Roslingifier: Semi-Automated Storytelling for Animated Scatterplots

Abstract

We present Roslingifier, a data-driven storytelling method for animated scatterplots. Like its namesake, Hans Rosling (1948--2017), a professor of public health and a spellbinding public speaker, Roslingifier turns a sequence of entities changing over time---such as countries and continents with their demographic data---into an engaging narrative telling the story of the data. From an in-depth analysis of public speakers using data visualization, we derive three specific techniques to achieve this: natural language narratives, visual effects that indicate the entities, and temporal branching that changes playback time of the animation. Our implementation of the Roslingifier method is capable of analyzing any given temporal dataset, identifying and clustering significant regions, automatically generating visual highlighting and a narrative for playback, and enabling the user to customize. In two user studies and an interview with three domain experts, we show that Roslingifier allows users to effectively create engaging data stories.