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Exploratory tool for axis breaks in charts

Axis breaks are used regularly to fit data with large gaps between data subsets into a single chart. Proposed in the 1980s, axis breaks have not gotten much attention since then in terms of what characterizes “good” breaks, how many of them to introduce, and where to best place them? To answer these questions, we propose a five-step framework that specifies (1) the number of breaks, (2) their location, (3) the scaling of the resulting subaxes, (4) the “niceness” of the breaks, and (5) the formatting of the breaks. To apply this framework, we introduce a new metric, called skew, to quantify how unevenly points are distributed along an axis. Skew is then used as a cost function to formulate the search for optimal axis breaks as a clustering problem, which we solve by applying a dynamic k-means algorithm. We apply our framework specifically to Parallel Coordinate Plots and compare our algorithmic solution to established methods like percentile breaks and Jenks natural breaks. An interactive testbed to try our framework as well as its source code are made freely available. (see Resources section)

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