| While strategies for emergency response to large-scale disasters have been extensively studied, little has been done to map medium- to long-term strategies capable of restoring supply chain infrastructure systems and reconnecting such systems from a local urban area to national supply chain systems. This is, in part, because no comprehensive, data-driven model of supply chain networks exists. Without such models communities cannot re-establish the level of connectivity required for timely restoration of goods and services. This dissertation builds a model of supply chain interdependent critical infrastructure (SCICI) as a complex adaptive systems problem. It defines model elements, data needs/element, the interdependency of critical infrastructures, and suggests metrics for evaluating success. Previous studies do not consider the problem from a systematic view and therefore their solutions are piecemeal, rather than integrated with respect to both the model elements and geospatial data components. This dissertation details a methodology to understand the complexities of SCICI within a real urban framework (St. Louis, MO). Interdependencies between the infrastructures are mapped to evaluate resiliency and a framework for quantifying interdependence is proposed. In addition, this work details the identification, extraction and integration of the data necessary to model infrastructure systems. |
