Tayler Ruggero, & Brian Tomaszewski. (2018). Geographic Information Capacity (GIC) Across International Scales: Comparing Institutional Structures of Germany to the United States. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1153–1155). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Over the last three decades, the number and severity of natural disasters all across the world has been increasing exponentially (Basher, 2006). This paper intends to consider geographic information capacity (GIC) as it relates to government, government regulated organizations, and international organizations, including the United Nations, and their involvement in disaster risk reduction and management. Specifically, the paper aims to understand similarities and differences and the connection between two governmental disaster management organizations, FEMA in the United States and BBK and THW in Germany. We present a comparative analysis on the two countries in terms of their organizational structures, how their structures affect geographic information capacity and how geographic information capacity is related to disaster risk reduction and disaster response. Future work can make comparisons across more countries, including developing countries, to see what structural changes can be made in government entities to help increase GIC when disaster strikes.
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Felix Wex, Guido Schryen, & Dirk Neumann. (2011). Intelligent decision support for centralized coordination during Emergency Response. 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: Automated coordination is regarded as a novel approaches in Emergency Response Systems (ERS), and especially resource allocation has been understudied in former research. The contribution of this paper is the introduction of two variants of a novel resource allocation mechanism that provide decision support to the centralized Emergency Operations Center (EOC). Two quantitative models are computationally validated using real-time, data-driven, Monte-Carlo simulations promoting reliable propositions of distributed resource allocations and schedules. Various requirements are derived through a literature analysis. Comparative analyses attest that the Monte-Carlo approach outperforms a well-defined benchmark.
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