Telmo Zarraonandia, Victor A. Bañuls, Ignacio Aedo, Paloma Díaz, & Murray Turoff. (2014). A scenario-based virtual environment for supporting emergency training. 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. 597–601). University Park, PA: The Pennsylvania State University.
Abstract: Simulation exercises are particularly popular for training in emergency situations. Exercises can vary in their degree of realism, complexity and level of stress, but they all try to reproduce a scenario of a real emergency so that each participant simulates the actions carried out for the role they should play. They not only support effective and situated learning, but they can also serve to improve the plan by allowing the identification of weak points and potential drawbacks in it. To facilitate the design and implementation of 3D virtual environments in which training exercises can be conducted, in this paper we propose to use the Cross-Impact Analysis technique in combination with an educational game platform called GRE. We also present a Simulation Authoring Tool that allows the designer to carry out the integration of the knowledge captured by means of Cross-Impact into the game designs that GRE can interpret.
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Niels Netten, & Maarten Van Someren. (2006). Automated support for dynamic information distribution in incident management. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 230–237). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: For all emergency response personnel involved in crisis situations it is essential to timely acquire all information critical to their task performance. However, in practice errors occur in the distribution of information between these collaborating actors leading to mistakes and subsequently more damage to the situation. In this paper we present a prototype system for dynamic information distribution able to support the information flow between collaborating crisis actors. The system has been evaluated by means of simulated experiments that use data from a real incident scenario. The results indicate that automated support by means of Machine Learning method works well. Especially, when actor work context features are included, then the performance on selecting and distributing relevant information is high. Furthermore, actors acquire relevant information much faster making group communication much more efficient.
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Joeri Van Laere, Jessica Lindblom, & Tarja Susi. (2007). Requirements for emergency management training from a 'passion for failures' perspective. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 449–456). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Swedish municipalities are stimulated to conduct emergency management exercises in addition to developing crisis plans. These exercises tend to be grounded in an instrumental philosophy. There is too much focus on doing the exercise and too little attention for the implementation of lessons learned afterwards. A common experience is that the same 'mistakes' are discovered again and again in yearly exercises. Furthermore there is a paradoxical balance between empowering the organization in its learning process (positive feedback) and revealing the failures (negative feedback). In this paper we reflect on the learning process in a Swedish municipality in 2006 where two emergency management exercises were held and where a minor and a major crisis occurred during the year. We argue that the longitudinal learning process should be the focus in stead of ad hoc exercises. In addition we develop some requirements for emergency management training from a 'passion for failures' perspective.
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Axel Schulz, Tung Dang Thanh, Heiko Paulheim, & Immanuel Schweizer. (2013). A fine-grained sentiment analysis approach for detecting crisis related microposts. 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. 846–851). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Real-time information from microposts like Twitter is useful for applications in the crisis management domain. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. Sentiment analysis has been shown as an effective tool to detect microposts (such as tweets) that contribute to situational awareness. However, current approaches only focus on two or three emotion classes. But using only tweets with negative emotions for crisis management is not always sufficient. The amount of remaining information is still not manageable or most of the tweets are irrelevant. Thus, a more fine-grained differentiation is needed to identify relevant microposts. In this paper, we show the systematic evaluation of an approach for sentiment analysis on microposts that allows detecting seven emotion classes. A preliminary evaluation of our approach in a crisis related scenario demonstrates the applicability and usefulness.
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Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Díaz, & Patrick Meier. (2013). Extracting information nuggets from disaster- Related messages in social media. 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. 791–801). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.
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