Enhancing Data-driven Storytelling with Animated Visualization

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering

PhD Thesis Defence

Title: "Enhancing Data-driven Storytelling with Animated Visualization"


Miss Xinhuan SHU


In today's data-driven world, visual data storytelling is enjoying 
increasing popularity, which communicates insights backed by data and 
illustrated with visualization. Among a variety of data stories, there is 
a recent surge of interest in enhancing storytelling with animated 
visualizations, which attracts a broad audience and elevates stories 
beyond static narration. However, despite the growing use and desirable 
properties, there is still little understanding of what designs are 
featured in animated data stories and what makes them understandable. 
Prior research has rarely investigated the effective way to create 
animated data stories for general users.

This thesis aims to explore the design space and guidance for animated 
data stories, and support the authoring of narrative visualization with 
short animations. The first work investigates design patterns in animated 
data stories, particularly focusing on a short and concise form named 
data-GIFs. GIFs embed simple visual messages in short animations that 
usually last less than 15 seconds and are played in automatic repetition. 
A structured design space and a list of design suggestions are summarized 
by a systematic review of real-world data-GIFs and two user studies. The 
second work takes a step further to investigate how to support authoring 
of data-GIFs, and propose a lightweight authoring tool for tabular data, 
namely, DataGifify. Inspired by short video effects and tools, DataGifify 
leverages the design space of data-GIFs and applies narrative effects to 
animated visualization. It follows a preview-based authoring paradigm and 
integrates a step-wise interface in the creation process. Finally, the 
third work explores the authoring tool for text data, and presents 
DancingWords that allows users to create animated word clouds with 
story-oriented interactions and automatic layouts. The galleries of 
generated examples and feedback from user studies demonstrate 
the expressiveness and usefulness of the proposed tools.

Date:			Thursday, 12 August 2021

Time:			3:00pm - 5:00pm

Zoom Meeting:		https://hkust.zoom.us/j/3941396622

Chairperson:		Prof. Hong Kam LO (CIVL)

Committee Members:	Prof. Huamin QU (Supervisor)
 			Prof. Hao CHEN
 			Prof. Cunsheng DING
 			Prof. Kai TANG (MAE)
 			Prof. Jianhua ZHU (CityU)

**** ALL are Welcome ****