Sticky Links: Encoding Quantitative Data of Graph Edges
PacificVisTVCGIEEE Transactions on Visualization and Computer Graphics, 2024.
[ Paper (0.4 MB)]
[ Source Code (Python)]
[ My Talk at PacificVis 2024 (32.4 MB)]
In this work we propose a new edge drawing for node-link diagram, Sticky Links. Using the metaphor of stickiness, sticky links can be used for expressive edge quantatitive encoding.
Enhancing Static Charts with Data-driven Animations
TVCGIEEE Transactions on Visualization and Computer Graphics, 2022.
[ Paper (1.82 MB)]
[ Project Homepage ]
[ Source Code (Javascript)]
In this work we propose data-driven animation to bring static charts to life, with the purpose of encoding and emphasizing certain attributes of data, specifically of non-temporal data.
Modeling Just Noticeable Differences in Charts
InfoVisTVCGIEEE Transactions on Visualization and Computer Graphics, 2021.
[ Paper (2.1 MB)]
[ Experiment Data ]
[ My Talk at VIS 2021 ]
In this work, we model the perception of Just Noticeable Differences (JNDs) in charts with two variables (intensity and distance) and find that JND grows as the exponential function.
Exploring Visual Information Flows in Infographics
CHI, Proceedings of the 2020 CHI conference on human factors in computing systems, 2020.
[ Paper (2.0 MB) ]
[ InfoVIF dataset ]
[ Demo Video (MP4, 21.1 MB) ]
[ My Talk at CHI 2020 ]
In this work, we propose Visual Information Flow (VIF) in infographics and present an framework to extract it in a wild dataset InfoVIF, by which the VIF design patterns are explored.
Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots
InfoVisTVCGIEEE Transactions on Visualization and Computer Graphics, 2019.
[ Paper (2.7 MB) ]
[Source Code (Python) ]
[ My Talk at VIS 2019 ]
In this work, we present Winglets, which are designed as a pair of dual-sided strokes, leveraging the Gestalt principle of Closure to shape the perception of the form of the clusters.