A learning-based approach to automatically evaluate the quality of sequential color schemes for maps

ABSTRACT

Color quality evaluation is key to judging map quality, which can improve data visualization and communication. However, most existing methods for evaluating map colors are tedious and subjective manual methods. In this paper, we study sequential color schemes, a widely used map color type and propose a learning-based approach for evaluating the color quality. The approach consists of two steps. First, we extract and characterize the cartographic factors for determining the quality of sequential color schemes, such as color order, color match, color harmony, color discrimination and color uniformity. Second, we present a model to predict the color quality based on AdaBoost, a type of ensemble learning algorithm with excellent classification performance and use these factors as input data. We conduct a case study based on 781 samples and train the AdaBoost-based model to predict the quality of sequential color schemes. To evaluate the model’s performance, we calculated the area under the receiver operating characteristic (ROC) curve (AUC). The AUC values are 0.983 and 0.977 on the training data and testing data, respectively. These results indicate that the proposed approach can be used to automatically evaluate the quality of sequential color schemes for maps, which helps mapmakers select good colors.

KEYWORDS:

• Map color
• sequential color scheme
• color quality
• machine learning

Acknowledgments

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

Data and code 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/l4_qcRccIRMU1AWILn3jHLznDkun5ugcXGva2IwcdLw

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant [41571439], the Key Project of Natural Science Research of Anhui Provincial Department of Education under Grant [KJ2020A0720], the Key Project of Research and Development in Chuzhou Science and Technology Program under Grant [2020ZG016] and the Natural Science Foundation of Anhui Province under Grant [2008085QD168].