Jennifer L Chan, Robert Colombo, & Altaf Musani. (2012). Mapping libyan health facilities – A collaboration between crisis mappers and the world health organization. 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: This practitioner report describes a recent example of the growing opportunities between humanitarian health organizations and the crisis mapping community. The World Health Organization (WHO) partnered with volunteer crisis mappers to quickly collect information and map over 600 health facilities after the 2011 Libya Crisis. This new collaboration between WHO staff, volunteers, technologists, GIS specialists, health cluster partners and a researcher helped provide health and geographic information to support the planning phases of an in-depth countrywide health facility assessment. Outcomes of this collaboration will also aid recovery and reconstructions efforts for the Libyan health system. © 2012 ISCRAM.
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Marnix De Ridder, & Chris Twenhöfel. (2004). The design and implementation of a decision support and information exchange system for nuclear emergency management in the Netherlands. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2004 – 1st International Workshop on Information Systems for Crisis Response and Management (pp. 33–38). Brussels: Royal Flemish Academy of Belgium.
Abstract: An information system for decision support and information exchange is designed and a prototype has been build for use in the Back Office Radiological Information (BORI) of the EPAn; the Dutch nuclear emergency organisation. System developments are directed at a fast and efficient production of a radiological status report and the improvement of information exchange and communications between the participating institutes of BORI. Special attention has been given to network security and the information infrastructure to manage virtual workplaces. We have chosen for a standard web based system development for the presentation and communication facilities. This is supplemented by a GIS based system for the aggregation of measurement data and model calculations. © Proceedings ISCRAM 2004.
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Philip Degener, Henning Gösling, & Jutta Geldermann. (2013). Decision support for the location planning in disaster areas using multi-criteria methods. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 278–283). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: In this paper, a multi-criteria facility location model is represented. The model is meant to support relief organisations to determine the best warehouse location to stock emergency relief supplies in the pre-disaster phase of a natural disaster. As a result of the prepositioning of the goods the relief organisations are able to respond immediately to an occurring disaster. In consideration of a multiplicity of quantitative and qualitative objectives a criteria hierarchy is developed which can be adapted to any specific disaster area by omitting irrelevant goals. Afterwards the multi-criteria methods PROMETHEE I+II as well as different sensitivity analysis are described and the model is applied on a local level in a flood-prone area in Bangladesh. Small organisations with restrictive financial and personnel resources can especially benefit from the clear structure of the model and the user friendliness and high transparency of the PROMETHE I+II methods.
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Stephen C. Fortier, & Ioannis M. Dokas. (2008). Setting the specification framework of an Early Warning System using IDEF0 and information modeling. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 441–450). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Our goal is to develop an Early Warning System for an engineering system with a special interest in applying this to a material recovery facility. This on-going research points out that there is no clear definition of what Early Warning Systems are. A literature search for Early Warning Systems identifies hundred of thousands hits (Buchanan-Smith, 1999; Davies, Buchanan-Smith, Lambert, 1991). Almost all of the references had to do with financial systems for third world countries, tracking the destructive nature of violent conflicts that led to human suffering, or systems for syndromic surveillance. The goal of our research, and of this paper, is to define a framework for creating a specification that can be considered as the basis for the development of any Early Warning System-specifically for engineering systems. Therefore, we will describe Early Warning Systems and its requirements and specifications. Based on specification patterns, we have developed an abstract model of an Early Warning System; and developed an IDEF0 model of a material recovery facility that provides the framework for specifying an Early Warning System. The Early Warning System is then specified using information modeling.
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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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