An Image-based Typology for Visualization
<|reference_start|>An Image-based Typology for Visualization: We present and discuss the results of a qualitative analysis of visual representations from images. We labeled each image's essential stimuli, the removal of which would render a visualization uninterpretable. As a result, we derive a typology of 10 visualization types of defined groups. We describe the typology derivation process in which we engaged. The resulting typology and image analysis can serve a number of purposes: enabling researchers to study the evolution of the community and its research output over time, facilitating the categorization of visualization images for the purpose of research and teaching, allowing researchers and practitioners to identify visual design styles to further align the quantification of any visual information processor, be that a person or an algorithm observer, and it facilitates a discussion of standardization in visualization. In addition to the visualization typology from images, we provide a dataset of 6,833 tagged images and an online tool that can be used to explore and analyze the large set of labeled images. The tool and data set enable scholars to closely examine the diverse visual designs used and how they are published and communicated in our community. A pre-registration, a free copy of this paper, and all supplemental materials are available via osf.io/dxjwt.<|reference_end|>
@article{chen2024an,
    title={An Image-based Typology for Visualization},
    author={Jian Chen and Petra Isenberg and Robert S. Laramee and Tobias Isenberg
  and Michael Sedlmair and Torsten Moeller and Rui Li},
    journal={arXiv preprint arXiv:2403.05594},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05594},
    primaryClass={cs.HC cs.CV cs.GR}
}