Modeling How Humans Judge Dot-Label Relations in Point Cloud Visualizations

When point clouds are labeled in information visualization applications, sophisticated guidelines as in cartography do not yet exist. Existing naive strategies may mislead as to which points belong to which label. To inform improved strategies, we studied factors influencing this phenomenon. We derived a class of labeled point cloud representations from existing applications and we defined different models predicting how humans interpret such complex representations, focusing on their geometric properties. We conducted an empirical study, in which participants had to relate dots to labels in order to evaluate how well our models predict. Our results indicate that presence of point clusters, label size, and angle to the label have an effect on participants' judgment as well as that the distance measure types considered perform differently discouraging the use of label centers as reference points.