Can Computers Predict Crimes?

Columbo would have hated the latest trend in crime-fighting. And it definitely would have made Dirty Harry even more unhinged.

But Sherlock Holmes, now he would have been impressed. The logic, the science, the compilation of data–all the stuff of Holmesian detective work.

I’m talking about something known as predictive policing–gathering loads of data and applying algorithms to deduce where and when crimes are most likely to occur. Late last month, the Los Angeles Police Department announced that it will be expanding its use of software created by a California startup named PredPol.

For the past six months, police in that city’s Foothill precinct have been following the advice of a computer and the result, according the the LAPD, is a 25 percent drop in reported burglaries in the neighborhoods to which they were directed. Now the LAPD has started using algorithm-driven policing in five more precincts covering more than 1 million people.

PredPol’s software, which previously had been tested in Santa Cruz–burglaries there dropped by 19 percent–actually evolved from a program used to predict earthquakes. Now it crunches years of crime data, particularly location and time, and refines it with what’s known about criminal behavior, such as the tendency of burglars to work the neighborhoods they know best.

Before each shift, officers are given maps marked with red boxes of likely hot spots for property crimes, in some cases zeroing in on areas as small as 500 feet wide. They’re told that whenever they’re not on calls, they should spend time in one of the boxes, preferably at least 15 minutes of every two hours. The focus is less on solving crimes, and more on preventing them by establishing a high profile in crime zones the computer has targeted.

Taking it to the streets

So, isn’t this pretty much what police always have done? Don’t they figure out patterns and spend most of their time patrolling high-crime areas? Well, yes and no. Good cops know trouble spots and veteran ones rely on what they’ve learned about a place over the years. But that’s largely based on personal experience and instinct, not statistical analysis.

It’s also true that many cities already have embraced CompStat, a law enforcement strategy launched in New York City in the mid-1990s and built around analysis of crime reports. CompStat was a big leap forward in applying data to crime-fighting, but it was still more about looking back than projecting forward.

PredPol and similar software that IBM has developed for police departments in Memphis and just recently, in Charleston, South Carolina, is far more precise and timely, with the data recalibrated daily. And while it might take a human analyst hours or even days to spot a pattern, the computer can connect the dots in seconds.

At the very least, say boosters of predictive policing, the software allows police to spend more time on the street instead of sitting in strategy sessions. Computers can handle more of the planning–which make this even more appealing to all the police departments losing officers to budget cuts.

Bad search results

But, as is often the case when computers call the shots, algorithmic crime-fighting makes some people nervous. Critics say it could easily lead to racial profiling or reinforcing stereotypes about certain neighborhoods, that once a computer identifies an area as a hot spot, it lowers the bar for what qualifies as suspicious behavior.

It’s only a matter of time, argues Andrew Ferguson, a Washington D.C. law professor, before a search based on predictive policing gets challenged in court. Here’s his take, from a recent interview with the Charleston (S.C.) City Paper:

“I think what you would say is the worst case — and I don’t even think this is that far-fetched — is that there will be a case where someone gets stopped on a street corner for suspicion of burglary. It’ll go before a court, and they’ll say, ‘OK, officer, what was your reasonable suspicion for stopping this person?’

“And he’ll say, ‘The computer told me,’ essentially, right? ‘The computer said look out for burglaries, I saw this guy in the location, so I stopped him because he looked like a burglar.’ And race, class, all of those things obviously are a part of it. And the judge will then just defer.

“How are you going to cross-examine the computer?”

21st century crime busting

Here are more examples of how technology is changing law enforcement:

• The eyes have it: As part of a project to expand on its old fingerprint database, the FBI is adding server space to store iris scans. More jails now are using high-res cameras to create images of prisoners’ irises when they’re booked.
• Smartphone justice: Britain’s Scotland Yard has created a smartphone app called Facewatch that encourages Londoners to help find criminals. Users enter their postal code and they’re shown pictures of suspects who may be in their areas. If they recognize somebody, they can tap on the image and send in that person’s name.
• Face to face: Engineers at Michigan State University have created algorithms that could make it easier to track down criminals by matching sketches made by police artists with images in a database of mug shots. That can make sketches, often based on unreliable traumamtic memories, more effective in solving crimes.
• Let’s go toss some robots: Police and firefighters have started using the Recon Scout Throwbot, an eight-inch long robot that can be thrown like a football, but lands upright and transmits video through its camera.
• The devil made me not do it: Researchers in Oregon say their analysis of more than 25 years of data suggests that crime rates tend to be lower in societies where many people believe in Hell and God’s punitive nature than in those where most people put their faith in a forgiving God.

Video bonus: For old times sake, spend a little time with Peter Falk as Columbo, the ultimate low-tech detective.

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Randy Rieland | | READ MORE

Randy Rieland is a digital media strategist and contributing writer in innovations for Smithsonian.com.