Data is Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvania
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Abstract (arXiv)
Many of the guidelines that inform how designers create data visualizations originate in studies that unintentionally exclude populations that are most likely to be among the 'data poor'. In this paper, we explore which factors may drive attention and trust in rural populations with diverse economic and educational backgrounds - a segment that is largely underrepresented in the data visualization literature. In 42 semi-structured interviews in rural Pennsylvania (USA), we find that a complex set of factors intermix to inform attitudes and perceptions about data visualization - including educational background, political affiliation, and personal experience. The data and materials for this research can be found at https://osf.io/uxwts/
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Jan 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
- 2025Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Chartscited by 3
- 2025Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attentioncited by 1
- 2026"Trust Junk" Leads to Unjustified Support for Highly Discriminatory Predictive Modelscited by 0
- 2026"Trust Junk" Leads to Unjustified Support for Highly Discriminatory Predictive Models14 Jul 2026
- 2025Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention22 Jul 2025
- 2025Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts23 May 2025
- 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs24 Jan 2021