And yet, more than a year after Chicago published its code, only one local government, in metro D.C., has tried to do the same thing.​ All cities face the challenge of keeping their food safe and therefore have much to gain from this data program. The challenge, then, isn’t just to design data solutions that work, but to do so in a way that facilitates sharing them with other cities. The Chicago example reveals the obstacles that might prevent a good urban solution from spreading to other cities, but also how to overcome them.

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After an initial test that failed, the Chicago innovation team retooled which variables they used to predict health violations—nine of them, including previous violations, nearby sanitary complaints, and length of time since last inspection—and how they weighed them. In September and October 2014, they generated a list of priority inspections and compared the projected violations with what inspectors really found. The results were clear: The algorithm found violations 7.5 days earlier, on average, than the inspectors operating as usual did.

“That trial gave us enough confidence that we were able to roll it out to drive day-to-day decisions,” Chicago’s chief data officer Tom Schenk tells CityLab.

Chicago started using the prediction tool for daily operations in February 2015, and the transition worked very smoothly, says Raed Mansour, the innovation-projects lead for the Department of Public Health. That’s because the department was careful to incorporate the algorithm in a way that minimally altered the existing business practices. Inspectors still get their assignments from a manager, for instance, but now the manager is generating schedules from the algorithm. The department will conduct an evaluation of the program after a year, and Mansour anticipates that the performance will meet or exceed the metrics from the test run.

But that was never meant to be the end of it. Back in November 2014, Schenk published the code for the algorithm on the programming website GitHub, so anyone in any other city could see exactly what Chicago did and adapt the program to their own community’s needs. That’s about as far as they could go to promote it, short of knocking on the door of every city hall in America. But the months since then have shown that it takes more than code to launch a municipal data program.

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Just because an idea is good doesn’t mean it will spread. The New Yorker’s Atul Gawande dissected this difficulty with the example of solutions to the two scourges of surgery: pain and infection. After the first public demonstration of anesthesia in 1846, the technology proliferated throughout the world in a matter of months, making surgery significantly less frantic. But antiseptic methods, like washing hands and sterilizing the operating room, took decades to gain wide acceptance. The evidence was out there that it saved lives, but evidence alone doesn’t alter people’s behavior.