Andreas Lotter, Philipp John, Patricia Schütte, Florian Brauner, & Frank Fiedrich. (2020). Field Observation of Parallel Working Coordination Groups and Command and Control Centres to Understand and Improve Inter-organizational Information Management – A Methodical 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. 303–314). Blacksburg, VA (USA): Virginia Tech.
Abstract: In emergency response, parallel working coordination groups and command and control centres are responsible to deal with complex events. A well-functioning exchange of information between organizations, officials and these coordination groups is the basis for an efficient risk management. This paper describes a methodical ap-proach for field observation to understand and improve the inter-organizational information management be-tween the involved partners. The method was tested within a practical approach and possible occurring problems during the observation were identified and solutions for these problems are provided.
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Mehdi Ben Lazreg, Usman Anjum, Vladimir Zadorozhny, & Morten Goodwin. (2020). Semantic Decay Filter for Event Detection. 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. 14–26). Blacksburg, VA (USA): Virginia Tech.
Abstract: Peaks in a time series of social media posts can be used to identify events. Using peaks in the number of posts and keyword bursts has become the go-to method for event detection from social media. However, those methods suffer from the random peaks in posts attributed to the regular daily use of social media. This paper proposes a novel approach to remedy that problem by introducing a semantic decay filter (SDF). The filter's role is to eliminate the random peaks and preserve the peak related to an event. The filter combines two relevant features, namely the number of posts and the decay in the number of similar tweets in an event-related peak. We tested the filter on three different data sets corresponding to three events: the STEM school shooting, London bridge attacks, and Virginia beach attacks. We show that, for all the events, the filter can eliminate random peaks and preserve the event-related peaks.
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Christelle Pierkot, Sidonie Christophe, & Jean François Girres. (2019). Exploring multiplexing tools for co-visualization in crisis units. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Natural hazards can generate damages in large inhabited areas in a very short time period. Crisis managers must
plan interventions very quickly to facilitate the arrival of the first emergency. In a crisis unit, experts visualize
heterogeneous visual representations of spatio-temporal information, in order to facilitate decision-making,
based on various types of screens, i.e. laptops, tablets, or wall screens. Visualizing all this information at the
same time on the same interface would lead to cognitive overload. In this paper, we assume that it could be of
interest to provide innovative co-visualization models and tools, to bring hazard, geospatial and climate
information together, in a shared interface. We propose to explore spatial and temporal multiplexing tools within
a dedicated geovisualization environment, in order to help expert decision-making. The proposition is
implemented with the case study of a tsunami event in the Caribbean sea.
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