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Fusion Methodologies in Crisis Management pp 177–211Cite as

Towards a Crowd-Sensing Enhanced Situation Awareness System for Crisis Management

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Abstract

Natural and man-made crises pose severe challenges on emergency responders, as they need to gain timely Situation Awareness (SAW) in order to decide upon adequate rescue actions. Computational SAW systems aim at supporting humans in rapidly achieving SAW by means of Information Fusion (IF), thus reduce information overload by fusing data stemming from various sensors to situation-level information. Recently, the increasing popularity of social media on mobile devices has enabled humans to act as crowd sensors, who broadcast their observations on the unfolding crisis situation over social media channels. Consequently, SAW systems for crisis management would benefit from exploiting social media as additional data source. Therefore, the aim of this chapter is to investigate upon how crowd-sensing can be incorporated into SAW systems for crisis management, by elaborating on the following issues: How can the SAW system seek and retrieve additional information from social media that may complement the situational picture obtained with other types of sensors? How can the SAW system adapt this crowd-sensing alongside the monitored situation, to keep pace with the underlying real-world incidents? We attempt at illustrating potential solutions towards these questions, by examining how crowd-sensing can deliver input data for SAW systems, elaborating on the challenges such systems need to overcome in order to identify and extract relevant information from social media, and finally, discussing the architecture of a situation-adaptive SAW system capable of exploiting both conventionally sensed data and unstructured social media content.

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Notes

  1. 1.

    Application programming interface.

  2. 2.

    http://www.pewinternet.org/2015/01/09/social-media-update-2014/, accessed on 22 April 2015.

  3. 3.

    www.internetlivestats.com/twitter-statistics, accessed on 22 April 2015.

  4. 4.

    The resulting labeled CrisisLex26 data set is available for research purposes under www.crisislex.org.

  5. 5.

    crowdsa.situation-awareness.net.

  6. 6.

    Therefore, from now on we will use the terms social media message and its Twitter-specific equivalent tweet interchangeably.

  7. 7.

    www.latimes.com/nation/nationnow/la-na-nn-hawaii-storm-iselle-juliio-20140808-story.html.

  8. 8.

    www.bbc.com/news/uk-scotland-28739164.

  9. 9.

    http://newyork.cbslocal.com/2014/08/13/flash-flood-watches-warnings-in-effect-as-heavy-rain-drenches-parts-of-tri-state/.

  10. 10.

    http://www.visual-paradigm.com.

  11. 11.

    http://hibernate.org.

  12. 12.

    http://www.drools.org.

  13. 13.

    https://irevolution.files.wordpress.com/2014/06/pcdspo-report-ondisaster-response-unified-hashtags.pdf.

  14. 14.

    http://twitter4j.org.

  15. 15.

    http://www.nhc.noaa.gov/data/tcr/index.php?season=2014.

  16. 16.

    http://dbpedia.org.

  17. 17.

    https://github.com/jendarybak/GATE-DBpedia_Spotlight.

  18. 18.

    http://www.geonames.org.

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Acknowledgements

This work has been funded by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) under grant FFG BRIDGE 838526 and under grant ÖAD WTZ AR10/2015.

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Authors and Affiliations

  1. Department of Cooperative Information Systems, Johannes Kepler University Linz, Altenbergerstr. 69, 4040, Linz, Austria

    Andrea Salfinger, Werner Retschitzegger & Wieland Schwinger

  2. Institute for Application Oriented Knowledge Processing, Johannes Kepler University Linz, Altenbergerstr. 69, 4040, Linz, Austria

    Birgit Pröll

Authors
  1. Andrea Salfinger
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  2. Werner Retschitzegger
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  3. Wieland Schwinger
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  4. Birgit Pröll
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Correspondence to Andrea Salfinger .

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Editors and Affiliations

  1. State University of New York at Buffalo, Buffalo, New York, USA

    Galina Rogova

  2. State University of New Yorkat Buffalo, Buffalo, New York, USA

    Peter Scott

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Salfinger, A., Retschitzegger, W., Schwinger, W., Pröll, B. (2016). Towards a Crowd-Sensing Enhanced Situation Awareness System for Crisis Management. In: Rogova, G., Scott, P. (eds) Fusion Methodologies in Crisis Management. Springer, Cham. https://doi.org/10.1007/978-3-319-22527-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-22527-2_9

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