MC2 - SUA: A Sensor Uncertainty Analysis Tool of Radiation Measurement Data (VAST Challenge 2019 Award - Integrated Tool with Rich Data Encodings)

Shaobin Xu, Yiming Lin, Dezhan Qu, Ke Ren, Huijie Zhang

Abstract: The effective analysis of data collected by mobile sensors and static sensors deployed in cities is a great challenge, due to the spatiotemporal characteristics and uncertainty. To meet the challenge of VAST 2019 Mini-Challenge 2, SUA, an interactive visual analysis tool with a novel uncertainty glyph is designed. The tool provides a clear understanding of conditions in the city by visualizing the trajectories, the measurements, as well as the uncertainty of the sensors, and supports an overview-to-detail visual reasoning process. The rich data encodings and interactions help users solve the questions effectively.