Communicating Uncertainty and Risk in Air Quality Maps

TL;DR

This paper explores how visualizing uncertainty in air quality maps influences user decision-making, demonstrating that uncertainty-aware maps lead to more cautious behavior and better risk communication.

Contribution

It introduces novel visualization techniques for uncertainty in air quality maps and evaluates their impact on user decisions through a user study.

Findings

Uncertainty visualization increases users' caution in decision-making.

Uncertainty-aware maps lead to more consistent responses.

Designing maps with uncertainty can improve risk communication.

Abstract

Environmental sensors provide crucial data for understanding our surroundings. For example, air quality maps based on sensor readings help users make decisions to mitigate the effects of pollution on their health. Standard maps show readings from individual sensors or colored contours indicating estimated pollution levels. However, showing a single estimate may conceal uncertainty and lead to underestimation of risk, while showing sensor data yields varied interpretations. We present several visualizations of uncertainty in air quality maps, including a frequency-framing "dotmap" and small multiples, and we compare them with standard contour and sensor-based maps. In a user study, we find that including uncertainty in maps has a significant effect on how much users would choose to reduce physical activity, and that people make more cautious decisions when using uncertainty-aware maps. Additionally, we analyze think-aloud transcriptions from the experiment to understand more about how the representation of uncertainty influences people's decision-making. Our results suggest ways to design maps of sensor data that can encourage certain types of reasoning, yield more consistent responses, and convey risk better than standard maps.