What makes bad figures bad
The author talks about three aspects that make bad figures bad: bad taste, bad data, and bad perception.
Bad taste here could be also understood as over design，designers added too many elements that unnecessary for delivering the message, or even fuzz the message. As the author said, given example Figure 1.4 obviously has too many design features such as the background texture, designed font, and the 3-dimensional effects and its shadow, making it hard to read and compare the data. Another example of bad taste is Minard’s visualization of Napoleon’s retreat from Moscow, and the author cited Tufte’s argument that “ Minard’s can be described and admired, but there are no compositional principles on how to create that one wonderful graphic in a million”, which pointed that Minard’s image contains too much-processed information to be used in daily design. While I’m not convinced by the point that only routine and imitable data images are well designed. Different data convey different information. Minard’s image seems to be a unique study of Napoleon’s retreat, which is not daily data. Therefore, in my point of view, it’s not proper to require every visual data to be able to apply to daily use. The author also compared Monstrous Costs’ by Nigel Holmes, and six kinds of summary boxplots by Tufte, explaining the conflict between designers and audiences, saying that over-designed images give audiences a deeper impression than Tufte’s image which delivers the data much more efficiently. In my opinion, delivering the message to audiences is the meaning of the design, only focusing on the “data-to-ink ratio” is the designer’s arrogant choice which ignoring the acceptors.
In Bad data, the author shows “A crisis of faith in democracy” from the New York Times, which we have discussed in class. This image magnified the decline of Democracy by analyzing data in a misleading way. This is the powerful point of data visualization which gives a convincing fact to indicate some forged political view.
The third part, Bad perception, talks about some bad design choices that might confuse audiences, such as 3-D format, junk-free plot, and aspect ratios. Here we see the 3-D format image again, and it really delivers wrong information. Since the column chart is meant to compare just 1-dimensional data, while the 3-D format is creating 3-dimensional volumes, enlarge the original data ratio, therefore it’s an incorrect format.