Peter Hall’s Taxonomy
A Response to Data Visualization Categories: Artistic, Journalistic, and Scientific.
My earliest exposure to data visualization design derived from reading Tufteâ€™s The Visual Display of Quantitative Information and Beautiful Evidence. Tufte provides objective, analytical claims for why certain types of data visualizations are more effective than other types. Josef MÃ¼eller-Brockmannâ€™s minimalist, Swiss International design style was my earliest influence with understanding the relationship between chartjunk and the reason for stripping down decorative ornamentation in favor of clear, concise, and concrete presentation. As Tufte claims, â€œEffective visual models (and cognitive graphics) that incorporate strong design principles can transcend time, culture and languageâ€ (Tufte 10).
Peter Hallâ€™s video segment (16:56) explains the scientific category of his taxonomy and how the human brain recognizes color before shape when comparing charts A and B. This recalls Fred Dretskeâ€™s presentation of What We See, from UX theory. Dretske states that â€œthe facts that we see are based on the object and the properties of that object,â€ (Dretske). So, I can now better understand how the relationship of Dretskeâ€™s philosophy of observation coincides with Hallâ€™s taxonomy.
I recall past design colleagues wanting to explore the relationship between graphic design integrity and the lack of visual interest with data visualizations in science, health, and mathematics. Subjectively, when considering Hallâ€™s three areas, I like to think that there have been a few leading designers and artists that have been effectively integrating Hallâ€™s taxonomy.
Since 2010, Iâ€™ve been intrigued with the ingenuity of Aaron Koblin, whom has consistently integrated scientific, journalistic and artistic integrity into his work. Prior to Koblin, I was drawn to the aesthetic styles of Nick Felton and Cristiana Couceiro. Whereas Couceiroâ€™s work leans in favor of art collage rather than data-specific presentation, she remains a favorite based upon composition, color choice and aesthetic style.
As for my own needs, Iâ€™m eager to learn more about the progression of data visualization, since itâ€™s an area that Iâ€™m somewhat new to, with respect to knowing how to effectively design data visualizations per category. In terms of generating large quantities of data, although I’m certified as an intermediate level in Processing, I’d also like to research or know more about which software is favored in our industryâ€¦ and perhaps, I will find out soon enough!
Questions and ideas to ponder:
1. How often does individual bias influence which visual models are the most effective?
2. Can visual models be combined?
3. Which topics have been the most widely chosen when designing data visualizations?
4. Some past and present ideas worth exploring: Gun Violence, Environmental Issues, Food and Water Shortage, The Causes of Poverty, Global Financial Crisis, Fracking, or Technological Distractions in the Modern Era.