Effective designs improve monitoring of current and past time series data
The Now and Then: Integrating Current and Historical Data in Small Multiple Time Series Visualization
Human-Computer Interaction
Summary
People need to watch ongoing data from many sources, like patients or machines, to quickly spot problems. The authors studied different ways of showing current and past data together to help users quickly find important values while still seeing trends over time. They tested designs that mixed or separated current values and historical lines, finding that combining them helps people respond faster. Using size to highlight current values and color to show if values are beyond limits worked best.
What this means in practice
- •For hospital data teams: Design dashboards that help medical staff quickly spot abnormal patient data across many individuals by integrating current values into trend charts.
- •For manufacturing monitoring teams: Create real-time machine monitoring displays that improve speed and accuracy in identifying machines operating outside acceptable parameters.
- •For financial risk analysts: Develop monitoring tools that allow quick appraisal of current asset values alongside their historical trends for better risk assessment.$Commercial implications: Enables creation of improved financial monitoring products selling to banks and investment firms needing rapid data appraisal.
Authors
Sydney K. Purdue, Enrico Bertini, Melanie Tory
Abstract
Small multiple time series visualizations are often used for real-time data monitoring tasks in high-impact domains such as healthcare and manufacturing. Effective design is critical because users rely on these visualizations to monitor data from many entities, such as patients or machines, often while distracted. Users may need to rapidly appraise current values for each entity, monitoring for those that go outside an acceptable range, while also watching temporal trends. However, no design guidelines currently exist for visually emphasizing current values in historical time series represented by small multiples. Via an iterative design process informed by a review of related literature and theory on visual channels and emphasis, we present a design space for glanceable time series small multiple displays. We evaluate this space through two online empirical studies, testing against non-threshold and threshold rapid appraisal tasks. Our results provide insights into merging current value and historical data visualizations for rapid appraisal tasks in time series monitoring. For non-threshold tasks, we found that size encodings on the current value, spatially integrated into the line chart, may provide a good compromise, with 28% response time improvement for tasks involving finding large current values and minimal interference with trend lookup tasks. More generally, integrated designs outperformed separated designs (in which the current value representation is spatially separated from the historical trend line). For threshold tasks, color threshold encodings significantly outperformed shaded band encodings. All supplemental materials are available at https://osf.io/wzf6a.