The Naked Future (25 page)

Even if the chain of causation between housing vouchers and violent crime wasn't clear, the relationship was still a useful guide for predicting where crime was going to occur. Janikowski had to make this case.

He sat down at a local cafeteria with Memphis police director Larry Godwin, local district attorney Bill Gibbons, and representatives from the department's Organized Crime Unit. Janikowski was blunt. He told them that to better focus their efforts and get more value for their money, they had to go back over arrest records and take a better look at when and where crimes were occurring.
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Operation Blue CRUSH (Crime Reduction Utilizing Statistical History) was born. The system used IBM's SPSS program and mapping software from Esri to better capture and disseminate crime data. When the initial test in an area in East Memphis called Hickory Hill proved successful at bringing down crime at less expense, the department increased the number of police working the tourist areas around Beale Street after 11
P.M
., then they focused on the relatively rough-and-tumble area that is today called Legends Park but that at the time was a seventy-year-old, soon-to-be-condemned housing development called Dixie Homes.

Blue CRUSH uses primarily rule-induction algorithms. In terms of complexity these lie somewhere between a neural net and a straight statistical regression. It's a learning program that comes up with its own rules for what different variables should weigh based on training
data its programmers have exposed it to (this process of coming up with rules is the induction part). It's still a varying weight model but one with more traceable results.

The Memphis PD also looked at a lot more variables than the nine (or so) different factors that Olligschlaeger modeled. In addition to weather patterns, seasonality, and area demographics, they could also model lighting conditions with a particular focus on garages and alleys. They looked at when big local employers issued paychecks by time of the week, the month, and the year and what times of day people went to and left work.

The same location optimization techniques that companies such as Esri provide to retail chains to find the best neighborhood to place a new store are also useful in mapping relationships between crime, economics, and physical space. “We can not only just manage what is this dot on the map that we call ‘burglary' or ‘robbery,' but how does that dot on the map interact with the demographics of the area, home values or population trends,” said Mike King, Esri's national law enforcement manager. “If you're in a predominantly blue-collar neighborhood that works at factories, what happens every other Friday when it's payroll time? Do we see increases in alcohol-related events? Do we see increases in domestic violence?”

Here's why the way these models work matters to the naked future: as we develop the capacity to monitor more of these signals and incorporate more variables, the statistical tools required to make use of them will become simpler and more transparent. (It's hard to conceive of practitioners today using a neural net, which is considered rather quaint.) As transparency increases, governmental decision makers will have an easier time accepting and supporting predictive policing programs. As more departments begin to use such programs, and share information about which variables and tools are most useful, these programs could get a lot better very quickly.

Changes in area economics have emerged as a useful signal for future crime predicting, but it's not a
clear
signal. If a sizable portion of the people in your neighborhood suddenly can't afford to pay
their phone bills, or are facing vehicle repossession, that can be indicative of more potential criminals since clearly these people have fallen on rough times. But a sudden rise of neighborhood inequality is also an indicator since part of the neighborhood now
perceives
itself to be less well-off compared with its neighbors. Criminality, like envy, can be contagious.

In 2005 a military base reconstruction project left many residents of a particular San Antonio neighborhood suddenly a lot better-off than their neighbors. A big base realignment and closing program resulted in a bump in demand for a very particular type of contractor. Neighborhoods that had been fairly uniform economically were suddenly divided into haves and have-nots. Cornell researchers Matthew Freedman and Emily G. Owens showed that “because of the targeted nature of the spending program, an important effect of this program was to increase the criminal opportunities of the average San Antonian.”
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But suddenly losing your job can also make you more likely to become a
victim
of crime. Janikowski found that when a group of women in Memphis who couldn't afford a landline were forced to make telephone calls from a pay phone on the side of a convenience store, their risk of suffering sexual assault increased.

One of the strongest indicators that crime in a given neighborhood is about to jump is foreclosures. Foreclosed homes invite burglars who ransack the residences for copper in wires and electrical equipment. Drug dealers seem to feel relatively comfortable working in a neighborhood the residents have been pushed out of by landlords and banks. Focusing on foreclosure clusters, and putting cops nearby as soon as the cluster appears, is broken-windows theory 2.0. Rather than react to neighborhood dereliction, it anticipates it.

Within the broader variable of seasonality, there's a lot of nuance. When the Memphis department focused a heavy police presence downtown during the end of summer, they were able to preempt a rash of burglaries and vehicle break-ins that would normally have been perpetrated by teenagers about to go back to
school. In one week the PD dropped crime in that precinct by 37 percent compared with the previous year
.
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The data collection and the analysis that made these predictive insights possible are accelerating and becoming cheaper. Mobile computing and the Internet of Things are allowing officers in the field to collect and disseminate incident data, and better access data from one another, much faster.

Today, police officers on the beat have the same rapidly evolving view of potential hot spots that headquarter dispatchers had a few years ago. Big command and control centers are moving away from situation rooms, where operators on headsets feed information to soldiers on the ground, and into a single console that patrol officers carry with them. The goal to make that information assimilation process work in a mobile environment is one of the key jobs of Mike King at Esri.

