Policing future, taking one crime at a time
LOS ANGELES: Sergeant Charles Coleman popped out of his police SUV and scanned a trash-strewn street popular with the city’s homeless, responding to a crime that hadn’t yet happened.
It wasn’t a 911 call that brought the Los Angeles Police Department officer to this spot, but a whirring computer crunching years of crime data to arrive at a prediction: An auto theft or burglary would probably occur near here on this particular morning.
Hoping to head it off, Coleman inspected a line of ramshackle RVs used for shelter by the homeless, roused a man sleeping in a pickup truck and tapped on the side of a shack made of plywood and tarps.
“How things going, sweetheart?” he asked a woman who ambled out. Coleman listened sympathetically as she described how she was nearly raped at knife point months earlier, saying the area was “really tough” for a woman.
Soon, Coleman was back in his SUV on his way to fi ght the next pre- crime. Dozens of other LAPD officers were doing the same at other spots, guided by the crime prognostication system known as PredPol.
“Predictive policing” represents a paradigm shift that is sweeping police departments across the country. Law enforcement agencies are increasingly trying to forecast where and when crime will occur, or who might be a perpetrator or a victim, using software that relies on algorithms, the same maths Amazon uses to recommend books.
“The hope is the holy grail of law enforcement - preventing crime before it happens,” said Andrew Ferguson, a University of the District of Columbia law professor preparing a book on big data and policing.
Now used by 20 of the nation’s 50 largest police forces by one count, the technologies are at the center of an increasingly heated debate about their effectiveness, potential impact on poor and minority communities, and implications for civil liberties.
Some police departments have hailed PredPol and other systems as instrumental in reducing crime, focusing scarce resources on trouble spots and individuals, and replacing officers’ hunches and potential biases with hard data.
But privacy and racial justice groups say there is little evidence the technologies work and note the formulas powering the systems are largely a secret. They are concerned the practice could unfairly concentrate enforcement in communities of colour by relying on racially skewed policing data.
And they worry that officers who expect a theft or burglary is about to happen may be more likely to treat the people they encounter as potential criminals.
The experiments are one of the most consequential tests of algorithms that are increasingly powerful forces in our lives, determining credit scores, measuring job performance and fl agging children who might be abused. The White House has been studying how to balance the benefits and risks they pose.
“The technical capabilities of big data have reached a level of sophistication and pervasiveness that demands consideration of how best to balance the opportunities afforded by big data against the social and ethical questions these technologies raise,” the White House wrote in a recent report.
It was 6.45am on a Monday, but the sheet of paper Coleman held in his hands offered a glimpse of how Oct 24 might go: an auto theft near Van Nuys and Glenoaks, a burglary at Laurel Canyon and Roscoe, and so on.
The crime forecast is produced by PredPol at the beginning of each shift. Red boxes spread across Google maps of the San Fernando Valley, highlighting 500- square-foot by- 500- squarefoot locations where PredPol concluded property crimes were likely.
The forecast is cutting edge, but it is used in the service of an old-fashioned policing philosophy: deterrence. Between calls that day, Coleman and other officers were expected to spend time and engage with people in the roughly 20 boxes PredPol identified around the Foothill Division.
Coleman sat behind the wheel of his SUV, plotting which boxes to hit the way someone consulting a weather map might weigh whether to bring an umbrella.
“It’s not always that we are going to catch someone in the box, but by being there we prevent crime,” said Capt. Elaine Morales, who oversees the Foothill Division.
Foothill is far from the glitz of Hollywood on the northern edge of L. A., but it has been at the centre of the transformation going on in policing.
The division was one of the fi rst in the nation to adopt predictive policing five years ago and has helped refi ne PredPol. The technology has spread to other LAPD divisions and more than 60 other departments across the country, making it the nation’s most popular predictivepolicing system.
PredPol often draws comparisons to the movie “Minority Report,” in which a government unit rounds up future criminals who have not yet committed crimes, but one of the software’s developers said it is not a crystal ball.
Jeff Brantingham, a professor at UCLA, said crime often seems random, but it follows patterns.
“The question becomes, ‘Can we build mathematical structures to understand these patterns?’ The answer is yes, absolutely,” Brantingham said. “The best way to capture the way we think about crime patterns . . . is to think about earthquakes.”
That’s not just an analogy. Brantingham said a breakthrough moment in PredPol’s development came when one of his partners realised an algorithm that described seismic activity could be used to predict crime.
Just as earthquakes happen along fault lines, Brantingham explained, research has shown crime is often generated by structures in the environment, like a high school, mall parking lot or bar. Additional crimes tend to follow the initial event near in time and space, like an aftershock.
PredPol uses years of crime data to establish these patterns, and then the algorithm uses near real-time crime data to predict the next property crime. Other systems use even more esoteric data - from the weather to phases of the moon - to arrive at their crime forecasts. But does it work? Coleman fi red up his SUV and headed out to a PredPol box as streaks of light poured over the dry hills surrounding L. A. Parents walked their kids to school, and others rushed to work in the blue- collar, largely Latino community.
Coleman’s SUV was one of only eight police cruisers circulating that morning in the Foothill, a 46- square-mile area that has a population of more than 180,000. It’s easy to see why any system that could accurately pinpoint crime would be a major boon to the LAPD.
For decades, police departments have mapped crimes using pushpins on paper maps and more recently blotchy hot- spot maps on computers. The maps always lagged behind crime on the street and could offer only general areas to focus patrols.
Coleman, a strapping and gregarious 26-year veteran of the LAPD, said he and other officers were initially sceptical that PredPol could anticipate a crime better than a seasoned officer - and do it in a box the size of a city block.
But he quickly became a believer.
“If you spend three hours in that box the week after you had 10 crimes, the next week you are going to see three,” Coleman said as L. A.’s low- slung houses, palm trees and strip malls slid by the SUV’s windows.
LAPD Cmdr. Sean Malinowski, who pioneered PredPol’s use in the department, was also convinced. He relayed a story of how two of his officers found a thief in a stolen car in an area where PredPol predicted an auto theft. The person escaped, but the officers found him again - in another stolen vehicle in another box where PredPol forecast a theft.
But the data on the effectiveness of PredPol - and other predictive systems - presents a murkier picture.
PredPol and the LAPD credit the system with helping bring about substantial reductions in property crime in the Foothill in 2012 through 2014, but crime has crept back up in the past couple years as it has in the rest of Los Angeles. — WP-Bloomberg
The hope is the holy grail of law enforcement - preventing crime before it happens. Andrew Ferguson, a University of the District of Columbia law professor