Anchoring and Alignment: Data Factors in Part-to-Whole Visualization
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914259646021632 |
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| author | Bailey, Connor Gleicher, Michael |
| author_facet | Bailey, Connor Gleicher, Michael |
| contents | We explore the effects of data and design considerations through the example case of part-to-whole data relationships. Standard part-to-whole representations like pie charts and stacked bar charts make the relationships of parts to the whole explicit. Value estimation in these charts benefits from two perceptual mechanisms: anchoring, where the value is close to a reference value with an easily recognized shape, and alignment where the beginning or end of the shape is aligned with a marker. In an online study, we explore how data and design factors such as value, position, and encoding together impact these effects in making estimations in part-to-whole charts. The results show how salient values and alignment to positions on a scale affect task performance. This demonstrates the need for informed visualization design based around how data properties and design factors affect perceptual mechanisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01881 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anchoring and Alignment: Data Factors in Part-to-Whole Visualization Bailey, Connor Gleicher, Michael Human-Computer Interaction We explore the effects of data and design considerations through the example case of part-to-whole data relationships. Standard part-to-whole representations like pie charts and stacked bar charts make the relationships of parts to the whole explicit. Value estimation in these charts benefits from two perceptual mechanisms: anchoring, where the value is close to a reference value with an easily recognized shape, and alignment where the beginning or end of the shape is aligned with a marker. In an online study, we explore how data and design factors such as value, position, and encoding together impact these effects in making estimations in part-to-whole charts. The results show how salient values and alignment to positions on a scale affect task performance. This demonstrates the need for informed visualization design based around how data properties and design factors affect perceptual mechanisms. |
| title | Anchoring and Alignment: Data Factors in Part-to-Whole Visualization |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.01881 |