This project was a collaboration with the University of Washington's Interactive Data Lab. The goal was to design and test the usability of different visual encodings of uncertainty for real-time decision-making around bus arrival data. The project was funded by the National Science Foundation and was published in CHI 2018 (The Association for Computing Machinery confrence on Human-Computer Interaction).

Finding effective ways to communicate uncertainty is a critical challenge in data visualization. This project aimed to address this challenge by designing and testing the usability of different visual encodings of uncertainty for real-time decision-making around bus arrival data. The end result was ranking of perceptually effective visual encodings of uncertainty for real-time decision-making and an important understanding of how people might make effective decisions with data visualization.