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Near Real-Time Decision Support for Disaster Management: Hurricane Sandy

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Decision Support Systems III - Impact of Decision Support Systems for Global Environments (EWG-DSS 2013, EWG-DSS 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 184))

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Abstract

Assessing vulnerability is fundamental for efficient risk management and emergency response. Integrating analyses from preparedness and risk reduction to inform the response phase requires that structural information about demographics or industry is combined with specific local information that highlights hotspots or emerging risks in near real-time. Owing to its availability on social media or other platforms, this local information is today often collected and processed remotely with the aim to inform responders and the public via reports, maps and apps published online. This paper addresses and discusses the challenges of remote near-real time vulnerability assessments by using an indicator model, which enables the combination of heterogeneous types of information while keeping track of the associated uncertainty. This approach is illustrated by the near real-time assessments for Hurricane Sandy that hit the East Coast of the United States in 2012.

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Notes

  1. 1.

    http://www.esa.doc.gov/about-economic-indicators.

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Acknowledgements

I thank my colleagues at CEDIM for the discussions about near real-time assessments. Furthermore, I am very grateful to my colleagues of the Disaster Resilience Lab, who shared their insights about Humanitarian Information Management.

Author information

Authors and Affiliations

  1. Centre for Integrated Emergency Management, University of Agder, Grimstad, Norway

    Tina Comes

Authors
  1. Tina Comes
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Correspondence to Tina Comes .

Editor information

Editors and Affiliations

  1. SimTech Simulation Technology, Graz, Austria

    Fátima Dargam

  2. Universidad de La Frontera, University of Liverpool Management School, Liverpool, UK, Temuco, Chile

    Jorge E. Hernández

  3. Toulouse University, Toulouse, France

    Pascale Zaraté

  4. University of Plymouth, Plymouth, United Kingdom

    Shaofeng Liu

  5. UNINOVA – CA3, Lisbon, Portugal

    Rita Ribeiro

  6. University of Belgrade, Belgrade, Serbia

    Boris Delibašić

  7. University of Macedonia, Thessaloniki, Greece

    Jason Papathanasiou

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Comes, T. (2014). Near Real-Time Decision Support for Disaster Management: Hurricane Sandy. In: Dargam, F., et al. Decision Support Systems III - Impact of Decision Support Systems for Global Environments. EWG-DSS EWG-DSS 2013 2013. Lecture Notes in Business Information Processing, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-319-11364-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-11364-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11363-0

  • Online ISBN: 978-3-319-11364-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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