Origami plot: a novel multivariate data visualization tool that improves radar chart

Rui Duan; Jiayi Tong; Alex J Sutton; David A Asch; Haitao Chu; Christopher H Schmid; Yong Chen

doi:10.1016/j.jclinepi.2023.02.020

Origami plot: a novel multivariate data visualization tool that improves radar chart

J Clin Epidemiol. 2023 Apr:156:85-94. doi: 10.1016/j.jclinepi.2023.02.020. Epub 2023 Feb 21.

Authors

Rui Duan¹, Jiayi Tong², Alex J Sutton³, David A Asch⁴, Haitao Chu⁵, Christopher H Schmid⁶, Yong Chen⁷

Affiliations

¹ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: rduan@hsph.harvard.edu.
² Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
³ Department of Health Sciences, University of Leicester, Leicester, UK.
⁴ Division of General Internal Medicine, University of Pennsylvania, Philadelphia, PA, USA; Leonard Davis Institute of Health Economics, Philadelphia, PA, USA.
⁵ Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA; Statistical Research and Innovation, Global Biometrics and Data Management, Pfizer Inc., New York, NY, USA.
⁶ Department of Biostatistics, Brown University, Providence, RI, USA.
⁷ Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA; Leonard Davis Institute of Health Economics, Philadelphia, PA, USA. Electronic address: ychen123@upenn.edu.

Abstract

Objectives: We propose the origami plot, which maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multicriteria decision-making.

Study design and setting: Built upon a radar chart, the origami plot adds additional auxiliary axes and points such that the area of the connected region of all dots is invariant to the ordering of axes. As such, it enables ranking different individuals by the overall performance for multicriteria decision-making while maintaining the intuitive visual appeal of the radar chart. We develop extensions of the origami plot, including the weighted origami plot, which allows reweighting of each attribute to define the overall performance, and the pairwise origami plot, which highlights comparisons between two individuals.

Results: We illustrate the different versions of origami plots using the hospital compare database developed by the Centers for Medicare & Medicaid Services (CMS). The plot shows individual hospital's performance on mortality, readmission, complication, and infection, as well as patient experience and timely and effective care, as well as their overall performance across these metrics. The weighted origami plot allows weighing the attributes differently when some are more important than others. We illustrate the potential use of the pairwise origami plot in electronic health records (EHR) system to monitor five clinical measures (body mass index [BMI]), fasting glucose level, blood pressure, triglycerides, and low-density lipoprotein ([LDL] cholesterol) of a patient across multiple hospital visits.

Conclusion: The origami plot is a useful visualization tool to assist multicriteria decision making. It improves radar charts by avoiding potential misuse of the connected regions. It has several new features and allows flexible customization.

Keywords: Multivariate data; Polar chart; Radar chart; Ranking; Spider chart; Star chart; Visualization tool.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Benchmarking
Blood Pressure
Data Visualization*
Humans
Medicare
Radar*
United States

Abstract

Publication types

MeSH terms

Grants and funding