Tina Mioch, Reinier Sterkenburg, Tatjana Beuker, & Mark A. Neerincx. (2021). Actionable Situation Awareness: Supporting Team Decisions in Hazardous Situations. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 62–70). Blacksburg, VA (USA): Virginia Tech.
Abstract: Situation Awareness (SA) has been recognized and studied as an important requirement for an effective task performance of first responders. The integration of increasingly advanced sensor, network and artificial intelligence technology into the work processes affects the building, maintenance and sharing of SA. Connecting SA to decision support models provides new possibilities for the development of actionable SA (aSA), entailing information that guides the momentary decision-making processes of the concerning actors. In the European ASSISTANCE project, we are developing an aSA module that displays information about gas distributions, its current and predicted future states (e.g., entailing risks of breathing-in of toxic gases), with references to effective decision-making patterns for this situation. The aSA model is continuously updated based on sensor data. This paper gives an overview of this aSA module for chemical hazard prediction and corresponding display, and presents initial team design patterns that will be integrated into this display to support its actionability.
|
|
Leon Derczynski, Kenny Meesters, Kalina Bontcheva, & Diana Maynard. (2018). Helping Crisis Responders Find the Informative Needle in the Tweet Haystack. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 649–662). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Crisis responders are increasingly using social media, data and other digital sources of information to build a situational understanding of a crisis situation in order to design an effective response. However with the increased availability of such data, the challenge of identifying relevant information from it also increases. This paper presents a successful automatic approach to handling this problem. Messages are filtered for informativeness based on a definition of the concept drawn from prior research and crisis response experts. Informative messages are tagged for actionable data – for example, people in need, threats to rescue efforts, changes in environment, and so on. In all, eight categories of actionability are identified. The two components – informativeness and actionability classification – are packaged together as an openly-available tool called Emina (Emergent Informativeness and Actionability).
|
|