Jeffrey Burkhalter, Charles Ehlschlaeger, Dawn Morrison, Natalie Myers, Liqun Lu, Antoine Petit, Yanfeng Ouyang, Olaf David, Francesco Serafino, David Patterson, Zhoutong Jiang
KEYWORDS: Systems modeling, Data modeling, Visualization, Visual process modeling, Complex systems, Monte Carlo methods, Error analysis, Data conversion, Analytical research, Spatial data uncertainty, Emergency preparedness
This research effort is developing a computational framework to support federated models of complex urban systems and enable information support for planning and response in emergency management. Systems analysis has been advocated to support emergency management activities, and there are a number of individual domain models designed to represent various system elements. However, effective implementation of this approach has its challenges. Traditional system analysis is often performed at regional or country scales. Further, information collection tends to be reductionist in process focusing on mission before the operating environment. Thus, there is limited data available to support high resolution urban systems modeling beyond localized areas. However, dense urban environment complexity requires the ability to capture and integrate the interrelationships between subpopulations and infrastructural systems. This system of systems modeling approach supports the analysis of cascading effects through interdependent infrastructure networks and the anticipated impacts on the subpopulations it supports, such as ethnicity, social class, access to transportation, or previously available services. The results are expected to reduce analyst workload by generating geospatial products and systems perspectives of demographic and infrastructure characteristics. We will be presenting an integrated infrastructure system demonstrating the cascading effects of component failure(s) combined with the effects on neighborhood-scale populations. The results are delivered to end-users using a geospatial visualization tool that includes information about the quality of the data products and the ability of the data to support information critical to emergency planning and response.
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