In the course NUTR-393, Data Visualization and Effective Communication, students create, analyze, and learn about the graphical representation of data and how the process of presenting findings must be evidence-based, efficient, clarifying, and ethical. A poorly-made visual with hidden motivations can easily skew findings and lead viewers into suggested conclusions instead of inviting them to ask good questions. As we conclude Black History Month in 2022, the following is a reflection from the course on one of a collection of famous hand-drawn infographics on African-American life in the 1900s created by W.E.B. Du Bois.
On its 120th anniversary, The Public Domain Review reprinted a famous data visualization created by William Edward Burghardt "W. E. B." Du Bois, the first African American to earn a doctorate at Harvard. The visual focused on economic life in Georgia in the 1900s and was based on an enormous amount of data collected by Du Bois and his students. The chart still possesses an enchanting power of aesthetic elegance, complexity, and curiosity.
We used this graph in the NUTR-393, Data Visualization and Effective Communication class to assess four visualization principles: evidence, efficiency, emphasis, and ethics. These principles are translated into four rules*:
- Evidence Rule: A logical path, context, underlying concepts, or results that form a graph should rest on solid evidence and facts. A graph should be created with a well thought out description in mind.
- Efficiency Rule: A graph should help to explain results or concepts by taking advantage of visual perception. A graph should utilize good visual properties.
- Emphasis Rule: A graph should be user-friendly, force the reader to note the unexpected, motivate questions, and clarify statements. A graph should be created with cognizance of the intended audience.
- Ethics Rule: A graph should promote essential values like accuracy, precision, reliability, integrity, and inclusiveness at neither excess nor deficiency, but at the mean of these two extremes. A graph should be created with awareness of its perception by the intended audience.
In class, we examined how these rules were observed in a visual to develop further appreciation for the produced work and suggest steps for improvement. Students commonly noted that the support for evidence was easier to establish than other criteria. Assessing the ethical aspects were more difficult, due to the general subjectivity of visual’s perception and interpretation. In this visual, we appreciated the sophistication and granularity of presented information on family incomes, averages, and ranges along with their interpretation in lay terms. We mused over itemized expenses like rent, clothes, leisure, health, and education, and how they were distributed across families with different incomes. We admired the clarity, organization, and insightful alignment of each expense category with income brackets aiding better understanding of how the contribution of each category changed. We examined the graph’s efficiency with clearly demarcated colors, concise labels at all levels, and supplemented photographs.
Upon deeper pursuit, we wished to know more: What was the size of families and how many people represented each income group? What was hidden behind the ‘Food’ category? What was collected by the Department of Agriculture and how was it presented in the Bulletin N71? Most importantly, we identified an eternal truth: basic necessities in 1900, like in 2022, account for the highest percentage of expenditure in lower income households. We noted that families earning $200-300 annually and no longer tax free were able to allocate the least to health and education. At the same time, for families in the two highest income brackets, the rent category was null.
We also discussed questions that have gained greater attention over the last decades: How were study participants selected, or informed about the study results, and how did they consent if they did? Was the survey implemented in a representative and equitable way? Who represented the intended audience? How did this type of work contribute to the fight for equal rights? The most rewarding experience in reviewing this graph was that, despite its age, the visual instilled a sense of relevance and wonder with a modern vibe.
*The four principles of analyzing the visuals were created by course professor Elena Naumova, relevant references to the exercise described in this course example are cited below:
- Zhou B, Liang S, Monahan KM, EI-Abbadi N, Cruz MS, Chen Y, DeVane A, Reedy J, Zhang J, Semenova I, Montoliu I, Mozaffarian D, Wang D, Naumova EN. An open access data platform: The Global Nutrition and Health Atlas (GNHA). Current Developments in Nutrition. 2022 (accepted Feb 22, 2022)
- Zhou B, Liang S, Monahan KM, Singh G, Simpson RB, Reedy J, Zhang J, DeVane A, Cruz MS, Marshak A, Mozaffarian D, Wang D, Semenova I, Montoliu I, Prozorovscaia D, Naumova EN. The Food and Nutrition Systems Dashboards: a Systematic Review. Advances in Nutrition. 2022 (accepted Feb 27, 2022)
- Naumova EN. Visual Analytics for Immunologists: Data Compression and Fractal Distributions. Self/Non-Self - Immune Recognition and Signaling. 2010.
Instructors: Elena Naumova, Corby Kummer, Ryan Simpson, Emily Sanchez
Class participants: Becket Harney, Lorna Tokos Harp, Rachel Kinney, Ann Suarez Mendoza, Brianna Lauren, Emma Laprise, Aarti Singh, Natalie Somers, Alessandra Strong, Jada Wensman