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Crime Prediction Technology Coming To A City Near You

News Image By Tom Olago March 01, 2016
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What better way could there be to deal with crime than to stop it from happening in the first place? It sounds like an impossible, delusional fantasy when it comes to our crime-ridden planet. It turns out however that this prospect is not so far removed from reality, after all. Systems are now being designed that can, to a large extent, predict crime and criminal attacks before they actually take place.


A recent CBS New York report quoted George Spadoro, former mayor of Edison, New Jersey in which he said, "This is the next level... When you have tight budgets, you need to be able to provide an increased level of protection for your citizens with less manpower."

An Hitachi system co-created by Mark Jules is at the heart of the predictive crime technology under review. The new visualization system gathers massive amounts of information from a wide range of unconnected sources. These sources include social media, transit maps, weather reports, crime statistics, camera feeds and more. It then interfaces all of those sources on one pane of glass.

Authorities can then activate the crime prediction software to look for patterns. Patterns that can not only help identify criminals' intent but also when and where they'll likely strike again within a two-block radius.

"If you go back and look at hey, its a Saturday, its a certain time of day, its a certain temperature, this is where thats happened... then you combine that with social media that can all start to predict when and where its going to happen," Jules told CBS2.

The software is currently being used in Washington, D.C. and is expected to roll out in six more cities soon, New York City not being one of them. The NYPD would not specifically comment on Hitachi, but did tell CBS2 it began field-testing a similar application just last month.

Maurice Chammah co-authored a report on these trends for theverge.com, based largely on precincts of Ferguson, St. Louis County, Missouri and against the backdrop of the racial flare-ups that occurred there last year.

According to Chammah, a new, similar software program called HunchLab, which crunches vast amounts of data to help predict where crime will happen next is also under review for implementation. HunchLab, produced by Philadelphia-based startup Azavea, represents the newest iteration of predictive policing, a method of analyzing crime data and identifying patterns that may repeat into the future. Studies of HunchLabs effectiveness are also underway in several cities.

HunchLab primarily surveys past crimes, but also digs into dozens of other factors like population density, census data, the locations of bars, churches, schools, and transportation hubs, schedules for home games  even moon phases. 

Some of the correlations it uncovers are obvious, like less crime on cold days. Others are more mysterious: rates of aggravated assault in Chicago have decreased on windier days while cars in Philadelphia were stolen more often when parked near schools. 

Such are the trends that are being used to predict the potential of future crimes being repeated based on historical crimes and the settings and circumstances in which they occurred. The data is then incorporated into predictive policing software. It has even been reported that the White House has asked Silicon Valley companies if they can develop algorithms to predict which people are likely to become "radicalized."

Since 2009, the National Institute of Justice had been funding research into crime prediction, transforming the field into competitive big business. IBM, Hitachi, and Lexis have all offered ways to predict crime through data.

Chammah reports however that the leader in the field is PredPol, a company that grew out of a team of researchers and officers. PredPols algorithms digest years of data on crime locations, times and types, spitting out the spots most likely to be hit by crime again. 

After using PredPol for four months, police in the Foothill Division in the San Fernando Valley claimed that property crime dropped 13 percent, while in the rest of the city, it rose by 0.4 percent. PredPol has received millions in venture capital funding and is now used by more than 50 police agencies in the US and UK.

But St. Louis County Police Chief Jon Belmars aide, Sgt. Colby Dolly was more attracted by Azaveas ability to analyze the impact of businesses, churches, and weather patterns on criminal activity. It was also cheaper: Azavea quoted around $50,000 for a year of HunchLab, where PredPol was asking for roughly $200,000.

Dolly was not opposed to examining and addressing the causes of crime, but the department was still focused on patrolling. He hoped using HunchLab might improve relations with the community by reducing the frequency with which police had to aggressively sweep an area in the wake of a crime.

"You can only go so far in enforcing or arresting your way out of crime issues," he said. "This is a way to combat crime that should have minimal impact on the community."


Dolly recently sat down at his computer at police headquarters. He logged into the HunchLab website and pulled up a map. The sprawling metropolis was covered in little bright dots. He clicked to zoom in, and the dots grew into transparent boxes, each covering a space roughly half the size of a city block, and each tinted green, orange, red, purple, blue, pink, or yellow. 

The colors indicated which type of crime was most likely to hit that box: green for larceny, orange for gun crimes, red for aggravated assault.

Such is a typical approach to how officers use intelligence from Hunchlab and other similar software to supplement patrolling and other crime-predictive indicators borne out of experience and training. 

According to Sean Captain of fastcompany.com, Predictive Crime Analytics provides a highly visual interface, with color-coded maps indicating the intensity of various crime indicators and even surprisingly clever icons for things like guns, cellphones, and surveillance cams. The system can pinpoint a location, down to a 200-meter square, and assign it a relative threat level from 0 to 100 percent. Jules calls this visual approach putting everything on a single pane of glass. 

Hitachi says that "about half a dozen" U.S. cities will join a proof of concept test of the technology beginning in October, and though Hitachi hasn't yet named them, Washington, D.C. could well be on the list. 

It's one of several dozen cities in the U.S. and Caribbean countries where the company already provides video surveillance and sensor systems to police departments with its Hitachi Visualization Suite. 

Concerns remain, however. What if Hitachi's Visualization Predictive Crime Analytics makes mistakes and guesses wrong? Could this lead to a new kind of biased profiling of innocents as potential criminals?

Lipscomb claims it would be the opposite, at least better than New York City's controversial stop-and-frisk practice, in which police can search anyone in a targeted neighborhood. "We're trying to provide tools for public safety so that [law enforcement is] armed with more information on who's more likely to commit a crime," says Lipscomb. 

"I don't have to implement stop-and-frisk. I can use data and intelligence and software to really augment what police are doing." 

That still leaves open other questions on accuracy: Will the technology really target the right places, where crime is likely to occur? Lipscomb acknowledges that he still has to prove the system will work. In the upcoming tech trials, some cities will be taking action based on the predictions, reallocating police to areas when the model predicts a higher likelihood of crime.

There will also be double-blind trials. Police departments will continue with business as usual, but the models will also be running in the background. Only after the test period will the police see what the model had predicted each day, so they can compare the predictions to what actually happened in the time frame. Hitachi has pledged to make all these results publicly available for scrutiny.

"We know that our approach is probably a little more innovative than some of the others, but we're not saying it's more accurate," Lipscomb says. "We want to prove it out with existing customers and then really go broad-based and say: Look, this works."

A reminder that although research on the impact of predictive policing programs is still in its infancy, indications point to encouraging results. Last year, PredPol researchers published a study finding that sending patrol officers to several areas of Los Angeles predicted by their algorithm led to a reduction, on average, of more than four crimes per week in these neighborhoods  twice as efficient as human crime analysts. 

The researchers said the savings  resulting from not having to investigate and prosecute crimes that otherwise may have happened  could reach $9 million per year.


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