Exploring Content-Driven Axis Compression for Visualization of Heterogeneous Data

Datasets with large heterogeneity in data density are prone to result in visualizations with unutilized whitespace and overplotting. Sequential data, such as in genomics or electronic health records, often has such density distributions, containing long empty stretches between data-rich regions. For these data types, the exact distance is not necessarily the central feature, and plotting along a linear axis causes occlusion of the actual datapoints. In this work, we evaluate content-driven axis compression for minimizing whitespace and reducing occlusion while maintaining distance information. The axis is compressed based on the density of the data, and it requires minimal a priori knowledge of the data distribution. We present a framework for reasoning about the benefits and drawbacks of using axis compression. The framework includes reasoning about the separability of items and clusters, suggests careful consideration of how the position visual channel is utilized, and highlights the influence of the usage context for which the visualization is designed. We conducted a study to evaluate the strengths and weaknesses of axis compression and found that it leads to improved item visibility while maintaining high accuracy, also for tasks targeted at understanding the data distribution.