Julian Spector at CityLab: “The three dozen inspectors at the Chicago Department of Public Health scrutinize 16,000 eating establishments to protect diners from gut-bombing food sickness. Some of those pose more of a health risk than others; approximately 15 percent of inspections catch a critical violation.
For years, Chicago, like most every city in the U.S., scheduled these inspections by going down the complete list of food vendors and making sure they all had a visit in the mandated timeframe. That process ensured that everyone got inspected, but not that the most likely health code violators got inspected first. And speed matters in this case. Every day that unsanitary vendors serve food is a new chance for diners to get violently ill, paying in time, pain, and medical expenses.
That’s why, in 2014, Chicago’s Department of Innovation and Technology started sifting through publicly available city data and built an algorithm to predict which restaurants were most likely to be in violation of health codes, based on the characteristics of previously recorded violations. The program generated a ranked list of which establishments the inspectors should look at first. The project is notable not just because it worked—the algorithm identified violations significantly earlier than business as usual did—but because the team made it as easy as possible for other cities to replicate the approach.
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….(More)”