Home » Community Learning and Practice » RDMLA Symposium 2022 » Poster Presentations » Patterns of Community-based Data in the U.S. State-level COVID-19 Dashboards

Zhan Hu | Doctoral Student, School of Library and Information Science, Simmons University

Originally from China, Zhan Hu has been a Boston resident for the past ten years. She graduated from UMass Boston with a bachelor’s degree in Economics and Finance. After completing the graduate program in Economics in Tufts University, Zhan switched to the Library and Information Science field to pursue her interests in information/knowledge management research. She entered the Doctoral program in SLIS upon finishing the MLIS program in Simmons.

Zhan’s primary research interests are information ecology and ecological approach to investigate information seeking behaviors, user experience research, and knowledge management. She is currently working on her dissertation work, focusing on parents' health information seeking and decision making during the COVID-19 pandemic.

Yishan Zhang | Doctoral Student, School of Library and Information Science, Simmons University

Yishan Zhang is a PhD student in the School of Library and Information Science, Simmons University. He earned a Bachelor’s degree in Information Management and Information System and another Bachelor’s degree in Economics from Peking University. He also holds a master’s degree in Information Science from Peking University. Since 2013, Yishan has been working in the field of government information management. He has participated in several projects of open government data and data-sharing in Beijing. His research interests include open government data, data-sharing activities among different institutions, and the digital divide.

Rong Tang, Ph.D. | Professor, School of Library and Information Science, Simmons University

Rong Tang received her doctorate from the School of Information and Library Science, University of North Carolina Chapel Hill. She has taught evaluation of information services, technology for information professionals, library automation systems, digital information services and providers, research methods and design, and usability and user experience research. Her research areas span from users’ relevance judgment, user needs assessments, user information behavior, citation analysis, collaborative information seeking, eye-tracking studies, to MOOCs in Asia.

Rong is the founding and current Director of Simmons Usability Lab. She is 2021 Simmons University Excellence in Research Awardee. Her co-author paper was one of the top cited papers on JASIST. She received an IMLS grant in 2019 for retooling librarians to become interprofessional informationists.

Rong believes in building connections with students, and she is committed to working with students to find an intellectual point where they “click” — the point for which they get lifelong inspiration and passion for information work.

Presentation Abstract

During the COVID-19 pandemic, within the US, various states had produced dashboards tracking COVID-19 development and vaccination progress. As indicated by Shah (2020), "Covid-19 has shown the importance of dashboards and visualization for everyone. Various countries and even states/regions have created their own COVID-19 dashboards to educate people on the pandemic." Over the past two years, a number of research studies investigated usage habits (e.g., Röddiger et al., 2020) and user experience with map-based interaction with the COVID-19 dashboard visualizations (e.g., Cay et al., 2020). However, seldom has any study focused on the characteristics of demographic data or other community-oriented information on dashboards, whether the grouping of demographics is consistent across all dashboards, and the extent to which community-based data grouping is available on dashboards. Specifically related to data visualization and demographic data, Atherton (2021) warns that “Incomplete demographic data obscures the virus’s full impact on marginalized communities. Without more information about who the virus is affecting and how we cannot protect our most vulnerable” (p. 80). Atherton found that CDC’s early data listed race as “missing/unspecified” at 78% of the cases. The author further points out that the “disconnects between reported datasets and data visualizations in public-facing COVID health and science communication” are seriously problematic (p. 80).

The objective of this study is to examine the presence of demographic data and other community-based data and their grouping in dashboard visualization. The second goal of the study is to identify and reveal potential gaps in state-level dashboards when presenting the COVID-19-related datasets.

For our data collection, we first identified the state-level COVID-19 data dashboard/portal of the 50 states in the US, plus the District of Columbia. The three researchers reviewed and documented the dashboards regarding patterns of data, focusing on demographic data such as age groups, sex/gender, race/ethnicity, as well as other kinds of data available on dashboards including medical conditions, high risk, or residence/clusters.

Preliminary results showed that all 51 dashboards featured data analysis and presentation by different “age groups”, though specifics of groupings vary. 47 (92.16%) dashboards had breakdowns of data by different groupings of Sex/Gender, and 49 (96.08%) dashboards had Race/Ethnicity groupings. A number of dashboards featured other types of groupings. Three states -- Arizona, Arkansas, and Louisiana included the grouping of people with chronic medical conditions or underlying health concerns. Another three dashboards: Kansas, Massachusetts, and Minnesota featured the clusters grouping of the facilities or residential areas such as “Bar or Restaurant,” “College or University,” “Corrections,” “Daycare,” “Government,” “Group Living,” “Long term care facility,” and more. In addition, Minnesota’s COVID-19 data dashboard also looked at how unemployment, worker characteristics, and group setting influence the case numbers. Ohio’s dashboard featured COVID-19 equity analysis by incorporating the calculation of the Ohio Opportunity Index. Oregon’s dashboard adopted CDC’s Social Vulnerability Index in their data visualization and also grouped data by types of disability and language.

While all dashboards included some level of demographic data, there is notable inconsistency in the groupings related to age group. It is not clear why there are different kinds of grouping in age. With regard to sex/gender grouping, the predominant grouping method is the binary “female/male,” with only one dashboard (Massachusetts) including a “transgender” category and another (Oregon) included a “non-binary” category. This reveals that for the sex/gender grouping, most of the states were very conservative and need to be updated with more current grouping. For ethics/race grouping, some dashboards had very simple grouping combining ethnicity and race. For instance, New Jersey and New York had the grouping of “Black, White, Asian, Hispanic, Other,” while others have separated race and ethnicity.

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Run Time: 6:36 | Transcript