# Data is Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvania

arXiv: https://arxiv.org/abs/1901.01920 · HTML: https://mlsanity.com/paper/1901.01920/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs](https://mlsanity.com/paper/2101.09824/) (2021)
- [Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts](https://mlsanity.com/paper/2505.17374/) (2025)
- [Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention](https://mlsanity.com/paper/2508.03713/) (2025)
- ["Trust Junk" Leads to Unjustified Support for Highly Discriminatory Predictive Models](https://mlsanity.com/paper/2607.14152/) (2026)

## 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/