Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations

By Jeffrey Heer, Nicholas Kong, Maneesh Agrawala. International Conference on Human Factors in Computing Systems, 2009

366 citations recorded by the source corpus.

We investigate techniques for visualizing time series data and evaluate their effect in value comparison tasks. We compare line charts with horizon graphs - a space-efficient time series visualization technique - across a range of chart sizes, measuring the speed and accuracy of subjects' estimates of value differences between charts. We identify transition points at which reducing the chart height results in significantly differing drops in estimation accuracy across the compared chart types, and we find optimal positions in the speed-accuracy tradeoff curve at which viewers performed quickly without attendant drops in accuracy. Based on these results, we propose approaches for increasing data density that optimize graphical perception.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/097ab740c8c6e3482bf39255e192894d12147f68

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In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

40 years of boxplots (2010, background): “The sectioned density plot is similar in spirit to horizon graphs for time series (Reijner, 2008), which have been found to be just as readable as regular line graphs despite taking up much less space (Heer et al., 2009).”
A Nested Model for Visualization Design and Validation (2009, neutral): “compare line charts to the more space-efficient horizon graphs [16].”
Crowdsourcing graphical perception: using mechanical turk to assess visualization design (2010, background): “’s [12] study of timeseries visualizations, which found that as chart heights were decreased (from a starting height of 48 pixels, or 135 mm on Heer et al.”
Stacking-Based Visualization of Trajectory Attribute Data (2012, background): “Horizon Graphs) requires sufficient height of the bands to achieve higher precision [15].”
UpSet: Visualization of Intersecting Sets (2014, methodology): “To mitigate this effect, we developed horizon bars, inspired by horizon charts [14, 22].”
Complex heatmap visualization (2022, methodology): “A horizon chart is a visualization method that vertically splits an area chart with uniform size, then the bands are layered on top of each other [27].”
When (ish) is My Bus?: User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems (2016, background): “Techniques like horizon graphs [15] and sparklines [34] have been proposed for visualizing time-series data in spaceconstrained environments.”