Helmets? Check. Oxygen tanks? Check. Hoses? Ditto. Algorithms? Yep: the New York City Fire Department is using data mining to predict which of the city's buildings are at highest risk of catching fire. Now that's metadata we can get behind.
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 poorer neighbourhoods are generally at a higher risk, and thanks to the data-driven programme, those structures will receive attention first. Prior to this, buildings were inspected essentially at random, with schools and libraries receiving extra attention.
Other cities have had success with similar data-driven strategies: in Boston, for example, public information including complaint calls, safety records, and tax collections are analysed to determine where police patrols should focus.
While the FDNY data programme hasn't been in place long enough to measure results, Assistant Commissioner for Management Initiatives Jeff Roth tells the Wall Street Journal, "ultimately, we should see the number of fires go down [. . . ] and fires should become less severe." [WSJ via The Verge]