Data Mining

How New York’s Fire Department Uses Data Mining

27th Jan `14, 06:00 AM in Data Mining

New York City has about a million buildings, and each year 3,000 of them erupt in a major…

BDMS
Guest Contributor
 

New York City has about a million buildings, and each year 3,000 of them erupt in a major fire. Can officials predict which ones will go up in flames?

The New York City Fire Department thinks it can use data mining to do that. Analysts at the department say that some buildings are linked to characteristics that make them more likely to have a fire than others.

Poverty, for one.

“Low-income neighborhoods are correlated with fires,” said Jeff Chen, the department’s Director of Analytics, at an industry conference in Las Vegas.

Other factors that correlate with deadly fires: the age of the building, whether it has electrical issues, the number and location of sprinklers and the presence of elevators. Buildings that are vacant or unguarded are twice as likely to have a fire, Chen says.

All this may sound obvious. But it is hard to absorb all the relevant factors at once.

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