In the wake of a large-scale disaster, strategies for emergency search and rescue, short-term recovery and medium- to long-term restoration are needed. While considerable effort is geared to developing strategies for the former two options, little comprehensive guidance exists on the latter. However, medium- to long-term restoration has a significant effect on local, regional and national economies and is essential to community vitality. In part, the deficit of robust strategies can be linked to the complexity in the data acquisition and limited methodologies to understand the interconnectedness of the relevant systems elements. This research utilizes infrastructure data for Supply Chain Interdependent Critical Infrastructure Systems (SCICI) such as transportation, energy, communications, or water, obtained or derived through open sources (such as The National Map of the U.S. Geological Survey) to identify, understand, and map the interdependencies between these system elements to enable restoration planning. Specifically, internal geographical relationships (herein called the ‘geographical interdependency’) of SCICI elements are mapped. These interdependencies highlight the stress points on the larger SCICI where failures occur and are not included in current built environment models. The mapping of these interdependencies is a key step forward in attempts to optimally restore an urban center’s supply chain in the wake of an extreme event.
Ramachandran, Varun; Shoberg, Tom; Long, Suzanna; Corns, Steven; and Carlo, Hector
"Identifying Geographical Interdependency in Critical Infrastructure Systems Using Open Source Geospatial Data in Order to Model Restoration Strategies in the Aftermath of a Large-Scale Disaster,"
International Journal of Geospatial and Environmental Research:
1, Article 4.
Available at: http://dc.uwm.edu/ijger/vol2/iss1/4