Risk-Based Decision Making and Resource Allocation

Risk-based decision making is a growing trend and we have developed tools and novel methods for interactive visual risk-based decision making environments and risk-based resource allocation.  Our risk-based decision making environments can assist in exploring alternative plans and courses of action, and evaluate the risk vs. return of different alternatives.

MetricsVis - A Visual Analytics Framework for Evaluating Individual, Team, and Organization Performance

Developed based on law enforcement agencies' need for more effective planning, resource allocation, decision-making, and community policing, MetricsVis allows users to measure, evaluate, and compare officer performance through interactive and coordinated visual dialogs, presented in a comparison and evaluation environment.

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cgSARVA - Coast Guard Search and Rescue Visual Analytics

This system provides Coast Guard managers and analysts with a suite of tools for analyzing the distribution of previous search and rescue cases and a methodology for understanding the risk, efficiencies, and benefits involved with reallocation or reduction of resources.

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Visual Analytics for Security Applications (VASA)

This project provides VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids.

Purdue: Ebert / University of North Carolina at Charlotte (UNCC): Ribarsky / University of Minnesota (UMN): Kennedy | University of Texas at Austin: Gaither

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Context-aware Mobile Visual Analytics for Emergency Response

This tool provides emergency personnel with context aware visualized data obtained from heterogeneous sources on mobile devices. 
Purdue: Ebert, Delp, Collins / Stuttgart: Ertl, Weiskopf
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Foreign Animal and Zoonotic Disease Visual Analytics

This project focuses on applying new exploratory visual analytics methods for spatial, space-time foreign animal and zoonotic disease data which will lead us to better disease outbreak prediction.

Purdue: Ebert, Cleveland / Penn State: Hardisty, MacEachren

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