Analytics Fights Fires in New York City
When we talk about predictive analytics it’s usually in the context of profitability – where it works wonderfully with products like, ahem…CAN’s very own Pulse. (Sorry, couldn’t help myself.)
But as analytics (or “big data” if you will) becomes more mainstream we find it being used to solve problems for everything from crime fighting, to city planning and even recruiting in Major League Baseball.
In New York City, the local government is learning to do more with less due to steep cutbacks in budgets and manpower in the face of increased demand for services. How does a city with millions of citizens cope? The answer: math. Mike Flowers – who heads up the analytics department in the Mayor’s office – leverages his team’s predictive analytics skills to solve problems that use to take hundreds of city workers and thousands of man hours.
His team’s most important task to date: predicting what buildings are most likely to have major fires. If they could get this right, not only would the city save money, but more importantly, lives would be saved.
It worked, in fact, it worked better than they anticipated.
Mr. Flowers explains how it’s done:
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.
The results:
Using our system, they’re finding these risky conditions at a sustained level of about 70 to 80 percent of the time in the complaints that we send them out to [investigate]. From a Department of Buildings standpoint, they’re very happy, because that’s a fivefold return on inspection man hours. From the fire department standpoint, it also turns out that these buildings we send them to are 15 to 17 times more likely to result in a fireman being injured or killed in the response to the fire, so they love it. It’s been going on for a year. We do it on a weekly basis and it’s worked out spectacularly well.
Above is an interview with Director of Analytics Mike Flowers.
