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Proceedings Paper

Visual analysis of situationally aware building evacuations
Author(s): Jack Guest; Todd Eaglin; Kalpathi Subramanian; William Ribarsky
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Paper Abstract

Rapid evacuation of large urban structures (campus buildings, arenas, stadiums, etc.) is a complex operation and of prime interest to emergency responders and planners. Although there is a considerable body of work in evacuation algorithms and methods, most of these are impractical to use in real-world scenarios (non real-time, for instance) or have difficulty handling scenarios with dynamically changing conditions. Our goal in this work is towards developing computer visualizations and real-time visual analytic tools for building evacuations, in order to provide situational awareness and decision support to first responders and emergency planners. We have augmented traditional evacuation algorithms in the following important ways, (1) facilitate real-time complex user interaction with first responder teams, as information is received during an emergency, (2) visual reporting tools for spatial occupancy, temporal cues, and procedural recommendations are provided automatically and at adjustable levels, and (3) multi-scale building models, heuristic evacuation models, and unique graph manipulation techniques for producing near real-time situational awareness. We describe our system, methods and their application using campus buildings as an example. We also report the results of evaluating our system in collaboration with our campus police and safety personnel, via a table-top exercise consisting of 3 different scenarios, and their resulting assessment of the system.

Paper Details

Date Published: 4 February 2013
PDF: 14 pages
Proc. SPIE 8654, Visualization and Data Analysis 2013, 86540G (4 February 2013); doi: 10.1117/12.2001535
Show Author Affiliations
Jack Guest, The Univ. of North Carolina at Charlotte (United States)
Todd Eaglin, The Univ. of North Carolina at Charlotte (United States)
Kalpathi Subramanian, The Univ. of North Carolina at Charlotte (United States)
William Ribarsky, The Univ. of North Carolina at Charlotte (United States)


Published in SPIE Proceedings Vol. 8654:
Visualization and Data Analysis 2013
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen, Editor(s)

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