design x food

Google Images became overwhelming while I searched for data visualizations. There were so many different types of visualizations to choose from; some boring, some eye-catching and others very confusing. Perhaps I liked the image below because of its striking, complex, and also simple qualities. Or maybe I was just hungry. Nonetheless, I chose this image because I wasn’t quite sure if it would meet the common standards of Tufte, Miller, and others we’ve studied thus far.

design x food

It took some investigating to figure out what exactly this graphic is trying to show. I didn’t know if this was representing details of an average American’s breakfast, if it was about cereal specifically, I couldn’t tell. While it was taken out of context, I felt it was problematic that it took so much searching to determine what it was the image was trying to convey. I certainly couldn’t tell from the image alone.

What I found was a book created by designer Ryan MacEachern that represents his diet through graphics rather than just numbers. He presents pages of graphics along with a table detailing his food intake for the day, explaining that the book explores “the nutritional values of the diet and presents it in a contrasting way, it juxtaposes the dull and boring appearance of the food I was eating by presenting data using colourful vibrant foods, which were almost entirely excluded from my diet” (MacEachern).

While overall these images, and the book in its entirety, are very visually appealing, many of the visuals seem to overshadow MacEachern’s overall goal, something which Tufte warns against. Sure, showing the nutritional values with cereal is more interesting than with tuna, and is a direct representation of the juxtaposition he was aiming at, but it doesn’t really have anything to do with the data. He was actually eating tuna in some cases, not the sugary cereal. The point here doesn’t seem to be on the numbers, but rather the visual representation and juxtaposition of the different diets, the differences in vibrancy of his diets. The statistical qualities, which I assume were supposed to be the main point, are pushed to the background (literally, the tables are very difficult to read even in the book) and the images distort the numbers. While in the case of the image above, the use of different cereals is definitely interesting and colorful, I was more interested in the representation than the actual dietary information. I noticed the food more than the numbers, which wasn’t really the point.

That being said, his representations do have some strengths. By transforming his tables of nutritional values into a graphical representation (even though it may be a bit distracting), the numbers become more visually appealing to the reader. Also, the basic representation is simple. He was trying to show the percentage fat, protein, and carbs make up of a day’s worth of calories and, if the distracting visual was taken out, the viewer could clearly see the breakdown of the numbers, seeing how, for example, the 38% of protein made up 482k calories. The graph is focused, in that he takes the key qualities of the table that he wants to show to create a bigger picture. Rather than focusing on the nutritional values as separate entities, he combines them to show how they make up one larger characteristic of his diet (calories).

The problem here is that there’s an uneven balance of design and statistical knowledge, the design aspect clearly winning. Yes, the graphical representations are more striking than a simpler pie chart could be, but it is also a distraction. Frankly, he would have been better off with the tables included in his book and a simpler pie chart, as those are more focused. The goal of this compilation is very interesting, but I don’t think, unfortunately, that the data visualization meets it in an effective way.


3 thoughts on “design x food

  1. I definitely found this example the “prettiest” and I actually spent some time reading through his book because I was curious to see what he was discussing. I feel that the crisp, straight, and clean presentation of the data, really aided in the fact that there wasn’t much text. Directly regarding the issue you had with the foods being presented, in contrast with the foods being discussed, I feel that while it would not match, it didn’t hinder my interpretation. In all of the examples, the food presented came from the same food grouping. Whether it was high protein foods, high carb foods, etc. they all came from the same category even though it might not have been the food consumed.

  2. I like the overall point you made about how data visualizations run the risk of overpowering statistical significance with over-the-top designs. Most of the readings we’ve done about how to effectively represent data in graphs and tables have pointed toward one golden rule: keep things pretty simple. Whether or not data visualizations abide by that rule, I don’t know. The whole point of a data visualization seems to be adding some interesting, eye-catching appeal to what some audiences may brush off as dull data, which has now occurred to me as somewhat contradictory to everything I’ve read thus far. I guess, like everything in life, it’s all about finding a happy medium, and in this case, that medium exists somewhere between design and data.

  3. This is a really interesting project! It sounds like it’s pretty complex; there’s a lot going on and not all of it fits together coherently. You raise a good point about how the image of the cereal doesn’t completely match the point he’s trying to make. Tufte might say the cereal is “chart junk.” But a visual designer would, perhaps, have another opinion. Great example!

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