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New analytics develop model to identify at-risk populations before disasters strike

In August 2017, Hurricane Harvey made landfall in Houston and poured water on the city for days. It became the worst documented rainstorm in the history of the United States.

In the months after the storm, it became clear to the Houston Department of Housing and Community Development (HCD) that the money coming in from FEMA for homeowners impacted by flooding would not be enough; it rarely is. FEMA bases how much aid it administers to a disaster-struck region on how many people apply for it, and then again, which of those applications meet the agency’s threshold for sustained damage.

This approach results in a severe underestimation of need: It fails to capture people who do not know to apply, or cannot, or whose properties sustained damage that FEMA doesn’t recognize. So Houston HCD, in November, issued a request for proposals, calling for a new, data-driven approach to identifying and quantifying need after a disaster like Harvey strikes.

“Houston’s been hit by five federally declared disasters in three years,” Tom McCasland, HCD director, tells Fast Company. “If the damage from these disasters is chronically undercounted, then we’re being chronically under-resourced for recovery. Harvey presented an opportunity to take on the problem of undercounting with this data project.”

HCD ultimately awarded the contract to the data consultancy firm Civis Analytics, which proposed a method of calculating aid based on both existing data streams that the city collects, like trash pickup locations and flood level modeling, and comprehensive community surveys and outreach. “We focused on helping them understand, at a really individual level, exactly which households were impacted by flooding,” says Amy Deora, Civis’s director of public sector analytics.

Read the full story at Fast Company.
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