Patterns in temporal data can often be found across different scales, such as days, weeks, and months, making effective visualization of time-based data challenging. Here we propose a new approach for providing focus and context in time-based charts to enable interpretation of patterns across time scales. Our approach employs a focus zone with a time and a second axis, that can either represent quantities or categories, as well as a set of adjacent periphery plots that can aggregate data along the time, value, or both dimensions.
The Periphery Plot concept was motivated by our work on PrecisionVISSTA, a NIH-funded effort to develop precision medicine algorithms and software focused on patient-generated health data. We also show a use case where periphery plots are used to visualized multi-variate weather data (shown in the animated image on this page).
This project is also related to our ongoing work on Contextual Visualization with support from the NSF.
Our implementation of periphery plots is available as open source software from GitHub: https://github.com/PrecisionVISSTA/PeripheryPlots
Bryce Morrow, Trevor Manz, Arlene E. Chung, Nils Gehlenborg, David Gotz. Periphery Plots for Contextualizing Heterogeneous Time-Based Charts. IEEE VIS Short Papers (2019). Preprint available on arXiv: arXiv:1906.07637.