They started with a list of every property lot in the city—all 900,000 of them. Next they poured in datasets from 19 different agencies indicating, for example, if the building owner was delinquent in paying property taxes, if there had been foreclosure proceedings, and if anomalies in utilities usage or missed payments had led to any service cuts. They also fed in information about the type of building and when it was built, plus ambulance visits, crime rates, rodent complaints, and more. Then they compared all this information against five years of fire data ranked by severity and looked for correlations in order to generate a system that could predict which complaints should be investigated most urgently.
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