Visualizing Situational Data: Applying Information Fusion for Detecting Social-Ecological Events

  • Authors:
  • Mark R. Altaweel;Lillian N. Alessa;Andrew D. Kliskey

  • Affiliations:
  • Computation Institute, University of Chicago, 5640 S.Ellis Avenue, RI 405, Chicago, IL 60637, USA;Resilience and Adaptive Management Group, Universityof Alaska Anchorage, AK, USA;Resilience and Adaptive Management Group, Universityof Alaska Anchorage, AK, USA

  • Venue:
  • Social Science Computer Review
  • Year:
  • 2010

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Abstract

As anthropogenic and environmental behaviors rapidly evolve many ecosystems and communities, managers of natural resources, scientists, and other stakeholders increasingly need tools that can rapidly alert them to emerging events that can affect social well-being. Data detailing such behaviors may derive from textual sources with varying content, requiring an approach to merge multiple media sources and create linked relationships between relevant event terms. In addition, applied methods need to provide both quantitative capacity and qualitative functionality, which can rapidly display emerging trends and potentially significant individual events. This article presents an information fusion approach for conducting text searches on web-based sources to provide managers and scientists with rapid search capabilities identifying potentially significant social芒聙聰ecological events. Along with general analytical utility, a network approach that links associated terms is used to show semantic relationships between social芒聙聰ecological terms at different timescales.