Communicating Uncertainty and Risk in Air Quality Maps
This work addresses the problem of effectively communicating environmental risk to the public, particularly for health-conscious individuals, by improving the design of air quality maps.
This paper investigates how different visualizations of uncertainty in air quality maps influence user decision-making regarding physical activity. The study found that uncertainty-aware maps led to more cautious decisions and a significant increase in users choosing to reduce physical activity compared to standard maps.
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.