Honeycomb Plots: Visual Enhancements for Hexagonal Maps

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.
Description

CCS Concepts: Human-centered computing --> Visualization techniques; Visualization theory, concepts and paradigms

        
@inproceedings{
10.2312:vmv.20221205
, booktitle = {
Vision, Modeling, and Visualization
}, editor = {
Bender, Jan
and
Botsch, Mario
and
Keim, Daniel A.
}, title = {{
Honeycomb Plots: Visual Enhancements for Hexagonal Maps
}}, author = {
Trautner, Thomas
and
Sbardellati, Maximilian
and
Stoppel, Sergej
and
Bruckner, Stefan
}, year = {
2022
}, publisher = {
The Eurographics Association
}, ISBN = {
978-3-03868-189-2
}, DOI = {
10.2312/vmv.20221205
} }
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