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Communicating Geographical Risks in Crisis Management: The Need for Research.

Simon French1, Nikolaos Argyris2, Stephanie M Haywood3

  • 1Department of Statistics, University of Warwick, Coventry, UK.

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Summary

Communicating spatiotemporal uncertainty during crises is challenging. Presenting multiple scenarios can help analysts convey complex geographical and temporal risks when quantification is difficult.

Keywords:
Crisis responsegeographical riskrisk communicationscenario-focused thinkingspatiotemporal uncertainty

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Area of Science:

  • Geographic Information Science
  • Crisis Management
  • Cognitive Science

Background:

  • Crises often involve significant spatiotemporal uncertainty, such as industrial accidents or hurricane paths.
  • Effectively communicating this uncertainty to crisis managers is a critical but underexplored area.
  • Existing literature on representing spatial uncertainty on maps is limited.

Purpose of the Study:

  • To explore methods for communicating spatiotemporal uncertainty in crisis situations.
  • To address the challenges analysts face in conveying complex, uncertain data.
  • To investigate the cognitive implications of spatial uncertainty representation.

Main Methods:

  • Review of literature on spatial uncertainty visualization and communication.
  • Analysis of cognitive factors influencing the understanding of uncertain information.
  • Exploration of alternative communication strategies for deep uncertainty.

Main Results:

  • The representation of spatial uncertainty on maps faces significant cognitive challenges and potential for confusion.
  • In early crisis stages, uncertainties are often 'deep' and difficult to quantify.
  • Presenting multiple scenarios is a viable strategy for communicating deep spatiotemporal uncertainty.

Conclusions:

  • Effective communication of spatiotemporal uncertainty is crucial for crisis management.
  • Cognitive limitations necessitate careful consideration when visualizing uncertain data.
  • Multiple scenario presentation offers a practical approach for conveying deep uncertainty when precise quantification is not feasible.