@inproceedings {Trautner-2022-HCP,
author = {Trautner, Thomas and Sbardellati, Maximilian and Stoppel, Sergej and Bruckner, Stefan},
title = {{Honeycomb Plots: Visual Enhancements for Hexagonal Maps}},
booktitle = {Proc. of VMV 2022: Vision, Modeling, and Visualization},
editor = {Bender, Jan and Botsch, Mario and Keim, Daniel A.},
pages = {65--73},
year = {2022},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-189-2},
DOI = {10.2312/vmv.20221205},
abstract = {Aggregation 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.},
pdf = "pdfs/Trautner-2022-HCP.pdf",
thumbnails = "images/Trautner-2022-HCP-thumb.png",
images = "images/Trautner-2022-HCP-thumb.png",
youtube = "https://youtu.be/mU7QFVP3yKQ",
git = "https://github.com/TTrautner/HoneycombPlots"
}