There’s nothing new about this program other than that it’s computerized and the information is obtained quicker. Back in my day, whenever there was a rash of burglaries in a certain area or neighborhood, my department would order all of us detectives to put on our uniforms and head out to patrol those areas during the nighttime. It was hilarious to see what some of us looked like in uniforms we could barely, and I do mean barely, get on.
We did succeed in reducing the number of burglaries, but all we did was to drive the thieves to other areas under our jurisdiction or to areas in neighboring jurisdictions (Riverside County to San Bernardino County). I suspect the same thing will happen with this computerized program.
COPS ON THE TRAIL OF CRIMES THAT HAVEN’T HAPPENED
by Melissae Fellet
NewScientist
October 12, 2011
THE patrol car comes to a stop in a sleepy neighbourhood of small, earth-coloured homes. A woman saunters past pushing a stroller. It is daytime in the artsy beach town of Santa Cruz but I am still a little spooked. Futuristic crime prediction software sent me here with my companion, deputy chief of police Steve Clark. And it's just possible that the tranquillity is due to our presence.
Rather than predicting who will commit crimes, like the fictitious "precrime" system from the 2002 film Minority Report, the software that the Santa Cruz police department has recently started field-testing looks at where crime might be committed.
It uses the locations of past incidents to flag up likely future crime scenes. Police can then target their patrols on these areas, in the hope that their presence might stop the predicted crimes from happening at all. At the very least, they will be on the spot to help victims and make arrests.
The program has been built by mathematician George Mohler, at Santa Clara University in California, and his colleagues. They noted that some crimes follow potentially predictable patterns. One burglary, for example, tends to trigger others nearby in the next few days, rather like aftershocks from an earthquake (see graph). In 2010, Mohler's team turned equations used to predict aftershocks into the basis for a program that uses the dates and times of reported crimes to predict when and where the "aftercrimes" will occur.
On average the program predicted the location and time of 25 per cent of actual burglaries that occurred on any particular day in an area of Los Angeles in 2004 and 2005, using just the data on burglaries that had occurred before that day (Journal of the American Statistical Association, DOI: 10.1198/jasa.2011.ap09546).
Now the program is undergoing its first field test in Santa Cruz. Every day. it flags up 10 areas, each 150 metres square, for each of three types of crime - residential burglary, auto burglary and auto theft. Clark updates the program each night with new data and calculates the location probabilities for the next day.
A small town like Santa Cruz may seem like a strange place to test a crime-prediction program, when larger cities like Chicago, Baltimore or Los Angeles may have much more going on. But Clark argues that, while certainly not a hotbed of violent crime, Santa Cruz is a challenging place to patrol, mainly because of an ever-changing population of tourists and students. "It's a very difficult demographic to game plan," Clark says, and that means it is a good place to test whether the algorithm works.
It's too early to tell if it does. But Mohler and his colleagues will conduct a controlled experiment with the Los Angeles police department later this year. Officers will run the prediction algorithms as they do in Santa Cruz, but patrol only half of the locations it flags. They will then compare crime levels in the two groups.
So far, Clark likes the program because it shakes police out of their usual habits. Officers tend to fall into patterns in terms of where they patrol, he says, that are based on intuition. "Something like this either confirms that or breaks us out of it."
Should the program prove effective, it can quickly be applied in other cities, says Jeffrey Brantingham, an anthropologist at the University of California, Los Angeles, and a collaborator on the algorithm.
Chris Calabrese of the American Civil Liberties Union says using algorithms isn't a problem per se, but cautions that some of the information fed into them could be prejudicial. For example, a large number of arrests in a particular neighbourhood could raise the chances of that area being flagged by the program even if crime levels are no higher than elsewhere. Clark acknowledges this: "You can screw this up by not getting a piece of data right."
As our patrol comes to an end, the car's computer display shows an officer taking a stolen car report - another data point that could drive tomorrow's patrol locations, and perhaps those further into the future.
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