For instance, let's say you're a cop looking for a robbery suspect late at night. You know the general part of town the perpetrator is in but need more information to nail down a location. Let's say you have access to a big data set indicating tens of thousands of arrest records and you can query that database to learn the type of place that most suspects of this crime go after a robbery. It could be home, girlfriend's house, mom's house, bar, et cetera. Let's say you can also bring up a map to show you all of the closed-circuit television (CCTV) cameras in the area of the incident and even which stores are open late, where you might be able to find a witness who saw something. You can further ask a network of community members and other cops to mark on a digital map where they saw something that could be useful—an article of clothing left behind by the subject, a stolen item, a sighting of someone matching the subject's description. You now have not just one map but several that you combine to rapidly narrow down where to find your suspect and even obtain the evidence for conviction. Esri, working with police departments around the world, is putting that command center view on laptops and even phones. This is the challenge that occupies King: “How do I get that information into the
officer's hands so that he can be there at that same time?” The NYPD, in partnership with Microsoft, has also been developing these sorts of capabilities for New York City beat cops through a program called “the dashboard.”
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To understand how that contributes to a more naked future, you have to imagine that dashboard dissemination of capability continuing, eventually, on to consumers. In the same way computers used to be the size of rooms and were available for experts to use, then became objects people could access on desktops, and are now objects in our pockets, the dissemination of this type of big police data is going to follow the same path.

In the next ten years there's no technological, economic, or even legal reason why every individual with a smartphone shouldn't be able to download a live crime map showing both current and expected hot spots. Predictive policing won't just be something that happens around you, it will be a process that you participate in directly. Information will grow much more rich and meaningful when it's combined with other bits of local data and personal information. I asked Mike King if he saw this eventuality as likely. “It's happening today,” he answered. “When I talk about Esri moving through mobile and other opportunities, that's the idea of getting this water to the end of the row.”

Ever better and more timely reporting of crimes and incidents are key to the continual improvement in predictive policing. Increasingly, that reporting is being done by bots.

New York, Milwaukee, and nearly seventy other cities around the United States use a sensor network system called ShotSpotter, which uses acoustics to detect and pinpoint gunfire the moment the trigger is pulled. In California and along the eastern seaboard, cameras snap pictures of license plates as cars enter and leave specific areas of various cities. That's on top of a growing CCTV infrastructure, which, in 2011, comprised more than 45 million systems around the globe. Growing by 33 percent per year, the global CCTV market is forecast to become a $3.2 billion per year industry by 2016.
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Not all of those cameras will be attached to buildings. In
2015 police departments around the country will begin testing aerial drones to establish a permanent eye in the sky in cities around the country, as authorized by H.R. 658, the FAA Modernization and Reform Act of 2012 signed into law by President Obama.
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As a result of this bill, thirty thousand unmanned aerial vehicles will be crisscrossing America by 2020, according to Todd Humphreys of the University of Texas at Austin.
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There's a huge industry incentive to make it seem as though the growing web of cameras, microphones, sensors, and robot planes keeping watch over us is making us safer. Unfortunately, predictive policing won't automatically fix any of the long-term issues that plague our criminal justice system or change the way many cops interact with residents in poor neighborhoods. Zero-tolerance policies—of which predictive policing programs often serve as a component—are really effective at putting people behind bars. In a country with the highest prison population rate on the planet, that's like taking a machine that produces a terrible product, say, exploding strollers, and “improving” it not by changing the design of the strollers but by enabling it to produce many more exploding strollers far faster and more cheaply. Even in places where every criminal is truly a threat to public health (which is no place), pumped-up arrests will exacerbate prison overcrowding, recidivism, and so forth.
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But the most striking abuses of predictive policing programs and surveillance in general will likely soon emerge from China. China will surpass the United States as the world's largest market for surveillance equipment by 2014, according to a report from the Homeland Security Research Corporation (HSRC). The manufacturing hub of Guangdong Province, which is near Hong Kong, boasts a $1-million-camera-security system. China is today spending more on internal security than it is on defense, but many in the West, including NATO and the U.S. DOD, claim that Chinese military funding and “public safety” funding overlap a great deal.

Predictive policing in the wrong hands looks less like a boon to public safety and more like a totalitarian hammer. Some predictive policing tactics have already been used to stifle dissent and protest in
the United States. In 2003 Miami police targeted and arrested several demonstrators prior to a major protest against the Free Trade Area of the Americas (FTAA) agreement. Today, police around the country routinely employ espionage tactics to predict and preempt spontaneous punk and dance shows (under the expansive and poorly written 2003 RAVE Act, sponsored by Joe Biden, which can be used to arrest concert promoters for the behavior of their patrons). If you're a police chief or mayor, preempting a protest is less risky than trying to disrupt one in progress, especially in an age where the kids you will be pepper spraying carry TV studios in their pockets.

This is bigger than busting garage punk shows, squashing Occupy marches before they take place, and shutting down raves before the speakers are even plugged in. It's bigger than the enforcement of vaguely worded local nuisance ordinances. The same tactics that can give police advance awareness of local protest events can also be used to predict civil demonstrations, marches, and clashes halfway around the world.

Acting locally is now visible globally.

The date is June 30, 2012. Computer scientist Naren Ramakrishnan is in his Virginia Tech lab watching a map of the Americas on his computer screen. A band of hundreds of red dots hovers over Mexico City; another band is over the Brazil-Paraguay border. The dot cluster is ringed by concentric circles of yellow, green, and blue. It looks almost like a radiant heat map, as though the capital of Mexico and the Brazilian border town of Foz do Iguaçu are on fire, but they aren't—at least, not yet. These dots represent geo-tagged tweets containing the terms
“país,” “trabajador,” “trabaj,”
“president,” and “protest.” The controversial Enrique Peña Nieto is about to be officially elected the president of Mexico and the geo-tagged tweets represent a march taking form to protest his election.