Texas A&M Develops
Real-Time Disaster Recovery
Monitoring Big Data Tool
August 20, 2021
analyzing 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 nearly
The scientists say the information gleaned from their analysis could
help agencies allocate resources effectively among communities ailing
from a disaster.
"Neighboring communities can be impacted very differently after a
natural catastrophic event," said Ali Mostafavi, a civil engineer at
Texas A&M University. "We need to identify which areas can recover
faster than others, and which areas are impacted more than others so we
can allocate resources to areas that need them more."
The U.S. National Science Foundation-funded researchers reported their
findings in Interface.
The standard way of obtaining data needed to estimate resilience is
through surveys. The questions considered, among others, are how and to
what extent businesses or households are affected by the natural
disaster and the stage of recovery. However, Mostafavi said these
survey-based methods, although extremely useful, take a long time to
conduct, with the results becoming available only many months after the
and collaborators turned to community-level big data, especially
information collected from anonymized cell phone data by companies that
keep track of visits to locations within a perimeter. In particular, the
researchers partnered with a company called SafeGraph to obtain location
data for people in Harris County around the time of Hurricane Harvey.
Their analysis revealed that communities that had low resilience also
experienced more flooding. However, the results also showed that the
level of impact did not necessarily correlate with recovery.
Jacqueline Meszaros, a program director in NSF's Directorate for
Engineering, added that, "in addition to being faster than surveys,
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."