Preston Annie, Ma Kwan-Liu
IEEE Trans Vis Comput Graph. 2023 Sep;29(9):3746-3757. doi: 10.1109/TVCG.2022.3171443. Epub 2023 Aug 1.
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.
环境传感器为了解我们周围的环境提供关键数据。例如，基于传感器读数的空气质量地图可帮助用户做出决策，以减轻污染对其健康的影响。标准地图显示单个传感器的读数或表示估计污染水平的彩色等高线。然而，显示单一估计值可能会掩盖不确定性并导致对风险的低估，而显示传感器数据会产生不同的解释。我们展示了空气质量地图中不确定性的几种可视化方法，包括频率框架“点图”和小多重图，并将它们与标准等高线图和基于传感器的地图进行比较。在一项用户研究中，我们发现地图中包含不确定性对用户选择减少身体活动的程度有显著影响，并且人们在使用具有不确定性意识的地图时会做出更谨慎的决策。此外，我们分析了实验中的出声思考记录，以更深入地了解不确定性的表示如何影响人们的决策。我们的结果提出了设计传感器数据地图的方法，这些方法可以鼓励特定类型的推理，产生更一致的反应，并比标准地图更好地传达风险。
