Mark Hoogendoorn, Catholijn M. Jonker, Viara Popova, Alexei Sharpanskykh, & Lai Xu. (2005). Formal modelling and comparing of disaster plans. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 97–100). Brussels: Royal Flemish Academy of Belgium.
Abstract: Every municipality in The Netherlands has its own disaster plan. A disaster plan contains the blueprint of how to handle incidents in the municipality with the aim of preventing incidents to grow into disasters. Given that each municipality has its own organisations, enterprises, infrastructure, and general layout, the disaster plans also differ. On the other hand, the disaster plans have a lot in common. Some municipalities use a common starting point, others develop their own disaster plan from scratch. In this paper two independently developed disaster plan are compared using formal modelling techniques. The analysis reveals that some interesting differences do not stem from a difference in the makings of the municipality. These differences touch the fundamentals of the communication during incident management, and might well have a critical impact in dealing with pending disasters.
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Corine H.G. Horsch, Nanja J. J. M. Smets, Mark A. Neerincx, & Raymond H. Cuijpers. (2013). Comparing performance and situation awareness in USAR unit tasks in a virtual and real environment. 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. 556–560). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: A convenient way to test Urban Search And Rescue (USAR) robots would be in virtual environments (VEs). Evaluations in VEs are generally accepted as alternative for real scenarios. There are obvious differences between operation in a real and virtual environment. Nonetheless, the current experiment showed no significant differences in situation awareness (SA) and performance during several elementary tasks (e.g. slalom) between a virtual world and a previous experiment in reality (Mioch, Smets, & Neerincx, 2012). Only small dependencies between the unit tasks were found. The effect of individual differences (like gender, km driven per year, and gaming experience), were significant for certain elementary tasks. Testing robots in virtual environments could still be useful even if differences between VE and reality exist, since comparisons of different conditions in VE seems to have the same results as the same comparison in the field (Bishop & Rohrmann, 2003; Van Diggelen, Looije, Mioch, Neerincx, & Smets, 2012).
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Jeremy Diaz, Lise St. Denis, Maxwell B. Joseph, Kylen Solvik, & Jennifer K. Balch. (2020). Classifying Twitter Users for Disaster Response: A Highly Multimodal or Simple Approach? In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 774–789). Blacksburg, VA (USA): Virginia Tech.
Abstract: We report on the development of a classifier to identify Twitter users contributing first-hand information during a disaster. Identifying such users helps social media monitoring teams identify critical information that might otherwise slip through the cracks. A parallel study (St. Denis et al., 2020) demonstrates that Twitter user filtering creates an information-rich stream of content, but the best way to approach this task is unexplored. A user's profile contains many different “modalities” of data, including numbers, text, and images. To integrate these different data types, we constructed a multimodal neural network that combines the loss function of all modalities, and we compared the results to many individual unimodal models and a decision-level fusion approach. Analysis of the results suggests that unimodal models acting on Twitter users' recent tweets are sufficient for accurate classification. We demonstrate promising classification of Twitter users for crisis response with methods that are (1) easy to implement and (2) quick to both optimize and infer.
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Lennart Landsberg, Jörg Schmidt, & Ompe Aimé Mudimu. (2022). Synthesising Comparisons to Develop a Generic Command and Control System. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 392–403). Tarbes, France.
Abstract: Large and small incidents challenge emergency services around the world. Regardless of the size of the incident, command and control (C2)-systems are used to manage the situation, allowing a rapid and coordinated intervention. As all implemented actions result from the outputs of C2-systems, they are a fundamental component of the response. That is why they must be highly reliable and efficient. A research initiative is therefore addressing the approach of evaluating C2-systems on a scenario basis and using key performance indicators (KPI). To ensure that the KPIs can be applied to any form of incident control, a generic C2-system was developed by comparing and merging six German- and English-language C2-systems as well as one international standard. With this step, a comprehensive and detailed C2-system was developed, which is presented in this paper.
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Linda Plotnick, Elizabeth Avery Gomez, Connie White, & Murray Turoff. (2007). Furthering development of a unified emergency scale using Thurstone's Law of Comparative Judgment: A progress report. 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. 411–418). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In disasters, local civilians on or near the scene, are often first to respond and give aid. Therefore, the public needs to be well-informed with accurate, time critical information. However, a primary information source is event-specific scales that are inconsistent in their categorization and measurement, adding confusion to public responsiveness. These scales are not extendable to new emergencies in a changing world. We argue for development of a unified emergency scale to facilitate communication and understanding. This scale will inform local communities with regional community-specific information, and will be extendable for further use by professional responders. Research in progress elicited 15 dimensions of an emergency using a Delphi-like process and then ranked the dimensions by importance utilizing Thurstone's Law of Comparative Judgment. Contributions of this paper are to highlight the need for an unequivocal, unified scale and further its development.
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Connie White, Murray Turoff, & Bartel A. Van De Walle. (2007). A dynamic delphi process utilizing a modified thurstone scaling method: Collaborative judgement in emergency response. 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. 7–15). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In an extreme event or major disaster, very often there are both alternative actions that might be considered and far more requests for actions than can be executed immediately. The relative desirability of each option for action could be a collaborative expression of a significant number of emergency managers and experts trying to manage the most desirable alternatives at any given time, in real time. Delphi characteristics can satisfy these needs given that anyone can vote or change their vote on any two options, and voting and scaling are used to promote a group understanding. Further utilized with Thurstone's Law of Comparative Judgment, a group decision or the range of acceptability a group is willing to consent to, can be calculated and utilized as a means of producing the best decision. A ubiquitous system for expeditious real-time decision making by large virtual teams in emergency response environments is described.
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