TY - GEN
T1 - Automatic Generation of Unit Visualization-based Scrollytelling for Impromptu Data Facts Delivery
AU - Lu, Junhua
AU - Chen, Wei
AU - Ye, Hui
AU - Wang, Jie
AU - Mei, Honghui
AU - Gu, Yuhui
AU - Wu, Yingcai
AU - Zhang, Xiaolong Luke
AU - Ma, Kwan Liu
N1 - Funding Information:
The work was supported by National Natural Science Foundation of China (61772456, 61761136020).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - Data-driven scrollytelling has become a prevalent way of visual communication because of its comprehensive delivery of perspectives derived from the data. However, creating an expressive scrollytelling story requires both data and design literacy and is time-consuming. As a result, scrollytelling has been mainly used only by professional journalists to disseminate opinions. In this paper, we present an automatic method to generate expressive scrollytelling visualization, which can present easy-to-understand data facts through a carefully arranged sequence of views. The method first enumerates data facts of a given dataset, and scores and organizes them. The facts are further assembled, sequenced into a story, with reader input taken into consideration. Finally, visual graphs, transitions, and text descriptions are generated to synthesize the scrollytelling visualization. In this way, non-professionals can easily explore and share interesting perspectives from selected data attributes and fact types. We demonstrate the effectiveness and usability of our method through both use cases and an in-lab user study.
AB - Data-driven scrollytelling has become a prevalent way of visual communication because of its comprehensive delivery of perspectives derived from the data. However, creating an expressive scrollytelling story requires both data and design literacy and is time-consuming. As a result, scrollytelling has been mainly used only by professional journalists to disseminate opinions. In this paper, we present an automatic method to generate expressive scrollytelling visualization, which can present easy-to-understand data facts through a carefully arranged sequence of views. The method first enumerates data facts of a given dataset, and scores and organizes them. The facts are further assembled, sequenced into a story, with reader input taken into consideration. Finally, visual graphs, transitions, and text descriptions are generated to synthesize the scrollytelling visualization. In this way, non-professionals can easily explore and share interesting perspectives from selected data attributes and fact types. We demonstrate the effectiveness and usability of our method through both use cases and an in-lab user study.
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U2 - 10.1109/PacificVis52677.2021.00011
DO - 10.1109/PacificVis52677.2021.00011
M3 - Conference contribution
AN - SCOPUS:85107393760
T3 - IEEE Pacific Visualization Symposium
SP - 21
EP - 30
BT - Proceedings - 2021 IEEE 14th Pacific Visualization Symposium, PacificVis 2021
PB - IEEE Computer Society
T2 - 14th IEEE Pacific Visualization Symposium, PacificVis 2021
Y2 - 19 April 2021 through 22 April 2021
ER -