Situated Visualization in Motion
| dc.contributor.author | Yao, Lijie | |
| dc.date.accessioned | 2025-01-09T08:10:41Z | |
| dc.date.available | 2025-01-09T08:10:41Z | |
| dc.date.issued | 2023-12-18 | |
| dc.description.abstract | In my thesis, I define visualization in motion and make several contributions to how to visualize and design situated visualizations in motion. In situated data visualization, the data is directly visualized near their data referent, i.e., the physical space, object, or person it refers to. Situated visualizations are often useful in contexts where the data referent or the viewer does not remain stationary but is in relative motion. For example, a runner is looking at visualizations from their fitness band while running or from a public display as they are passing it by. Reading visualizations in such scenarios might be impacted by motion factors. As such, understanding how to best design visualizations for dynamic contexts is important. That is, effective and visually stable situated data encodings need to be defined and studied when motion factors are involved. As such, I first define visualization in motion as visual data representations used in contexts that exhibit relative motion between a viewer and an entire visualization. I classify visualization in motion into 3 categories: (a) moving viewer & stationary visualization, (b) moving visualization & stationary viewer, and (c) moving viewer & moving visualization. To analyze the opportunities and challenges of designing visualization in motion, I propose a research agenda. To explore to what extent viewers can accurately read visualization in motion, I conduct a series of empirical perception studies on magnitude proportion estimation. My results show that people can get reliable information from visualization in motion, even if at high speed and under irregular trajectories. Based on my perception results, I move toward answering the question of how to design and embed visualization in motion in real contexts. I pick up swimming as an application scenario because swimming has rich, dynamic data. I implement a technology probe that allows users to embed visualizations in motion in a live swimming video. Users can adjust in real-time visual encoding parameters, the movement status, and the situatedness of visualization. The visualizations encode real swimming race-related data. My evaluation with designers confirms that designing visualizations in motion requires more than what traditional visualization toolkits provide: the visualization needs to be placed in-context (e.g., its data referent, its background) but also needs to be previewed under its real movement. The full context with motion effects can affect design decisions. After that, I continue my work to understand the impact of the context on the design of visualizations in motion and its user experience. I select video games as my test platform, in which visualizations in motion are placed in a busy, dynamic background but need to help players make quick decisions to win. My study shows there are trade-offs between visualization's readability under motion and aesthetics. Participants seek a balance between the readability of visualization, the aesthetic fitting to the context, the immersion experience the visualization brings, the support the visualization can provide for a win, and the harmony between the visualization and its context. | |
| dc.description.sponsorship | Agence Nationale de la Recherche (ANR). Grant number: ANR-19-CE33-0012. | |
| dc.identifier.citation | Lijie Yao. Situated Visualization in Motion. Human-Computer Interaction [cs.HC]. Université Paris-Saclay, 2023. English. NNT : 2023UPASG093. | |
| dc.identifier.other | https://theses.hal.science/tel-04413122 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/3607101 | |
| dc.language.iso | en | |
| dc.publisher | Université Paris-Saclay | |
| dc.title | Situated Visualization in Motion | |
| dc.title.alternative | Visualisation localisée en mouvement | |
| dc.type | Thesis |
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