Trautner, ThomasSbardellati, MaximilianStoppel, SergejBruckner, StefanBender, JanBotsch, MarioKeim, Daniel A.2022-09-262022-09-262022978-3-03868-189-2https://doi.org/10.2312/vmv.20221205https://diglib.eg.org:443/handle/10.2312/vmv20221205Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing --> Visualization techniques; Visualization theory, concepts and paradigmsHuman centered computingVisualization techniquesVisualization theoryconcepts and paradigms10.2312/vmv.2022120565-739 pages