3 research outputs found
Communicating Uncertainty and Risk in Air Quality 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
Stippling of 2D Scalar Fields
We propose a technique to represent two-dimensional data using stipples. While stippling is often regarded as an illustrative method, we argue that it is worth investigating its suitability for the visualization domain. For this purpose, we generalize the Linde-Buzo-Gray stippling algorithm for information visualization purposes to encode continuous and discrete 2D data. Our proposed modifications provide more control over the resulting distribution of stipples for encoding additional information into the representation, such as contours. We show different approaches to depict contours in stipple drawings based on locally adjusting the stipple distribution. Combining stipple-based gradients and contours allows for simultaneous assessment of the overall structure of the data while preserving important local details. We discuss the applicability of our technique using datasets from different domains and conduct observation-validating studies to assess the perception of stippled representations.publishe