Contextual Visualization: Combating Selection Bias During Visual Analysis

We are designing, developing, and evaluating a set of Contextual Visualization Methods for exploratory data analysis which are designed to support the discovery of more robust and generalizable insights from high-dimensional data. More specifically, this project is exploring new techniques for detecting, communicating, and reducing the impact of selection bias and other threats to validity which can arise during interactive visualization-based data analysis.

This is part of a multi-year effort made possible by a grant from the National Science Foundation (NSF) under Grant No. 1704018. (Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.)

A more comprehensive summary the project and regular updates can be found on the project’s official website.

Open-Source Software

This project has led to the release of three open source repositories on GitHub:

Cadence Exploratory Visual Analytics: https://github.com/VACLab/CadenceEVA
Periphery Plots: https://github.com/PrecisionVISSTA/PeripheryPlots
Inline Replication: https://github.com/VACLab/ir

Publications

David Borland, Wenyuan Wang, Jonathan Zhang, Joshua Shrestha, David Gotz. Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data. To appear in IEEE TVCG and IEEE VAST 2019.
[Preprint available on arXiv: arXiv:1906.07625]
[Video figure available by clicking here.]

David Gotz, Jonathan Zhang, Wenyuan Wang, Joshua Shrestha, David Borland. Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation. To appear in IEEE TVCG and IEEE VAST 2019.
[Preprint available on arXiv: arXiv:1906.07617]
[Video figure available by clicking here.]

Bryce Morrow, Trevor Manz, Arlene E. Chung, Nils Gehlenborg, David Gotz. Periphery Plots for Contextualizing Heterogeneous Time-Based Charts. To appear in IEEE VIS 2019 Short Papers.
[Preprint available on arXiv: arXiv:1906.07637]
[Video figure available by clicking here.]

Jonathan Zhang, David Borland, Wenyuan Wen, Jonathan Shrestha, David Gotz. Dynamic Hierarchical Aggregation, Selection Bias Tracking, and Detailed Subset Comparison for High-Dimensional Event Sequence Data. Under review for publication.

David Gotz, Wenyuan Wang, Annie T. Chen, David Borland. Visualization Model Validation via Inline Replication. Information Visualization (Published online ahead of print: January 25, 2019).
[A preprint of this article is available by clicking here. This article is also available through the journal’s website.]

David Borland, Wenyuan Wang, David Gotz. Contextual Visualization: Making the Unseen Visible to Combat Bias During Visual Analysis. IEEE Computer Graphics and Applications (Volume 38, Issue 6, 2018).
[A preprint of this article is available by clicking here. This article is also available through the journal’s website.]

Yufei Zhang, David Borland, David Gotz. Increasing Understanding of Survey Re-Weighting with Visualization. IEEE VIS Posters, Berlin, Germany (2018).
[PDF, 770k]

David Borland and David Gotz. Dual View: Multivariate Visualization Using Linked Layouts of Objects and Dimensions. IEEE VIS Posters, Berlin, Germany (2018).
[PDF, 381k]

David Gotz, Shun Sun, Nan Cao, Rita Kundu, and Anne-Marie Meyer. Adaptive Contextualization Methods for Combating Selection Bias During High-Dimensional Visualization. ACM Transactions on Interactive Intelligent Systems (Volume 7, Issue 4, 2017).
[This article is available through the journal’s website. View the article by clicking here.]