- Users often rely on realtime predictions in everyday contexts like riding the bus, but may not grasp that such predictions are subject to uncertainty.
- We present a novel mobile interface design and visualization of uncertainty for transit predictions on mobile phones based on discrete outcomes.
- We present several candidate visualizations of uncertainty for realtime transit predictions in a mobile context, and we propose a novel discrete representation of continuous outcomes designed for small screens, quantile dotplots.
- To develop it, we identified domain specific design requirements for visualizing uncertainty in transit prediction through: 1) a literature review, 2) a large survey of users of a popular realtime transit application, and 3) an iterative design process.
- In a controlled experiment we find that quantile dotplots reduce the variance of probabilistic estimates by ~1.15 times compared to density plots and facilitate more confident estimation by end-users in the context of realtime transit prediction scenarios.
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@MikeTamir: “Visualizations of Uncertainty in everyday predictive systems #MachineLearning #DataScience”
UW Interactive Data Lab