Hurricane Ida has demonstrated once again how chaotic the days after a natural disaster can be. Relief organizations are often on the ground right away, but determining the extent of damage and displacement can take time, delaying the delivery of essential supplies. In the wake of Hurricane Harvey, a team of researchers from Texas A&M University used the experience of their own state to develop a framework to monitor the recovery of communities in nearly real-time.

Traditionally, community resilience and recovery from natural disasters are determined with surveys, but this takes time and leaves gaps in data. To create this new and more efficient assessment tool, the researchers analyzed visitation patterns at essential establishments like pharmacies, religious centers, and grocery stores before and after Harvey. They partnered with a company called SafeGraph to create a resilience map using community-level big data around Harris County.

Unsurprisingly, they found that communities with low resilience also experienced more flooding and that the level of storm impact didn’t necessarily correlate with flooding. This is in line with research that found that flood risk studies fail to properly assess low-income regions and that a majority of flooding-prevention funding goes to higher-income neighborhoods.

Moving forward, other communities can use this tool to assess their resilience before a storm and deliver targeted relief afterward. Jacqueline Meszaros, a program director in NSF’s Directorate for Engineering says, “These research methods avoid some human errors such as memory failure, they have some privacy-preserving advantages, and they don’t require time and effort by the people affected. When we can learn about resilience without imposing on those who are still recovering from a disaster, it’s a good thing.”

Source study: Interface – Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data