Event temporal distribution analysis aims to capture both global (e.g., rises and peaks) and local patterns (e.g., frequent occurrences and sudden absences). Traditional charts typically rely on adjusting binning granularities to reveal such patterns. However, this strategy forces a trade-off between global clarity and local detail and may require considerably more screen space as the number of bins increases, which limits its applicability in space-constrained visual interface design. In this paper, we propose DensityBars, a space-efficient visualization that embeds fine-grained density heatmaps of event occurrences into the coarse-grained bar chart to convey both global and local patterns simultaneously. Two real-world use cases and two formal user studies demonstrate its effectiveness and usability. Insights from studies provide valuable implications for the visual design of temporal distribution visualizations.
ACM CHI Conference on Human Factors in Computing Systems