The New York City Fire Department uses data mining to predict which of the city's 1 million buildings are most at risk of a major fire. By analyzing 60 factors such as a building's age, electrical issues, sprinkler system, and vacancy status, the department calculates a risk score for each of the city's 330,000 inspectable buildings. Fire officers are directed to inspect higher-risk buildings first based on these scores. The data-driven approach aims to reduce the number and severity of fires compared to the previous random inspection method. Other cities like Boston are also using big data to identify problem properties for targeted police visits.

1. 12/11/2014 How New York’s Fire Department Uses Data Mining ­Digits
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January 24, 2014, 2:12 PM ET
How New York’s Fire Department Uses Data
Mining
ByElizabeth Dwoskin
A map of New York City that displays the number of
serious fire incidents within commercial and high­rise
buildings. FDNY uniformed fire personnel inspect
commercial and high­rise
residential buildings during
their regularly scheduled building inspection time.
FDNY Analytics Unit
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.
“Low­income
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.
So New York officials have taken roughly 60 different factors and built an algorithm that assigns each one
of the city’s 330,000 inspectable buildings with a risk score (The Fire Department doesn’t inspect single
and two­family
homes).
When fire officers go on weekly inspections, the computer spits out a sheet with a list of buildings, ranked
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2. 12/11/2014 How New York’s Fire Department Uses Data Mining ­Digits
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by their risk score, that they should visit first.
The data­mining
program went into effect in July and will be expanded to 2400 categories in the coming
months. Inspections before it was adopted were fairly random, Chen says.
Buildings considered to be safety priorities, like schools and libraries, were supposed to be inspected more
frequently. But the city didn’t target specific buildings based on their risk.
What’s happening in New York City–which became more data­driven
under former Mayor Michael
Bloomberg–is an example of how many municipalities are trying to to use the data they routinely collect to
improve services.
Boston uses big data in its Problem Properties program, which exploits information from different city
sources–such as complaint calls, safety records, crimes and tax collections–to identify which properties
should get visits by police.
While making investments in big data systems seems like common sense, cities have trouble measuring
their success. Officials may be able to cite statistics showing the number of fires or crimes dropping, but
demonstrating that big data tools were the reason may be difficult because it involves proving a negative–
that something didn’t happen because of their efforts.
“Ultimately, we should see the number of fires go down,” says Jeff Roth, the Fire Department’s Assistant
Commissioner for Management Initiatives. “And fires should become less severe.”
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