In retail, the ability to predict trends can generate sales. In law enforcement, it can save money. “For every crime that was prevented, you didn’t have to arrest anyone,” says McCue, whose targeted Richmond initiative meant that the department could deploy 50 fewer officers on a single New Year’s Eve, saving $15,000 in personnel costs. “You didn’t have the time associated with processing and booking criminals. You didn’t have the costs associated to hold them if they needed to be held prior to trial, or the judicial resources to try them or the correctional resources to incarcerate them.”
Retailers also spend a lot of time thinking about space, McCue says. “How do you move someone through a store? How do you position things on shelves? We’re doing similar things. We’re asking, How do bad guys move through communities? How can we position our policing assets to be unfavorable to crime?” She says that someday police officers will be as adept at predicting what branch a bank robber will hold up next as Netflix or Amazon are at predicting what movie or book a customer will like. The data analysis incorporates not only the past behavior—the “likes”—of that particular consumer or criminal based on what books they bought or cars they burglarized, but also the preferences demonstrated by other, similar buyers, or bad guys. “Just knowing that a relationship exists, Walmart can make sure they have enough Pop-Tarts on their shelves to meet demand,” McCue says. A big-box store doesn’t need to understand why people crave toaster treats when the wind begins to howl, just as cops don’t need to understand why criminals fire guns or steal cars. They just need to know where and when.
Before we leave Linden Street, Clark records our drive-through on his dashboard computer. Every check-in means more data, and, after the six month trial, the recorded check-ins will help Mohler determine how effective the program has been. Clark runs a check on the Hamburglar’s license plate, which doesn’t turn up anything. He then looks at the hot-spot maps and we head downtown, toward a triple-decker parking garage that’s been flagged all week. “You can see how something like this has a high potential for auto burglary,” Clark says as we zigzag up toward the open roof. “You’re isolated. You’ve got lots of areas to exit. You can walk up here with your backpack, smash a window, grab a purse, and go.”
One of the most common criticisms of predictive policing is that it will not tell police officers anything they don’t know already. In Santa Cruz, some officers work Sunday through Wednesday, Clark says, while others are on a Wednesday-through-Saturday schedule. There are three shifts during the day, and every four months the officers change shifts. Officers don’t necessarily talk to colleagues who aren’t on the same watch, which can lead to gaps in the collective knowledge of the department. Inevitably, some members of the force will be new to the job, new to the area or simply less vigilant than they could be. There isn’t always a good system (or any system at all) for capturing the institutional knowledge of a retiring officer. And although their experience may be invaluable, not even the best officers can process information the way a computer can. “The human brain cannot weigh more than three or four variables at one time,” says Sean Malinowski, a captain at the Los Angeles Police Department who plans to launch a predictive-policing program of his own. Whereas humans are emotional and our perceptions easy to influence, a computer is impartial. Clark notes the 12:00 and 14:00 on his map. “What wouldn’t necessarily ping are these high-probability time windows,” he says.
Afternoon passes, and the evening shift begins its patrols. Calls coming in over the radio increase: We respond to reports of a stolen car, an assault in a public restroom, and an eight-year-old girl described as wearing a pink T-shirt and Hello Kitty flipflops missing on the beach (she’s quickly found). The only arrest I witness is of a painfully skinny, needle-marked woman picked up for snatching a purse. The bag in question belongs to a college student, one of a group of teenagers living at the dingy Peter Pan Motel a couple of blocks from the boardwalk. As the police officers book the snatcher, I stand around talking with the kids, Christians from the deep South who have devoted their summer to selling soft-serve and T-shirts and spreading the gospel on the California coast. I try to explain my own mission: the maps, the statistics, the effort to stop crimes before they even take place. “Sounds like Minority Report,” one of the kids says.single page
Five amazing, clean technologies that will set us free, in this month's energy-focused issue. Also: how to build a better bomb detector, the robotic toys that are raising your children, a human catapult, the world's smallest arcade, and much more.