Having just researched and written about Edward Tufte's Beautiful Evidence I have an immediate sense of relief reading the opening paragraph of this essay. This statement, particularly, resonated with me - "The graphs you make are meant to be looked at by someone. The effectiveness of any particular graph is not just a matter of how it looks in the abstract, but also a question of who is looking at it, and why." This is a concept Tufte largely ignores in the reading I've done. There seems to be resistance from Tufte and Healy about creating illustrative, or high ink to data, visualizations. Even when those images are more easily recalled by readers than more spare visualizations.
I appreciate the author's emphasis on critical thinking over pre-scripted design patterns.
I'm reminded by the potential, intentional or inadvertent, of data visualizations to distort reality or express a bias. This could be related to data or the presentation layer of a visualization.
I find the 'bad taste' argument to be problematic. Who are the taste-makers? The creators of data visualizations. Why isn't the consumer's needs and taste taken into account? There are plenty of designs that exist in the world that don't match my personal taste but that doesn't invalidate their appeal for others.
Legibility, accuracy and comprehension seem to be the core underpinnings of successful data visualizations. My concern about the bad-taste criteria isn't an argument for misleading fluff but rather a hesitation to accept a narrow concept of what tools should be considered. There is something to be said for designer to the audience expectations. If a client loves to see 3D bar charts, why not give them what they desire to facilitate a positive outcome for a meeting? NFL and NBA graphics come to mind. I find them overly done but they appeal to the audience and seem to convey information effectively enough.
The section on the relationship between color and shape is fascinating. similarly the section on Poisson and Matérn models. The comparison section referencing William S. Cleveland and Robert McGill study struck a chord. Particularly the aspect that the further one got from comparison the less accurate was the comprehension of the data. With this in mind it matches the association I have with more complex data visualizations with a degree of elitism. Yes, they may be clearer on some level but perhaps as a cost of legibility. It's the humanist in me. Not all designs are meant for all audiences but design should take into account the audience, their level of patience and aptitudes for understanding the language laid before them.
The following principles are also used in UX/UX design:
- Proximity: Things that are spatially near to one another seem to be related.
- Similarity: Things that look alike seem to be related.
- Connection: Things that are visually tied to one another seem to be related.
- Continuity: Partially hidden objects are completed into familiar shapes.
- Closure: Incomplete shapes are perceived as complete.
- Figure and Ground: Visual elements are taken to be either in the foreground or the background.
- Common Fate: Elements sharing a direction of movement are perceived as a unit.
I was confused by the statement, "Remember, often the main audience for your visualizations is yourself." Is he really suggested that most data visualizations are serving the needs of the maker as opposed to a client or project?
Misleading axes on graphs
Fantastic. Now how to educate everyone who is treating misleading data visualizations as gospel? I think most average viewers of the Bloomberg Business Week's critique would be been equally confounded by their response. Maybe I'm a cynic.
Regarding whether to include 0 in a line graph: is seems like this might depend on how far back the designer can track relevant data. If 0 has relevance than it should be included. If 0 isn't directly relevant, say the visualization is very clear about plotting a specific subset of years within the lifespan its subject, than 0 becomes less important.
The Principle of Proportional Ink
The concept of proportional ink makes sense yet has my head spinning as I'm sure most data visualizations I've seen in the mainstream violate this rule. I appreciate the redundancy of the message: be critical of how data are represented. I've taken many of these principles for granted over the years and imagine it will take some trial and error to try different designs and methodologies to internalize these rules.