Enhancing Data-driven Storytelling with Animated Visualization

PhD Thesis Proposal Defence


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

by

Miss Xinhuan SHU


Abstract:

In today's data-driven world, visual data storytelling that communicates 
insights backed by data and illustrated with visualization is enjoying 
increasing popularity. With the proliferation of various data stories, there is 
a recent surge of interest in enhancing storytelling with animated 
visualizations that attract and engage a broad audience and elevate stories 
beyond static narration. However, in spite of the growing use and desirable 
properties, there is still little understanding of what characterizes animated 
data stories and what makes them understandable. In addition, prior research 
has rarely investigated the effective and easy way to generate and author 
animated data stories for general users.

This proposal 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, with a particular focus on a short and concise form of animated 
visualization named data-GIFs. GIFs embed simple visual messages in short 
animations that usually last less than 15 seconds and are played in automatic 
repetition. Informed by a corpus of real-world data-GIFs, we summarize a 
structured design space and propose a list of design suggestions for creating 
more effective data-GIFs through semi-structured interviews and an extensive 
online study. The second work takes a step further to investigate how to 
support authoring and develop a lightweight authoring tool for data-GIFs, 
namely, DataGifify. Inspired by short video effects and editing 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, scaffolding and accelerating the 
creation process for data-GIFs.  Finally, we briefly present our ongoing work, 
exploring animated word clouds to tell stories, by introducing the research 
problem and the preliminary design space summarized through an exploratory 
study.


Date:			Thursday, 3 June 2021

Time:                  	2:00pm - 4:00pm

Zoom Meeting: 
https://hkust.zoom.com.cn/j/99254388953?pwd=VG85aURCMnZGVFprNFNBcjA4OHIrUT09

Committee Members:	Prof. Huamin Qu (Supervisor)
 			Dr. Xiaojuan Ma (Chairperson)
 			Prof. Andrew Horner
 			Prof. Chiew-Lan Tai


**** ALL are Welcome ****