Good, but Not That Good: An Honestly-Noisy Visualization of Low-Fidelity Data Streams

Long-term, ubiquitous sensing on wireless, power-limited devices requires aggressive data-reduction at the source to meet stringent networking and power constraints. However, the naive approach of error-triggered data updates obfuscates data and system information that is useful for downstream tasks. For example, understanding error and stability of outdoor temperature data is useful for those who are deciding what to wear in the morning. We constructed a hypothetical student-led deployment of low-fidelity temperature sensors across a university campus; designed a "noisy sensor" conceptual model to visually communicate the error and stability of the data; and compared our design against the naive baseline of displaying raw data values and a classic data visualization alternative of including a historical average. We then conducted an online survey with 150 participants and found that both the baseline and the classic alternative caused users to over-estimate accuracy of the data and stability of the underlying real-world temperature. Our noisy sensor design corrected these errors, but caused users to report false trends in the data. This study identifies need for continued work in developing task-based visualizations for low-fidelity data streams and in designing sensing systems that support them.