Computational assessment of the aesthetic quality of map colors

Abstract

Crafting beautiful map colors is challenging for not only experts but also novices, suggesting the need for an aesthetic quality assessment of map colors for the effective design of maps and visualizations. To fill this gap, we present a computational method to assess the aesthetic quality of map colors. First, we couple the idea of computational aesthetics with map aesthetic principles and identify four and two basic types of aesthetic features in terms of order and complexity, respectively. Then, we collect 2000 map samples and derive 149 aesthetic metrics by instantiating the above aesthetic features while considering the spatial weights and figure–ground organization of the map samples. We also recruit participants (N = 438) to rate the aesthetic quality of the map colors. Finally, we train an aesthetic predictor by fitting those aesthetic metrics with user ratings. The experimental results show that the proposed method can assess aesthetic quality of map colors with high accuracy (R2 = 0.73 on the training set and R2 = 0.65 on the validation set). We also explore the dominant aesthetic metrics that positively and negatively influence aesthetic appreciation, as well as those metrics that have no significant influence. This work offers a portable and flexible aesthetic quality assessment approach for map colors and can be further improved by considering complex symbols, spatial structures, and color–semantic and color–emotion associations.