THE IMPORTANCE OF GEOSPATIAL REFERENCING FOR URBAN SUPPLY CHAIN INFRASTRUCTURE DATA TO LARGE-SCALE DISASTER RESTORATION MODELING

Supply chain interdependent critical infrastructure (SCICI) modeling after a large scale disaster must account for complex, multi-scale, and multi-dimensional characteristics of infrastructures and the interdependence between these infrastructures. To understand these complexities and interdependencies, a rich set of infrastructure and supply chain data are required. The bulk of these data must be geospatially located. Large-scale data layers, including The National Map of the U.S. Geological Survey, serve as the base structure for geo-referencing SCICI data. This work focuses on the acquisition and integration of urban infrastructure data for the metropolitan St. Louis, Missouri area. Locating these data geospatially permits the development of a robust methodology for modeling complexity in the restoration of SCICI in the wake of a disaster.