and redress is required.

Organisations such as the UK’s StateWatch have been increasingly alarmed by the “predictive policing” concept, singling out “murder prediction” research by the Ministry of Justice and Greater Manchester Police. They are also concerned by the data-sharing involved, pointing out the risk of bias and structural racism, especially from misuse of personal data.

However, they do not try to argue that it’s not possible to make predictions that prevent crime. A peer-reviewed 2024 study of big-data driven policing from University College London examines 161 previous papers, finding that only six were “evidence strong” with regards to the effectiveness or otherwise of big-data driven policing. Their conclusion was that more research is needed on which policy-makers can rely.

Irene van Droffelaar, senior analyst for defence, security and justice research at Rand Europe, says using any kind of data to improve policing is difficult as risks from historical, survivor and collection bias are real. The most detailed data is on successful cases, where a suspect was apprehended or when something is recorded – for example, riding an unlit bicycle is likely only recorded if the rider is fined.

So, techniques should always be evaluated relative to current alternatives, and police only have data on past cases, while modus operandi adapt quickly to changes in policing, she says. There is no ideal or perfect system.

“The alternative [to data-based prediction], realistically, would be departments or individual officers making their own judgement, using their own heuristics – for example, when deciding where to patrol. These heuristics are often affected by similar challenges to AI systems,” Droffelaar adds.