Visualizing Situational Data: Applying Information Fusion for Detecting Social-Ecological Events
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-ecological events. Along with general analytical utility, a network approach that links associated terms is used to show semantic relationships between social-ecological terms at different timescales.
visualization information fusion social-ecological events decision support text analysis data mining