Monday, March 11, 2013
Slate looks at how Mike Flowers used "big data" to revolutionize the way New York conducts building inspections:
Among the first challenges the team tackled was “illegal conversions”—the practice of cutting up a dwelling into many smaller units so that it can house as many as 10 times the number of people it was designed for. They are major fire hazards, as well as cauldrons of crime, drugs, disease, and pest infestation. A tangle of extension cords may snake across the walls; hot plates sit perilously on top of bedspreads. People packed this tightly regularly die in blazes. In 2005 two firefighters died trying to rescue residents. New York City gets roughly 25,000 illegal-conversion complaints a year, but it has only 200 inspectors to handle them. There seemed to be no good way to distinguish cases that were simply nuisances from ones that were poised to burst into flames. To Flowers and his kids, though, this looked like a problem that could be solved with lots of data.
They started with a list of every property lot in the city—all 900,000 of them. Next they poured in datasets from 19 different agencies indicating, for example, if the building owner was delinquent in paying property taxes, if there had been foreclosure proceedings, and if anomalies in utilities usage or missed payments had led to any service cuts. They also fed in information about the type of building and when it was built, plus ambulance visits, crime rates, rodent complaints, and more. Then they compared all this information against five years of fire data ranked by severity and looked for correlations in order to generate a system that could predict which complaints should be investigated most urgently.