Understanding and Automating Graphical Annotations on Animated Scatterplots

Danqing Shi*, Antti Oulasvirta, Tino Weinkauf, Nan Cao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


Scatterplots are commonly used in various contexts, from scientific publications to infographics for the general public. However, not everyone is able to read them, and even experts may struggle to notice some important information such as overlapping clusters or temporal changes. To address these issues, a computational approach for annotating scatterplots has been developed. This approach involves various forms of annotation, including drawing lines to show correlations, circling areas to show clusters, and indicating movement with arrows. The approach is based on a study that identified common annotation strategies used by people to annotate scatterplots. These strategies are distilled into an automated method for generating graphical annotations on scatterplots. The method involves a problem formulation using a Markov Decision Process and a model for making annotation decisions. The model generates step-by-step graphical annotations by analyzing data insights and observing the chart. The final result conveys a narrative that is easy to understand and allows for the conveyance of temporal changes in the data. The study results suggest that the method can generate understandable and functional annotations that are comparable to those created by human experts. This approach can potentially reduce the time and effort required to read scatterplots, making it a useful tool for data visualization novices.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE 17th Pacific Visualization Conference, PacificVis 2024
PublisherIEEE Computer Society
Number of pages10
ISBN (Electronic)979-8-3503-9380-4
Publication statusPublished - 2024
MoE publication typeA4 Conference publication
EventIEEE Pacific Visualization Symposium - Tokyo, Japan
Duration: 23 Apr 202426 Apr 2024

Publication series

NameIEEE Pacific Visualization Symposium
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773


ConferenceIEEE Pacific Visualization Symposium
Abbreviated titlePacificVis


  • Annotated Visualization
  • Scatterplot


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