Cast: Authoring data-driven chart animations

Abstract

We present CAST, an authoring tool that enables the interactive creation of chart animations. It introduces the visual specifcation of chart animations consisting of keyframes that can be played sequentially or simultaneously, and animation parameters (e.g., duration, delay). Building on Canis [19], a declarative chart animation grammar that leverages data-enriched SVG charts, CAST supports auto-completion for constructing both keyframes and keyframe sequences. It also enables users to refne the animation specifcation (e.g., aligning keyframes across tracks to play them together, adjusting delay) with direct manipulation and other parameters for animation efects (e.g., animation type, easing function) using a control panel. In addition to describing how CAST infers recommendations for auto-completion, we present a gallery of examples to demonstrate the expressiveness of CAST and a user study to verify its learnability and usability. Finally, we discuss the limitations and potentials of CAST as well as directions for future research.