Investigating Effective Uncertainty Visualizations for Ordinal Crowdsourced Data of Crowding Conditions
arXiv:2607.28072 · doi:10.1145/3772363.3798740
The paper studies how different visualizations convey uncertainty in crowdsourced ordinal data about railway crowding, finding that cluster visualizations lower cognitive load and boost confidence, while bubble treemaps improve accuracy in assessing crowd levels.
Abstract
Commuters often encounter crowding in railway systems, particularly in queues where passenger density varies throughout the day. This introduces uncertainty in crowdedness, making it difficult for individuals to anticipate conditions and plan their trips effectively. Crowdsourcing has been a valuable method for collecting localized user data. But the unpredictability of crowds and the uncertainty of crowdsourced information pose new challenges for decision-making. However, we know little about how to effectively visualize uncertainty in crowdedness to support informed commuting decisions, particularly when using crowdsourced ordinal data. Here, we investigated different uncertainty visualizations and their effectiveness in representing the variability and reliability of crowdsourced crowding data. They were evaluated through an online study, and we found that cluster visualization is best suited to reduce cognitive load while maximizing user confidence and trust. On the other hand, participants showed higher accuracy in determining crowd levels when using bubble treemaps.