Maps with Barton²

This article is a boring yet simultaneously interesting read on the rhetoric behind map design and reading. Boring in the sense it seems to go unnecessarily deep on a rather dull subject in maps, and interesting for the same reason. The core of the article seems to focus on two rules or conventions within map design in what the article calls the “Rules of Inclusion” and the “Rules of Exclusion and Repression.” The rules of inclusion were initially summed up in the article as “whether something is mapped, what aspects of a thing are mapped, and what representational strategies and devices are used to map those objects.” With a map there’s a whole list of things you can map like landmarks, lakes, rivers, demographics, state capitals, and many more. This takes into consideration what the map is used for, by whom, and in the article who was even allowed to look at the map. The rules of exclusion and repression were largely the same idea, but opposite. The big rhetorical narrative I got from the article is the rhetorical conventions of “what do I map and why” as well as “what do I not map and why.”

Admittedly I saw another blog post for my image, but I wanted to expand this idea.



I wanted to compare two different maps of the same region to attempt to exemplify the rules discussed in the article. These two maps serve different purposes and that idea is obvious by the different styles as well as what it includes and excludes. The top map I used a countless number of times my first year here at Purdue because it does it’s job well which I’ll assume to be as giving a clear layout of campus. You could technically zoom in further on the lower Google map, which would then show you building names but it doesn’t follow the rule of inclusion presented in the top. A lot of the times on my class schedule it would merely say XXX417 where XXX was the building code, like EE for Electrical Engineering Building. If your class was in RAIL for example, I’m unable to find this on Google Maps without typing the exact name “American Railway Building” information that is not readily available, but the location is easily found on the top map.  Whereas the bottom map seems to be more tailored for roadway navigation, only including major landmarks around Purdue, as well as including landmarks such as ponds. You could perhaps use the top map as a sort of translator for the bottom map, should you have to use GPS to find the building for your class. A lot of the times it was hard to Google the name of a building just based off the short code given in my class schedule.

While this article was over maps, I feel the general idea of rules of inclusion and exclusion can also apply to our infographics, I feel that was probably why this was included as a reading. How could we apply these rules or re-engineer these ideas to our infographics with that in mind?


Celebrex! Celebrex! Dance to the Music

The main focal point of this article is the supposed unethical way in which Celebrex presents its commercials, focusing specifically on an example from 2005. The commercial uses fine print, and other medical information about the drug to form the images on the screen. From people, to bikes, and other objects rather than being made of lines they are made from fine textual sentences of which some are focused onto the screen for the user to read. The unethical portion comes into play when asking if this is an acceptable way to present the “fine print” of a drug. Drug commercials are weird, because you have to walk a fine line of truth I feel like in order to get people interested, and appear better than your competitors. You can’t honestly expect drug commercials to be completely unbiased black and white text on a screen. One can argue that the calming nature in the presentation of the video, the cherry picking of relevant information, and the way in which the text was presented come across as unethical because it presents the message how the company wants to read it. This is obviously good for the company, but some may feel cheated or even betrayed if they suffer medical complications. The problem is, when you ask the question “How should it be portrayed” I personally can’t answer that. There’s this spectrum or continuum of style in how a commercial like this can be presented. I feel like this falls almost on the extreme end, with the other being a black and white screen with the work “Celebrex,” the information, and a screen of possible complications.

For me, had the word Celebrex not been in the title, I thought for sure this article was going to be about the product HeadOn. I suppose this is  a cure for a headache but the commercial actually doesn’t say anything beyond “HeadOn, apply directly to the forehead” three times fast. The product is basically pure wax and doesn’t treat anything. The commercial never actually claims to do anything, it merely instructs you to look like an idiot for rubbing a $6 bar of wax on your head. There’s nothing illegal about that but what about unethical?

I feel like I’m going to ask a similar question to a lot of other people, but what “line” do we set for ethically presenting something, especially something like a medical commercial? Celebrex is merely presenting information in a way that isn’t exactly lying, but fuzzing the truth. HeadOn isn’t doing anything, but they way they present the commercial gives the annoying association of “Oh, I rub this on my forehead when I get a headache.”

Visual Ethics

When you told us in class that the Dragga and Voss was popular within English rhetoric I was surprised. Not because the article is bad, but it seems narrow-minded in focus. It’s funny to hear Tufte, another well-known name with the English community, talk down about “chartjunk” only for Dragga and Voss to say we should add these as to not dehumanize the statistics and be empathetic to the subject matter. The type of visuals Dragga and Voss talk about feel like they fit into the category of decorative talked about in this article. It’s meant to illicit an emotional response, which may not always be correct.

