Hans Julius Betke. (2018). A Volunteer Coordination System Approach for Crisis Committees. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 786–795). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: In disaster situations security authorities and organizations have the responsibility and duty to manage the disaster response. These organizations work in elaborated command and control structures with well trained employees. But in recent events, supported by new technologies like social media and mobile devices, spontaneous volunteers from the local population gained new importance as helpful force in disaster response. The high amount of volunteers bears high potentials to improve the efficiency of several activities through pure manpower. However, these people are not integrated in existing structures and lack a proper qualification. The proper coordination of spontaneous volunteers poses new challenges for disaster authorities. In this paper we introduce the prototype of a novel information system enabling crisis committees to coordinate spontaneous volunteers by semi-automated purposive communication and allocation. The results of first staff exercises are discussed to emphasize potential benefits and open challenges.
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Elmhadhbi Linda, Karray Mohamed Hedi, & Archimède Bernard. (2018). Towards an Operational Emergency Response System for Large Scale Situations: POLARISC. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 778–785). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: After a lot of recent natural and human-made disasters all over the word, the large scale emergency response process is becoming very critical and challenging. Lives can be lost and property can be harmed. To respond to these major threats, an effective operational emergency response system needs to address the necessity of data sharing, information exchange and correlation between different Emergency Responders (ERs) including firefighters, police, health care services, army, municipality and so on to successfully respond to large scale disasters. Therefore, the goal of this paper is to introduce POLARISC, an interoperable software solution based on a common and modular ontology shared by all the ERs. Its main objective is to solve the problem of semantic difference and heterogeneity of data to guarantee a common understanding among the various ERs in order to coordinate and to obtain a real time operational picture of the situation.
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Béatrice Linot, Jérôme Dinet, François Charoy, & Valérie L. Shalin. (2018). Information gain in sociotechnical systems. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 764–777). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Much of the crisis management literature focuses on improving communication by improving the integrity of communication equipment, vulnerable for example, to the loss of electricity. But communication issues arise in sociotechnical systems with functioning communication equipment, prompting researchers and practitioners alike to bemoan the absence of information sharing. Computer scientists envision a giant virtual display accessible to all, but little thought has gone into the principles for selecting, formatting and organizing content to make it useful. Here we argue that what is needed is information rather than data, and that situating data in context is key to the provision of information. Documentation of information exchange issues in real crisis management is quite superficial, generally pointing to conclusions without any supporting data. Using documentation of the Deepwater Horizon Accident in 2010, we distinguish between data and information, and the challenge this poses to the design of computational support for information sharing.
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Jürgen Moßgraber, Désirée Hilbring, Hylke van der Schaaf, Philipp Hertweck, Efstratios Kontopoulos, Panagiotis Mitzias, et al. (2018). The sensor to decision chain in crisis management. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 754–763). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: In every disaster and crisis, incident time is the enemy, and getting accurate information about the scope, extent, and impact of the disaster is critical to creating and orchestrating an effective disaster response and recovery effort. Decision Support Systems for disaster and crisis situations need to solve the problem of facilitating the broad variety of sensors available today. This includes the research domain of the Internet of Things and data coming from social media. All this data needs to be aggregated and fused, the semantics of the data needs to be understood and the results must be presented to the decision makers in an accessible way. Furthermore, the interaction and integration with risk and crisis management systems are necessary for a better analysis of the situation and faster reaction times. This paper provides an insight into the sensor to decision chain and proposes solutions and technologies for each step.
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Patrick Lieser, Alaa Alhamoud, Hosam Nima, Björn Richerzhagen, Sanja Huhle, Doreen Böhnstedt, et al. (2018). Situation Detection based on Activity Recognition in Disaster Scenarios. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 737–753). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: In disaster situations like earthquakes and hurricanes, people have difficulties accessing shelter and requesting help. Many smartphone applications provide behavioral advice or means to communicate during such situations. However, to what extent a person is affected by a disaster is often unclear, as these applications rely on the user's subjective assessment. Therefore, detecting a user's situation is key to provide more meaningful information in such applications and to allows first responders to better assess incoming messages. We propose a predictive model that recognizes four normal and ten disaster-related activities achieving an average f1-score of up to 90.1\%, solely based on sensor readings of the subject's mobile device. We conduct an extensive measurement-based evaluation to assess the impact of individual model parameters on the prediction accuracy. Our model is orientation-independent, position-independent, and subject-independent, making it an ideal foundation for future context-aware emergency applications.
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