On Monday of this week, O'Reilly's Alex Howard published an interview with Michael Flowers, New York City's director of analytics for the Office of Policy and Strategic Planning in Mayor Bloomberg's office. In the interview, "Predictive data analytics is saving lives and taxpayer dollars in New York City," Mike discusses the ways that he and his team have used predictive data analytics toward "preemptive government" and some of the positive results they've achieved, including improved process efficiency and effective detection of crimes, such as fraud and bootlegging. An excerpt:
When you combine human capital with technological tools, what's the outcome downstream? What are you able to do and what results have you achieved?
Flowers: We never wanted to be a solution in search of a problem. By way of example, the city receives roughly 20,000 to 25,000 complaints for something called an 'illegal conversion' every year. An illegal conversion is a situation where you have an apartment or a house that’s zoned for six people to live in safely and a landlord’s chopping them up and putting 60 people in there. They represent significant public safety hazards—and not just from fire, but from crime and from epidemiological issues. To throw at those 20,000 to 25,000 complaints, we have roughly 200 inspectors to the Department of Buildings.
What we’ve done is come up with a way to prioritize those [complaints] which represent the greatest catastrophic risk, as a structural fire. In doing that, we built a basic flat file of all 900,000 structures in the city of New York and populated them with data from about 19 agencies, ranging from whether or not an owner was in arrears on property taxes, if a property was in foreclosure, the age of the structure, et cetera. Then, we cross-tabulated that with about five years of historical fire data of all of the properties that had structural fires in the city, ranging in severity.
After we had some findings and saw certain things pop as being highly correlative to a fire, we went back to the inspectors at the individual agencies, the Department of Buildings, and the fire department, and just asked their people on the ground, “Are these the kinds of conditions that you see when you go in post-hoc, after this catastrophic event? Is this the kind of place that has a high number of rat complaints? Is the property in serious disrepair before you go in?”
And the answer was yes. That told us we were going down the right road.
What we’ve done now is run every new complaint that comes in against that flat file. We find those [complaints] which represent the top five percent for historic fire risk and then send that top five percent back out to the inspectors to follow up on with urgency.
The rest of the interview is definitely worth reading. The city's use of data is a great example of the data-driven innovation in the public sector.