VisCollage: Annotative Collages for Organizing Data Event Charts

Xiao-Han Li, Yi-Ting Hung, Jia-Yu Pan, Wen-Chieh Lin

Abstract: While existing visualization systems excel in exploring datasets and discovering data patterns and insights, challenges remain in automatically generating infographics from exploration-derived visualizations. We propose VisCollage, a computational pipeline that automatically organizes and renders charts from an exploration in a “visual collage”, which is inspired by data journalism and can be viewed as a kind of “partitioned poster infographic”. By analyzing the relation (e.g., drill-down or comparison) between charts established during exploration, VisCollage groups and merges them to reduce data redundancy. In addition, VisCollage automatically identifies a main chart of the exploration and arranges annotations and background charts around it. User studies evaluated from the perspectives of creators, professional data journalists, and general readers indicate that our system assists creators in generating satisfactory visualization summaries of data events, enables the general audience to extract insights from the data through visual collages, and are well received by professionals.