Space-Time Kernel Density Estimation for Real-Time Interactive Visual Analytics

We present a GPU-based implementation of the Space-Time Kernel Density Estimation (STKDE) that provides massive speed up in analyzing spatial- temporal data. In our work we are able to achieve sub- second performance for data sizes transferable over the Internet in realistic time. We have integrated this into web-based visual interactive analytics tools for analyzing spatial-temporal data. The resulting inte- grated visual analytics (VA) system permits new anal- yses of spatial-temporal data from a variety of sources. Novel, interlinked interface elements permit efficient, meaningful analyses

Scalable, situationally aware visual analytics and applications

There is a need to understand large and complex datasets to provide better situa- tional awareness in-order to make timely well-informed actionable decisions in critical environments. These types of environments include emergency evacuations for large buildings, indoor routing for buildings in emergency situations, large-scale critical infrastructure for disaster planning and first responders, LiDAR analysis for coastal planning in disaster situations, and social media data for health related analysis. I introduce novel work and applications in real-time interactive visual analytics in these domains. I also detail techniques, systems and tools across a range of disciplines from GPU computing for real-time analysis to machine learning for interactive analysis on mobile and web-based platforms

An Integrated In-Situ Approach to Impacts from Natural Disasters on Critical Infrastructures

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 causes 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.publishe