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Björn J.E. Johansson, Jiri Trnka, & Rego Granlund. (2007). The effect of geographical information systems on a collaborative command and control task. 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. 191–200). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper tests the claimed benefits of using geographical information systems (GIS) in emergency response operations. An experimental study comparing command teams using GIS and paper-based maps is presented. The study utilized a combined approach using microworld simulations together with physical artefacts. Participants in the experiment took the role of command teams, facing the task of extinguishing a simulated forest fire. A total of 132 persons, forming 22 teams, participated in the study. In eleven of the teams, the participants were given access to GIS with positioning of fire-brigades as well as sensor data about the fire outbreak. In the other eleven teams, the participants were using paper-based maps. The result shows that teams using GIS performed significantly better than teams with paper-based maps in terms of saved area. Communication volume was considerably reduced in the case of GIS teams. Implications of these results on GIS are discussed as well as methodological considerations for future research.
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Philipp Hertweck, Tobias Hellmund, Hylke van der Schaaf, Jürgen Moßgraber, & Jan-Wilhelm Blume. (2019). Management of Sensor Data with Open Standards. 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: In an emergency, getting up-to-date information about the current situation is crucial to orchestrate an efficient response. Due to its objectivity, preciseness and comparability, time-series data offer broad possibilities to manage emergency incidents. Since the Internet of Things (IoT) is rapidly growing with an estimated number of 30 billion sensors in 2020, it offers excellent potential to collect time-series data for improving situational awareness. The IoT brings several challenges: caused by a splintered sensor manufacturer landscape, data comes in various structures, incompatible protocols and unclear semantics. To tackle these challenges a well-defined interface, from where uniform data can be queried, is necessary. The Open Geospatial Consortium (OGC) has recognized this demand and developed the SensorThings API standard, an open, unified way to interconnect devices throughout the IoT, which is implemented by the FRaunhofer-Opensource-SensorThings-Server (FROST). This paper presents the standard, its implementation and the application to the domain of crisis management.
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Schreiber. (2007). Automatic generation of sensor queries in a WSN for environmental monitoring. 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. 245–254). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The design of a WSN for environmental data monitoring is a largely ad-hoc human process. In this paper, we propose the automatic generation of queries for sensor data extraction, based on the collection of a number of parameters concerning the physical phenomenon to be controlled, the relevant physical variables, the types of sensors to be deployed and their allocation, the data collection frequencies, and other features.
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Shane Errol Halse, Aurélie Montarnal, Andrea Tapia, & Frederick Benaben. (2018). Bad Weather Coming: Linking social media and weather sensor data. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 507–515). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: In this paper we leverage the power of citizen supplied data. We examined how both physical weather sensor data (obtained from the weather underground API) and social media data (obtained from Twitter) can serve to improve local community awareness during a severe weather event. A local tornado warning was selected due to its small scale and isolated geographic area, and only Twitter data found from within this geo-locational area was used. Our results indicate that during a severe weather event, an increase in weather activity obtained from the local weather sensors does correlate with an increase in local social media usage. The data found on social media also contains additional information from, and about the community of interest during the event. While this study focuses on a small scale event, it provides the groundwork for use during a much larger weather event.
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Gerhard Wickler, George Beckett, Liangxiu Han, Sung Han Koo, Stephen Potter, Gavin Pringle, et al. (2009). Using simulation for decision support: Lessons learned from FireGrid. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper describes some of the lessons learned from the FireGrid project. It starts with a brief overview of the project. The discussion of the lessons learned that follows is intended for others attempting to develop a similar system, where sensor data is used to steer a super-real time simulation in order to generate predictions that will provide decision support for emergency responders.
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