What makes a scatterplot hard to comprehend: data size and pattern salience matter

With the growing popularity of visualizations in various fields, visualization comprehension has gained considerable attention. In this work, we focus on the effect of data size and pattern salience on comprehension of scatterplot, a popular visualization type. We began with a preliminary study in which we interviewed 50 people in terms of comprehension difficulties of 90 different visualizations. The results reveal that data size is one of the top three factors affecting visualization comprehension. Besides, the effect of data size probably depends on the pattern salience within the data. Therefore, we carried out our experiment on the effect of data size and data-related pattern salience on three intermediate-level comprehension tasks, namely finding anomalies, judging correlation, and identifying clusters. The tasks were conducted on the scatterplot due to its familiarity to users and ability to support diverse tasks. Through the experiment, we found a significant interaction effect of data size and pattern salience on the comprehension of the trends in scatterplots. In specific conditions of pattern salience, data size impacts the judgment of anomalies and cluster centers. We discussed the findings in our experiment and further summarized the factors in visualization comprehension. Graphic abstract