Streamgraph Generation with Enhanced Readability

Abstract

<p indent=0mm>In order to enhance the readability of the streamgraph and assist users identify the target layer more efficiently, a method to optimize the color assignment and label layout in streamgraph based on the existing layout of streamgraph is proposed. First, the color assignment evaluation function of the streamgraph is established according to the layout, and then the color assignment result is got which can maximize the color difference between layers as well as the luminance difference between the background color and layers through genetic algorithm. Secondly, the sliding window is used to traverse each layer, and the tilt angle of each label is automatically adjusted by least squares to fit the shape change of each layer. Then the label layout results are obtained with adaptive label size. The method is compared with existing methods on multiple real and simulated datasets through user study and quantitative evaluation. The results show that the method can significantly improve the recognition accuracy, reduce the recognition time of the layers, and increase the label size at the same time, which means it can improve the readability of the streamgraph effectively.