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.

Cosmological simulations produce a multitude of data types whose large scale makes them difficult to thoroughly explore in an interactive setting. One aspect of particular interest to scientists is the evolution of groups of dark matter particles, or "halos," described by merger trees. However, in order to fully understand subtleties in the merger trees, other data types derived from the simulation must be incorporated as well. In this work, we develop a novel interactive linked-view visualization system that focuses on simultaneously exploring dark matter halos, their hierarchical evolution, corresponding particle data, and other quantitative information. We employ a parallel remote renderer and a local merger tree selection tool so that users can analyze large data sets interactively. This allows scientists to assess their simulation code, understand inconsistencies in extracted data, and intuitively understand simulation behavior on all scales. We demonstrate the effectiveness of our system through a set of case studies on large-scale cosmological data from the HACC (Hardware/Hybrid Accelerated Cosmology Code) simulation framework.