The Los Angeles Police Department, like many urban police forces today, is both heavily armed and thoroughly computerised. The Real-Time Analysis and Critical Response Division in downtown LA is its central processor. Rows of crime analysts and technologists sit before a wall covered in video screens stretching more than 10 metres wide. Multiple news broadcasts are playing simultaneously, and a real-time earthquake map is tracking the region’s seismic activity. Half-a-dozen security cameras are focused on the Hollywood sign, the city’s icon. In the centre of this video menagerie is an oversized satellite map showing some of the most recent arrests made across the city – a couple of burglaries, a few assaults, a shooting.
On a slightly smaller screen the division’s top official, Captain John Romero, mans the keyboard and zooms in on a comparably micro-scale section of LA. It represents just 500 feet by 500 feet. Over the past six months, this sub-block section of the city has seen three vehicle burglaries and two property burglaries – an atypical concentration. And, according to a new algorithm crunching crime numbers in LA and dozens of other cities worldwide, it’s a sign that yet more crime is likely to occur right here in this tiny pocket of the city.
The algorithm at play is performing what’s commonly referred to as predictive policing. Using years – and sometimes decades – worth of crime reports, the algorithm analyses the data to identify areas with high probabilities for certain types of crime, placing little red boxes on maps of the city that are streamed into patrol cars. “Burglars tend to be territorial, so once they find a neighbourhood where they get good stuff, they come back again and again,” Romero says. “And that assists the algorithm in placing the boxes.”
Romero likens the process to an amateur fisherman using a fish finder device to help identify where fish are in a lake. An experienced fisherman would probably know where to look simply by the fish species, time of day, and so on. “Similarly, a really good officer would be able to go out and find these boxes. This kind of makes the average guys’ ability to find the crime a little bit better.”
Predictive policing is just one tool in this new, tech-enhanced and data-fortified era of fighting and preventing crime. As the ability to collect, store and analyse data becomes cheaper and easier, law enforcement agencies all over the world are adopting techniques that harness the potential of technology to provide more and better information. But while these new tools have been welcomed by law enforcement agencies, they’re raising concerns about privacy, surveillance and how much power should be given over to computer algorithms.
P Jeffrey Brantingham is a professor of anthropology at UCLA who helped develop the predictive policing system that is now licensed to dozens of police departments under the brand name PredPol. “This is not Minority Report,” he’s quick to say, referring to the science-fiction story often associated with PredPol’s technique and proprietary algorithm. “Minority Report is about predicting who will commit a crime before they commit it. This is about predicting where and when crime is most likely to occur, not who will commit it.”
PredPol is now being used in a third of the LA Police Department’s 21 geographic policing divisions, and officers on patrol are equipped with maps sprinkled with a dozen or more red boxes indicating high probabilities of criminal activity. For now, the LAPD is focusing on burglary, vehicle break-ins and car theft – three types of crime that last year made up more than half of the roughly 104,000 crimes recorded in LA.
Dozens of other cities across the US and beyond are using the PredPol software to predict a handful of other crimes, including gang activity, drug crimes and shootings. Police in Atlanta use PredPol to predict robberies. Seattle police are using it to target gun violence. In England, Kent police have used PredPol to predict drug crimes and robberies. Brantingham notes that Kent police are taking a more proactive approach by not only concentrating officers in prediction areas, but also civilian public safety volunteers and drug intervention workers.
The prediction algorithm is constantly reacting to crime reports in these cities, and a red box predicting crime can move at any moment. But although officers in the divisions using PredPol are required to spend a certain amount of time in those red boxes every patrol, they’re not just blindly following the orders of the crime map. “The officer still has a lot of discretion. It’s not just the algorithm,” Romero says. “The officer still has to know the area well enough to know when to adjust and go back into manual.”
Clicking on a few of the boxes for more detail, Romero brings up Google Street View images of the predicted crime areas. Two are centred on the car parks of big box stores, not particularly surprising places for car break-ins and thefts, says Romero. But Brantingham contends that the algorithm is doing much more than just telling cops what they already know.
“Crime hotspots are incredibly dynamic,” he says. “Yes, there are bad sides of town and good sides of town, but within those broad distinctions crime hotspots pop up and spread and disappear and pop up again in really complicated ways that are just very, very difficult, if not impossible, for the individual to intuit.”
Read more at The Guardian.