UofC-Bayes: A Bayesian Approach to Visualizing Uncertainty in Radiation Data

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2019-10
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Abstract
Disasters demand a quick response based on incomplete information. For the Saint Himark dataset, part of the 2019 VAST Challenge, we focused on delivering a visualization which accurately conveyed that uncertainty. While our analysis was done offline, we chose techniques and algorithms which could easily be applied to real-time usage. Our visualization for the second mini-challenge was two separate screens for two separate tasks: a broad overview of radiation levels, and a detailed look at specific sensors.
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Hornbeck, H., & Alim, U. (2019). UofC-Bayes: A Bayesian approach to visualizing uncertainty in radiation data. 2019 IEEE Conference on Visual Analytics Science and Technology (VAST). doi:10.1109/vast47406.2019.8986936
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http://hdl.handle.net/1880/113482
 https://doi.org/10.11575/PRISM/46129
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Science Research & Publications