NYC fire department uses data mining to find potential fire threats

FDNY officials are using a 60-facet algorithm to determine which department-inspected buildings pose the greatest fire threat, fast-tracking fire inspectors to the riskiest buildings. Structures that are old, vacant, or located in poor neighborhoods are generally at a higher risk, and thanks to the data-driven program, those structures will receive attention first. 

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.

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