  Air quality maps help users make decisions to mitigate the effects of
pollution on their health. Standard maps show readings from individual air
quality sensors or colored contours indicating estimated pollution levels.
However, showing a single estimate may conceal uncertainty and reduce the
appearance 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 these maps has a significant effect on how much users would
choose to reduce their 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 that can encourage certain types of reasoning,
yield more consistent responses, and convey risk better than standard maps.
