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Tina Comes, & Bartel A. Van De Walle. (2014). Measuring disaster resilience: The impact of hurricane sandy on critical infrastructure systems. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 195–204). University Park, PA: The Pennsylvania State University.
Abstract: Modern critical infrastructure (CI) systems are tightly coupled, resulting in unprecedented complexity and difficulty to predict, limit and control the consequences of disruptions caused by hazards. Therefore, a paradigm shift in disaster risk management is needed: instead of focusing on predicting events, resilience needs to be improved as a basis for adequate response to any event. This paper starts from a definition of CI resilience that provides a basis for quantitative and qualitative decision support. For the quantitative modelling approach, which aims at measuring the resilience of individual CIs, we focus on two CIs of fundamental importance for disaster response: transportation and power supply. The qualitative framework details relations between CIs. The results of this research are illustrated by a case study that analyses the impact of Hurricane Sandy. The findings highlight the need for a framework that combines qualitative and quantitative information from heterogeneous sources to improve disaster resilience.
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Christopher W. Zobel. (2010). Comparative visualization of predicted disaster resilience. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The disaster resilience triangle is a simple but effective tool for illustrating the relationship between the initial impact of a disaster event and the subsequent time to recovery. This tool can also be expanded, however, to provide an analytic measure of the level of resilience exhibited by a particular entity in a given disaster situation. We build upon the previous work in this area by developing a new approach for visualizing and analyzing the tradeoffs between the two primary defining characteristics of the disaster resilience triangle. This new approach supports strategic decision making in a disaster planning environment by providing a straightforward means for directly comparing the relative predicted resilience of different critical facilities within an organization, with respect to both location and type of risk.
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Christopher W. Zobel. (2011). Representing the multi-dimensional nature of disaster resilience. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Although quantitative analytical information systems are an important resource for supporting decision-making in disaster operations management, not all aspects of a disaster situation can be easily quantified. For example, although the concept of the disaster resilience of a community has a technical dimension within which one can measure the resistance of the infrastructure against, and the speed of its recovery from, a disaster event, it also has social, organizational, and economic dimensions within which these characteristics may be more difficult to measure. This work-in-progress paper introduces a quantitative framework within which the multi-dimensional nature of such disaster resilience can be represented in a concise manner. This can help to improve understanding of the complexities associated with the concept, and thus directly support decision-making in disaster operations planning and management.
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Christopher W. Zobel, Stanley E. Griffis, Steven A. Melnyk, & John R. MacDonald. (2012). Characterizing disaster resistance and recoveryusing outlier detection. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Most definitions of disaster resilience incorporate both the capacity to resist the initial impact of a disaster and the ability to recover after it occurs. Being able to characterize and analyze resilient behavior can lead to improved understanding not only of the capabilities of a given system, but also of the effectiveness of different strategies for improving its resiliency. This paper presents an approach for quantifying the transient behavior resulting from a disaster event in a way that allows researchers to not only describe the transient response but also assess the impact of various factors (both main and interaction effects) on this response. This new approach combines simulation modeling, time series analysis, and statistical outlier detection to differentiate between disaster resistance and disaster recovery. Following the introduction of the approach, the paper provides a preliminary look at its relationship to the existing concept of predicted disaster resilience. © 2012 ISCRAM.
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