论文标题
传达空气质量图的不确定性和风险
Communicating Uncertainty and Risk in Air Quality Maps
论文作者
论文摘要
环境传感器提供了重要的数据，以了解我们的周围环境。例如，基于传感器读数的空气质量图可帮助用户做出决定，以减轻污染对其健康的影响。标准地图显示了来自单个传感器或彩色轮廓的读数，表明估计污染水平。但是，显示单个估计值可能会隐藏不确定性并导致风险低估，同时显示传感器数据产生的解释各不相同。我们介绍了空气质量图中不确定性的几种可视化，包括频率构图的“ dotmap”和小倍数，我们将它们与标准轮廓和基于传感器的地图进行了比较。在一项用户研究中，我们发现在地图中包括不确定性会对用户选择减少体育锻炼的程度有重大影响，并且在使用不确定性吸引地图时，人们会做出更谨慎的决定。此外，我们分析了实验中的思考转录，以更多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，这些数据可以鼓励某些类型的推理，产生更一致的响应并比标准地图更好地传达风险。
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.
