Abstract:
Semi-transparent visualizations are commonly used to reveal information in overlapped regions by applying colors and opacity.
While a few studies made recommendations on how to choose colors and opacity levels to maintain depth perception, they often conflict and overlook the interaction effect between these factors.
In this paper, we systematically explore the impact of color and opacity on depth order perception across eight colors, three opacity levels, and various layer orders and arrangements.
Our inferential analysis shows that both color hue and opacity significantly influence depth order perception, with the effectiveness depending on their interaction. We also derived 12 features for predictive analysis, achieving the best mean accuracy of 80.72\% and mean F1 score of 87.75\%, with opacity assigned to the front layer as the top feature for most models.
Finally, we provide a small design tool and four guidelines to better align the design rules of colors and opacity in semi-transparent visualizations.
Acknowledgement:
We would like to thank Weifeng Ni for his contributions to data collection.
In this study, Qiong Zeng is supported by grants from the National Key Research and Development Program of China (No. 2021YFF0704300) and the National Natural Science Foundation of China (No. 62372271);
Lin Lu is supported by the National Natural Science Foundation of China (No. 62472258);
and Yunhai Wang is supported by the National Natural Science Foundation of China (No. 62132017), the Natural Science Foundation of Shandong Province (No. ZQ2022JQ32),
the Fundamental Research Funds for the Central Universities, the Natural Science Foundation of Beijing Municipality (No. L247027),
and the Research Funds of Renmin University of China.
Bibtex:
@inproceedings{depthorderperception,
title = {Seeing Through the Overlap: The Impact of Color and Opacity on Depth Order Perception in Visualization},
author = {Zhiyuan Meng and Yunpeng Yang and Qiong Zeng and Kecheng Lu and Lin Lu and Changhe Tu and Funmeng Yang and Yunhai Wang},
year = {2025},
isbn = {},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3706598.3714070},
doi = {10.1145/3706598.3714070},
booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems},
articleno = {},
location = {Yokohama, Japan},
series = {CHI '25}
}