The thing I enjoyed about this article the most was the continuum or spectrum they showed with images and language. This article felt like it was more focused on fitting graphics to the situation less than the need for empathy that Dragga and Voss focused on. I feel this is the appropriate way to present data because it’s unbiased, and an emotional response is not always the response you want. I talked in the previous blog post about not showing a lack of empathy, like the colorful Hiroshima graphic from Zer-Aviv, but I don’t believe empathy is necessary especially when it can detract from your data’s focus.

“An ethical deployment of an informative visual would be a diagram, chart, table, or graph the enables an intended audience to extract the statements or ideas needed to follow the author’s thoughts.”

My big argument, and main belief from prior learnings before this class, and from the readings and rhetorical situations we’ve been put in through class discussions and projects is to always suit your data to the audience. If empathy is needed, add it. If a clean, unbiased visual is needed go that route. I haven’t been a huge fan of some of the past readings in class of Tufte and Dragga and Voss because they’re so definitive in their definitions of what is needed within visuals, I don’t think that is correct. I’ve always, especially when looking at the past two case studies, applied my visuals to fit the situation and I think that is the route to take in creating visuals.

While I won’t make the claim that one author is “correct” in their assumptions about visuals, but who’s ideas and guidelines for visuals do you feel is the most correct, especially in the scope of this class? Looking specifically at some of the past articles in Tufte, Dragga and Voss, etc.

Does something like this fall under the category of decorative, informative, or both? It’s on this weird spectrum where it’s showing cool visuals, but they’re all realistic and relevant to the data. They’re not there for decorative purposes, in my opinion, rather they are there to supplement the text.

Data and Empathy

Dragga and Voss as well as Zer-Aviv both present an interesting concept that I didn’t even know was a problem in today’s reading in the idea of empathy within data visualization. In Zer-Aviv’s tagline it even begs the question “Should it even?” which for me, I initially said no when reading Dragga because it seemed needless but I flipped my opinion because of the opener of Zer-Aviv’s article. It’s not that he convinced me that we should add empathy to data visualization, but I did enjoy his example tweet of not showing empathy in the Hiroshima statistics. As Dragga and Voss said, we don’t need to show gruesome pictures to show empathy, but the example in the Hiroshima statistics seems widely underwhelming in the emotion it’s portraying, perhaps even displaying the wrong emotion entirely. For me, I still feel pushing for empathy in data visualization isn’t necessary, but I don’t think ignoring it is correct either. Even without directly showing empathy, a tone is set within a data visualization by how you present it. When I was doing my case study 2, I didn’t directly show empathy in drug user’s problems and downfalls, but I also recognized the seriousness of the topic at hand. For me, a lot of the example graphs and visual statistics shown in Draggo and Voss don’t present a problem to me. The closing section of the article is titled “possible solutions” assuming this is a problem. It talks a lot about ethics and emotion but I don’t feel that by neglecting to show direct empathy they are defaulting to not being empathetic.

For me, if there was an argument for showing empathy within data visualization, the reason for me would be for pathos and emotional appeal to the audience. The example we did in class “Slavery Footprint” I don’t find successful because it shows empathy in it’s statistics, I think it’s successful because it shows empathy for the right reason. The whole cause of the website is to persuade and give the audience the realization of their actions. Looking at the graph of Napoleon’s causalities I don’t feel pictures of headstones are necessary. I gain nothing by gathering empathy from the graph because that was not it’s purpose. Empathy within data visualization needs to serve a facilitating purpose within your graphic, otherwise I don’t personally agree with its usage.

Am I wrong in this assumption that empathy within data visualization should only be used to facilitate an emotional response (if that’s what you’re going for) from the audience? Should we be careful not to show a lack of empathy, like with the Hiroshima statistics in the Zer-Aviv example?

I feel showing visual empathy within a graph also bridges the line of bias which I think is important to avoid.

Kostelnick and Clarity

I really enjoyed Kostelnick’s article because it finally hits on a lot of the frustration I have in Tufte’s points in his articles. Tufte focuses too much on the actual content and clarity of charts from the conventional side of things while ignoring the audience that chart is directed at. There was a lot going on in the article, but the beginning has a lot of talking points that Tufte seems to dislike especially in reference to the actual composition of the data. He begins by talking about the effectiveness of different types of charts, talking about the ineffectiveness of pie charts in comparison to bar graphs and others. I could tell him and Tufte shared similar grounds in the visual representation clarity department. The quote that really made me happy, and almost say outloud “Finally” was the direct reference to Tufte on page 284 in “the designer may even have to resort to some of Tufte’s loathsome ‘chartjunk.’ In these ways, clarity may initially depend on pathos appeals that draw readers in the display by stirring their emotions. This is one of the biggest points Tufte always missed on in his articles. Especially on the internet, short little blurbs, infographics, data visualizations and similar objects are everywhere. What makes me read these is not the clarity and composition of the data, it’s the initial design that draws me in. I’ll sooner find myself reading about a topic I have no interest in because of it’s initial design that intrigued me than I will read about I topic I enjoyed presented in the cut and dry manner Tufte would have you believe is correct.

