Big Data-Derived Tool Facilitates Closer Monitoring Of Recovery From Natural Disasters
By analyzing peoples’ visitation patterns to essential establishments like pharmacies, religious centers and grocery stores during Hurricane Harvey, researchers at Texas A&M University have developed a framework to assess the recovery of communities after natural disasters in near real time.
They say the information gleaned from their analysis would help federal agencies allocate resources equitably among communities ailing from a disaster.
“Neighboring communities can be impacted very differently after a natural catastrophic event,” said Ali Mostafavi, associate professor in the Zachry Department of Civil and Environmental Engineering and director of the Urban Resilience.AI Lab. “And so, we need to identify which areas can recover faster than others and which areas are impacted more than others so that we can allocate more resources to areas that need them more.”
The researchers have reported their findings in Interface, a publication of The Royal Society.
The metric that is conventionally used to quantify how communities bounce back from nature-caused setbacks is called resilience, and is defined as the ability of a community to return to its pre-disaster state. To measure resilience, factors like the accessibility and distribution of resources, connection between residents within a community and the level of community preparedness for an unforeseen disaster are critical.