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GCN : May 2015
case study 28 GCN MAY 2015 • GCN.COM VIRGINIATECH BY STEPHANIE KANOWITZ GEOSPATIAL Virginia Tech graduate students built a tool that integrates local open data to aid in emergency response Ebola modeling flows into flood app When natural disasters hit, responders depend on quickly having accurate geospatial data to help them coordi- nate rescue efforts, and they often turn to the Federal Emergency Management Agency or the American Red Cross. But the next time a flood hits Hamp- ton Roads, Va., the region will be better prepared to handle it, thanks to a map- ping model from an unlikely source: Virginia Tech’s Virginia Bioinformatics Institute (VBI). The model was created to help health workers in Ebola-stricken parts of Liberia figure out where to best es- tablish central resource centers. It used information on demographics, family structures, travel patterns and activities to help model what would potentially happen as the disease spread. With those models, VBI helped the Defense Department quickly find the best loca- tions for emergency treatment centers by looking at the road infrastructure in West Africa and identifying hot spots where additional outbreaks were con- sidered likely. The eureka moment came when two graduate students in the university’s genetics, bioinformatics and computa- tional biology program realized they could adapt the Ebola model for use in local emergency response to flooding. After about 30 volunteer hours during an open-data hackathon, they had their solution. The team modified the Ebola model by adding a 100-year storm surge — or a 9-foot rise in sea levels — to see how roads would be affected, said Pyrros Telionis, who is pursuing a doctorate in computational epidemiology and a post-graduate certificate in geospatial analysis. “You’re trying to decrease the dis- tance from each member of the popu- lation to the nearest resource [so] that the population as a whole has reduced travel time,” said James Schlitt, also a Ph.D . candidate in the program. For the model, the team pulled open data from various resources. Open- StreetMap provided the road network, population density was calculated us- ing Census Bureau information, and elevation data came from the U.S. Geo- logical Survey. Meanwhile, Schlitt built a scraping tool to find the locations of schools and parking lots of big-box stores, which Graduate students came up with a flood-response app by adapting a tool used to predict the spread of Ebola in Liberia. They incorporated a variety of data to identify locations for post-flood resource centers in Hampton Roads, Va. 0515gcn_028-029.indd 28 4/30/15 10:22 AM