Evacuation of large urban structures, such as campus buildings, arenas, or stadiums, is of prime interest to emergency responders and planners. Although there is a large body of work on evacuation algorithms and their application, most of these methods are impractical to use in real-world scenarios (nonreal-time, for instance) or have difficulty handling scenarios with dynamically changing conditions. Our overall goal in this work is toward developing computer visualizations and real-time visual analytic tools for evacuations of large groups of buildings, and in the long term, integrate this with the street networks in the surrounding areas. A key aspect of our system is to provide situational awareness and decision support to first responders and emergency planners. In our earlier work, we demonstrated an evacuation system that employed a modified variant of a heuristic-based evacuation algorithm, which (1) facilitated real-time complex user interaction with first responder teams, in response to information received during the emergency; (2) automatically supported visual reporting tools for spatial occupancy, temporal cues, and procedural recommendations; and (3) multi-scale building models, heuristic evacuation models, and unique graph manipulation techniques for producing near real-time situational awareness. The system was tested in collaboration with our campus police and safety personnel, via a tabletop exercise consisting of three different scenarios. In this work, we have redesigned the system to be able to handle larger groups of buildings, in order to move toward a full-campus evacuation system. We demonstrate an evacuation simulation involving 22 buildings in the University of North Carolina, Charlotte campus. Second, the implementation has been redesigned as a WebGL application, facilitating easy dissemination and use by stakeholders.

Abstract-Natural disasters can have a devastating effect on critical infrastructures, especially in case of cascading effects among multiple infrastructures such as the electric power grid, the communication network, and the road network. While there exist detailed models for individual types of infrastructures such as electric power grids, these do not encompass the various interconnections and interdependencies to other networks. Cascading effects are hard to discover and often the root cause of problems remain unclear. In order to enable real-time situational awareness for operational risk management one needs to be aware of the broader context of events. In this paper, we present a unique visual analytics control room system that integrates the separate visualizations of the different network infrastructures with social media analysis and mobile in-situ analysis to help to monitor the critical infrastructures, detecting cascading effects, performing root cause analyses, and managing the crisis response. Both the social media analysis and the mobile in-situ analysis are important components for an effective understanding of the crisis and an efficient crisis response. Our system provides a mechanism for conjoining the available information of different infrastructures and social media as well as mobile in-situ analysis in order to provide unified views and analytical tools for monitoring, planning, and decision support. A realistic use case scenario based on real critical infrastructures as well as our qualitative study with crisis managers shows the potential of our approach.

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 realtime, for instance) or have di culty 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 di↵erent scenarios, and their resulting assessment of the system.

Binge drinking is a severe health problem faced by many US colleges and universities. College students often post drinking-related text and images on social media, portraying their alcohol use as socially desirable. In this project, we investigated the feasibility of mining the heterogeneous data (e.g. text, images, and videos) on Facebook to identify drinking-related contents. We manually annotated 4266 posts during 21 October 2011 and 3 November 2014 from "I'm Shmacked" group on Facebook, where 511 posts were drinking-related. Our machine learning models show that by combining heterogeneous data types, we were able to identify drinking-related posts with an F1-score of 0.81. Prediction models built on text data were more reliable compared to those built on image and video data for predicting drinking-related contents. As the first step of our efforts in this direction, this feasibility study showed promise toward unleashing the potential of mining social media to identify students who binge drink.

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