Predictive policing: Don’t even think about it

Magnifying Glass (Photo credit

It is getting easier to foresee wrongdoing and spot likely wrongdoers

THE meanest streets of Kent are to be found in little pink boxes. Or at least they are if you look at them through the crime-prediction software produced by an American company called PredPol. Places in the county east of London where a crime is likely on a given day show up on PredPol’s maps highlighted by pink squares 150 metres on a side. The predictions can be eerily good, according to Mark Johnson, a police analyst: “In the first box I visited we found a carving knife just lying in the road.”

PredPol is one of a range of tools using better data, more finely crunched, to predict crime. They seem to promise better law-enforcement. But they also bring worries about privacy, and of justice systems run by machines not people.

Criminal offences, like infectious disease, form patterns in time and space. A burglary in a placid neighbourhood represents a heightened risk to surrounding properties; the threat shrinks swiftly if no further offences take place. These patterns have spawned a handful of predictive products which seem to offer real insight. During a four-month trial in Kent, 8.5% of all street crime occurred within PredPol’s pink boxes, with plenty more next door to them; predictions from police analysts scored only 5%. An earlier trial in Los Angeles saw the machine score 6% compared with human analysts’ 3%.

Intelligent policing can convert these modest gains into significant reductions in crime. Cops working with predictive systems respond to call-outs as usual, but when they are free they return to the spots which the computer suggests. Officers may talk to locals or report problems, like broken lights or unsecured properties, that could encourage crime. Within six months of introducing predictive techniques in the Foothill area of Los Angeles, in late 2011, property crimes had fallen 12% compared with the previous year; in neighbouring districts they rose 0.5% (see chart). Police in Trafford, a suburb of Manchester in north-west England, say relatively simple and sometimes cost-free techniques, including routing police driving instructors through high-risk areas, helped them cut burglaries 26.6% in the year to May 2011, compared with a decline of 9.8% in the rest of the city.

For now, the predictive approach works best against burglary and thefts of vehicles or their contents. These common crimes provide plenty of historical data to chew on. But adding extra types of information, such as details of road networks, can fine-tune forecasts further. Offenders like places where vulnerable targets are simple to spot, access is easy and getaways speedy, says Shane Johnson, a criminologist at University College London. Systems devised by IBM, a technology firm, watch how big local events, proximity to payday and the weather affect the frequency and location of lawbreaking. “Muggers don’t like getting wet,” says Ron Fellows, IBM’s expert. Jeff Brantingham of PredPol thinks that finding speedy ways to ingest crime reports is more important than adding data sets. Timelier updates would allow PredPol to whirr out crime predictions constantly, rather than once per shift. Mr Fellows enthuses about sensors that detect gunshots (already installed in several American cities) and smart CCTV cameras that recognise when those in their gaze are acting suspiciously. He promises squad cars directed by computers, not just control centres, which could continually calculate the most useful patrol routes.

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