Yang, HaiyanPajarola, RenatoGuthe, MichaelGrosch, Thorsten2023-09-252023-09-252023978-3-03868-232-5https://doi.org/10.2312/vmv.20231233https://diglib.eg.org:443/handle/10.2312/vmv20231233Scatterplot sampling has long been an efficient and effective way to resolve the overplotting issues commonly occurring in large-scale scatterplot visualization applications. However, it is challenging to preserve the existence of low-density points or outliers after sampling for a sub-sampling algorithm if, at the same time, faithfully representing the relative data densities is of importance. In this work, we propose to address this issue in a visual-assisted manner. While the whole dataset is sub-sampled, the density of the outliers is modeled and visually integrated into the final scatterplot together with the sub-sampled point data. We showcase the effectiveness of our proposed method in various cases and user studies.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Information visualization; Visualization techniquesHumancentered computing → Information visualizationVisualization techniques10.2312/vmv.20231233115-1217 pages