Toward barrier-free cartography: a similarity and discrimination-based approach to automatically enhance qualitative color schemes for color vision deficiency

Qualitative color schemes are often designed without considering people with color vision deficiency, causing confusion when reading maps. Thus, this paper proposes an automatic approach to enhancing existing qualitative color schemes for color vision deficiency. We built an optimization model by combining color similarity and discrimination to obtain enhanced color schemes similar to the original schemes that are distinguishable by people with color vision deficiency. We provide a case study adopting the Tianjin metro map and the Liaoning land cover map as experimental maps. Thirty-two participants with color vision deficiency were invited to complete tasks on the original and enhanced experimental maps. The results showed that participants with color vision deficiency achieved significantly greater accuracy and shorter response times on the enhanced experimental maps than on the original maps. In addition, 47 participants with color-normal vision were invited to participate; the results showed that they also achieved high accuracy and shorter response times on enhanced experimental maps. This suggests that the enhanced color schemes generated using our approach are barrier-free schemes that meet the map-reading requirements for color vision deficiency and normal color vision.