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. 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.
Supply Chain Resiliency: Maritime Transportation
This research develops a tool for Decision Making in Complex Environments that quantifies and ranks select environmental impact indicators within a Maritime Transportation System. The model will provide policy-makers in the shipping industry with an analytical tool that can evaluate tradeoffs within the system and identify possible alternatives to mitigate detrimental effects on the environment.
Virtual Communities
Community for Data Integration (CDI), U.S. Geological Survey Earthcube, National Science Foundation
Homeland Infrastructure Foundation-Level Working Group (HIFLD)
