Alongside big-city problems like lowering the murder rate, cutting the number of stolen garbage carts may seem like small stakes. But lost garbage carts actually cost Chicago a lot of money and time -- it takes scarce resources to field the complaints, acquire new carts and pay staff to deliver them. What if data analysis could help the city minimize the number of lost carts?
Evaluating garbage cart losses with mapping software and comparing that information to streetlight failures, city staff confirmed what they had suspected: In certain neighborhoods, if the alley lights go down, garbage cart thefts spike. That intelligence gives a new sense of urgency to getting lights repaired.
"Government has been very good at collecting data, but not as good at using the data," says Brett Goldstein, the city of Chicago's CIO. So Chicago is in the process of building a predictive analytics platform that will do more analysis and much more sophisticated analysis. That work is being funded in part through a $1 million grant the city received in March as a runner-up in Bloomberg Philanthropies' Mayors Challenge, a competition to fund innovative ideas in city government.
The city still has far to go in completing the predictive platform. Goldstein has spent the past two years laying a foundation for this analytics work, including hiring experts from the private sector and academia with experience in big data and open source. His team has also created a single database on the MongoDB open source platform, into which data is fed from dozens of legacy IT systems, providing better visibility into municipal operations across departments.