Editor’s note: NBC News MACH has partnered with the American Museum of Natural History to present three documentary films at the Margaret Mead Film Festival, which will be held in New York City from Oct. 19-22. In “Pre-Crime,” filmmakers Monika Hielscher and Matthias Heeder examine the rise of predictive policing, which aims to stop crime before it occurs. The film led MACH to explore this new tool for law enforcement — its power and potential peril.
We all know police officers respond to crimes after the fact. But what if cops could learn about crimes before they occur — and take steps to prevent them? That’s the promise of predictive policing, a high-tech approach to public safety that uses data about previous crimes to forecast new criminal acts.
It’s a bit like weather forecasting, only for crime: At the beginning of their shifts, officers review a map showing areas where crimes are expected to occur, based on arrest records and the dates and locations of recent crimes. The cops review the information and then head out on their beats, armed with information of where they should go at which times — and even who the bad guys are likely to be.
“Police already know where the bad neighborhoods are,” says Daniel Neill, a Carnegie Mellon University computer scientist who helped create a crime-forecasting software tool called CrimeScan. “What they don’t always know is the dynamics — like when a bad neighborhood is suddenly going to see a flare-up in crime. Those are the sorts of questions [predictive policing] can answer.”
If this scenario sounds like something ripped from some sci-fi novel, get this: Legal experts think predictive policing could go a step further, with law enforcement officers using brain scans and genetic information to identify children who seem likely to engage in criminal acts when they get older.
Obviously, the technology raises thorny ethical questions. Ensuring that predictive policing algorithms enhance safety without worsening existing racial and socioeconomic biases will be difficult.
“There’s a massive opportunity for using big data to have positive social impact,” says Nuria Oliver, chief data scientist at Data-Pop Alliance, an organization that uses digital information to tackle social problems. “But at the same time, we need to be aware of its limitations and be honest in terms of its performance.”
Crime and Geophysics
The idea of forecasting crime dates back at least to the 1990s, when the U.S. Department of Justice launched projects aimed at developing statistical models of where crime might occur. The maps produced were crude — and because of limitations in computing power at the time, they were unable to handle large amounts of input data.
But the efforts led social scientists like University of California anthropologist Jeffrey Brantingham to think about spatial patterns of crime and equations that might help simulate such activity.
“A given crime can have two possible causes in a statistical sense,” says Brantingham, the creator of PredPol, a predictive policing software now used by more than 60 U.S. law enforcement agencies. “It can spontaneously arise out of the background, or a crime can be a contagious effect of some previous crime.”
The seeds of PredPol were sown in 2006, when Brantingham and his collaborators began building mathematical models to predict crime by borrowing equations from an unexpected source: geophysics. It turned out that the same math used to estimate a geographical region’s odds of experiencing an earthquake could also be used to estimate whether a given location might see criminal activity.
The forecasts are based on historical records. Just as an area that experienced an earthquake of a certain magnitude has specific odds of being shaken again, neighborhoods that have experienced a certain amount of crime are likely to have a predictable likelihood of crime in the future.
What Research Has Shown
Brantingham’s team partnered with the Los Angeles Police Department to run a controlled experiment to test their ideas. Officers in three districts in L.A. were given instructions to patrol 20 half-block areas based on predictions from either professional crime analysts or Brantingham’s algorithm.
The results were striking. After 21 months, the mathematical model was pinpointing crime hotspots twice as accurately as the professionals, leading to a greater reduction in crime in places where it was used.
The experiment showed the power of the crime-prediction algorithm, says Brantingham. Police might already be able to identify the first-, second-, and third-most crime-prone areas in their city, but could they definitively point to the ninth? Or the fifteenth? A computer’s calculations are often much better than personal experience or human intuition at determining whether it’s best to deploy officers to the seventeenth most crime-prone spot versus the eighteenth.
Lessons from Epidemiology
Around the time PredPol was being developed, Daniel Neill, the Carnegie Mellon researcher, was working with his colleague Wilpen Gorr to tackle violent crime prediction using mathematical models borrowed from epidemiology. Both disease and crime tend to cluster in specific geographic areas, the researchers realized, and so it turns out that predicting the latter isn’t too different from predicting the former.
For example, gang violence might start with “somebody from gang A punching somebody from gang B,” Neill explains. “The next day, gang B comes back with baseball bats. The following day gang A comes back with guns.”
The research done by Neill and Gorr led to the development of a crime-predicting software tool known as CrimeScan. It uses historical crime data as well recent 911 calls and police reports of minor crimes like disorderly conduct, narcotics trafficking, and loitering to show where violent crime is likely to occur.
CrimeScan was tried first in Chicago and is now being deployed in Pittsburgh, where police are running an experiment to test its effectiveness much like the one conducted for PredPol.