That said, this article delves into multiple angles of user clarity in data design, as it’s not such a simple concept to tackle. You can find clarity in interactivity, adaptation for your audience, and others. But for me, I’m a firm believer that the initial impression is the most important. Regardless of the clarity of the content within, you’ll never get the user to actually absorb that content if they’re not inclined to look at it. I feel an attractive, slightly less readable chart with “chartjunk” is better than a perfectly readable visualization that never captures the audience’s attention to begin with.

With that said, what do you think is the most important factor, especially in relation to this article, in user clarity? Obviously a combination of all is the best bet, but if you had to focus on a certain element of design?

Slightly relevant XKCD as always

Tufte and Visual Explainations

Tufte’s article sets up two different examples of real life situations where data visualization is used to come to a conclusion. It was interesting to see how many different ways these data sets could be compiled to show different conclusions, especially the initial one with the water pump and John Snow.

The first example, from the mid 1800s, follows the story of a man named John Snow in the middle of a cholera outbreak in London. Plotting deaths from cholera on a map, he is able to make a correlation between the outbreak and a water pump on Broad Street. Obviously this is in the 1800s and his methods aren’t exactly up to the scientific specifications of today it was interesting to see that in a time of lower scientific advancement he tested the water itself to no conclusion. Initially I thought this was pretty interesting, he worked with what he had, but Tufte gave examples to put a bit of a spin on Snow’s data visualization. Tufte showed different aggregations of the same data through using different geological subdivisions of the map. Depending on how the locations of the map were seperated, the data could be displayed in such a way that show no correlation to the water pump location and the outbreak. He makes an important note on plot maps like this in that they could just show population data. This way not the case in this example, but it is possible there are more cases in an area simply because it’s more highly populated. It reminded me of this XKCD comic, linked below.

It really hits home the message of right and wrong ways to display data. It’s easy to work backwards from today knowing the cause of the outbreak and look at his data map and say “Of course, it’s so obvious.” But at the time, it was probably counter-intuitive for Snow to find no impurities in the water, only to keep looking at it as a possible cause. I felt myself saying this a lot in the second part of the article about the Challenger accident. Looking at the graphs as a layperson it seems obvious to look at and say “Look, there’s more accidents the colder it gets” but I’m sure scientists who knew better than me with millions of dollars and multiple lives on the line were more scientific in their analysis. That said, it looks like their is a clear correlation between temperature and O-ring failure from the small data set shown. I think a lot of it had to do with the fact NASA had never rescheduled a launch prior. It seemed like they put convenience and reputation over clear evidence.

Going back to Tufte’s examples of different ways to display data in reference to the cholera outbreak dot map, how do we know when we’re displaying data correctly? Like I said prior, it’s easy to make a correlative link between the two now because we know they’re linked, but at the time what if Snow had aggregated the data wrong or displayed it in a different way?

10/28 Readings

The first reading, Communicating with Animated Infographics, talks about two different types on infographics and, as the title implies, how they communicate information. The first, depicting statistics, talks about different manners in which infographics can do this. The first example, using iconic graphics, was a bit lost on me as the example video from Youtube had since been changed to private so I had no basis to their explainations. That said, I sort of understood where this explaination was going. Using a storyline was interesting because it actually hit on the style used by one of the infographics in my conventional analysis essay. This method of storytelling and information presentation,  udging by the previous video I did for the essay, is really great at putting a statistic or problem into a more dramatic effect. The audience can actually get a back story, or more emotional appeal, by putting the statistic into a story. The video in question is embedded below.

Post-production animation, similar to what we did in case study one, is the act of adding infographics onto previously recorded video. The example video was cool and worked well in putting the statistics into perspective. It puts a lot of information into a short video but I never felt bored while watching it even though I wasn’t particularly or initially interested. It seems like a very powerful tool, but very hard to implement, I imagine this video took a while to put together.

Not to get too hung up on the first reading, I wanted to inspect the second reading a bit as the title Themes For a Good Infographic seemed like an important read with the case study 2 on the horizon. I particularly liked the section of “The graphic stands on its own” as this was the direction I wanted to take my case study 2. While it is a bit of a bad habit, most people browsing the internet don’t want to read, and I feel that’s why infographics have surged so much in popularity. Presenting my case study 2 in a manner that interests the audience through using mainly visuals, I believe, will be an important aspect to a successful infographic. Just to share another relevant artifact, I always enjoyed the below type of infographic, even though I don’t even drink I just think it’s a very cool representation.


Looking at the different types of infographics and visual tactics in today’s readings, what are some ways the class is thinking of applying these to case study 2? As I mentioned, I really like the idea of using a heavy visual representation and letting that speak for itself, as seen above.