An automatic approach to generating barrier-free qualitative schemes for color vision deficiency

ABSTRACT

Hundreds of millions of people suffer from color vision deficiency, leading to confusion in the perception of maps. Barrier-free colors can reduce confusion and improve the readability of maps. However, most of these colors are manually designed by experts based on extensive experience. For most mapmakers, especially novices, creating barrier-free map colors is a challenge. In this paper, we focus on qualitative schemes, a color type that easily causes confusion for people with color vision deficiency, and propose an approach to automatically generate barrier-free colors. The proposed approach consists of two steps: 1) extracting the factors of barrier-free qualitative schemes, including color vision deficiency factors and cartographic rule factors, and characterizing them, and 2) building an optimization model using these factors to generate barrier-free qualitative schemes. The approach was tested with two experimental maps: a metro map for public use and a special-use land cover map. Twenty-two students with color vision deficiency were invited to read these maps and complete tasks. The results suggested that the map features using the generated barrier-free schemes were easy to distinguish for people with color vision deficiency. In addition, we recruited twenty-eight students with normal color vision to read the maps, and the results suggested that the generated schemes are effective for people with normal color vision as well.

KEYWORDS:

• Barrier-free color
• color vision deficiency
• qualitative scheme
• map color
• map design
• visualization

Acknowledgments

The authors thank Mingguang Wu and Yunqi Zhang for their suggestions. The authors are also grateful for the valuable comments from the editor and the anonymous reviewers.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data and codes that support the findings of this study are available at datadryad.org under the identifier https://datadryad.org/stash/share/TyIAd8ig_YwyvvTdfQIZbmW-JrDJP_7n2222XfjIfE8.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grants [42271443 and 41571439], the Anhui Provincial Natural Science Foundation [2208085Y15], Top Scholar of Anhui Provincial Department of Education [gxbjZD2021079] and the Key Project of Natural Science Research of Anhui Provincial Department of Education under Grant [KJ2020A0720].