2xBarton and Maps

Barton and Barton use their article to provide a critical analysis of using maps in visual rhetoric. They describe a map as quintessentially ideological when used as a visual aid. For instance, on a picture of a map of the U.S.A., it may be color coded to set apart different state regions, but we are to understand that those sections of states are not literally one color. The Bartons express that maps are always linked with authority and power to de-naturalize the world and set social rules. They talk about the rules of inclusion and exclusion which determine what is and isn’t included when creating a map. Rules of inclusion would include the purpose of creating the map, which is most likely to claim or legitimate territory (like in war). Another inclusion is what exactly is to be identified: terrain, roads, climate. The last inclusion I will mention is what symbolism and tools are used to create the map – something like what icons or how things are spaced.


My example uses a map of the world (albeit, an inclusion based map using man-made territory lines, flat layout, naming tendencies, etc.). This map shows the popularity of photo spots in the world, with yellow being the most popular and the faded grey being the least popular. Now this is not what the earth looks like, but we’ve manipulated the planet in such a way that this visual can easily communicate a message about photography habits in the world.

How could we apply the Barton’s concepts of inclusion and exclusion to something like typography? What do we include and what do we try to minimize and take away from when using typography?


Celebrex with Dombrowski

Dombrowski sets out in his article to ethically and rhetorically critique a real-life pharmaceutical ad from 2005. He begins with a little bit of background about the drug so we have proper context for his evaluation. Celebrex was a arthritis pain-reliever that was marketed in tense competition with other similar drugs and was under pressure from the FDA to be clear about associated heart risks. Dombrowski expresses that Celebrex is a prime example of how to mislead the audience to not comprehend the full message or all of the message with any impact. One way Dombrowski critiques the ad is that marketers would claim that abstract images and emotional triggers are meant to create cognitive links between the medicine and these images, but the Celebrex ad uses them blatantly to distract from the bad feelings of the drug. As a pharmacutical ad, the FDA requires the “fine print” of risks to be listed, but the Celebrex ad twists those words into unreadable lines that make up the visual figures and making it impossible to read. There are facts used, although selectively, and the ad works hard to present the benefits of using the drug over the very real possibility of side effects that had been seen in testing. Dombrowski feels that the ad does a great job of distracting, but a terrible job at informing.


My example is the Skechers advertisements for Shape Up shoes. Skechers advertised that all you had to do was tie your shoes to lose weight and that using these shoes would help tone your body. They exaggerated obviously, and ended up having to pay a $40 million settlement.

My question is how can you have marketing without distraction or flattering of a product? If you want to make your product as successful as possible, you want it to look amazing to every potential consumer.

Manning and Amare begin their article by discussing three ways to think rhetorically about visuals regardless of data accuracies. The first visual strategy is evoking a feeling using something decorative. This would include things like typography. The second strategy is choosing an indicative visual that provokes action by. This would include colors that grab attention or signs that give directions. The third strategy is to promote understanding by using visuals that clear up relationships. This includes things like charts or graphs. I liked what the authors said about the relatively new hybrid of combining visual and textual rhetoric all for the purpose of promoting ethics. I think this is the most effective way to create new and interesting data visuals instead of trying so hard to set the two types of rhetoric apart.

Manning and Amare go on to discuss visual rhetoric in various frameworks. The first is a Piercian approach to visual ethics. In this discussion, the authors agree with Pierce and reject the utilitarian mindset of maximizing pleasure and assuring the immediate realization of truth by the audience. Instead, the authors express that it is the discovery and research that makes the truth more effective. As long as the long term goal of getting a point across is accomplished, some immediate misunderstanding or provocation into studying is acceptable. Manning and Amare discuss in their second point that making visuals pleasing aesthetically is important, but should not be the #1 focus. Trying to “spice up” a presentation can go very wrong by fogging up the important data or adding distracting sounds and colors. It is a rhetorical choice in itself to balance visual strategies and visual rhetoric.


This is my example which I think does a good job of merging visual and textual rhetoric.

My question is how could we apply Tufte’s ideas of humanizing visual data to the three techniques presented by Manning and Amare? If we can’t think of how to apply the concept to all three, which technique would it fit into?

Empathy and Data Visualizations

The readings for today focused on implementing empathy into data visualizations in an effective way. This was an interesting read mostly because this is something I have never really thought about in infographics and other data. Dragga, Voss, and Zer-Aviv discuss how data visualizations are too focused on presenting data in an objective way and do not make the appropriate effort to “humanize” that data. Dragga and Voss give examples of various types of human death statistics and point out that the bar or pie graphs used to illustrate the numbers are lifeless and do not appropriately represent the victims in these accidents. They want the reader to try to imagine how a photo or illustration might make someone actually feel what those numbers mean. I did not really agree with Dragga and Voss for a few reasons. One is that I think trying too hard to implement a complimentary graphic can easily look sloppy and cause distaste rather than empathy. If you included the wrong picture, wouldn’t that be worse than including no picture or an emphasizing piece of text? I also think that presenting the numbers as graphic data is the easiest way to get the information across to the audience as clearly and efficiently as possible, something Kostelnick and Tufte both saw as important. I personally do not think empathy is necessary, and there are other forms of visualization that would be an appropriate place to try to conjure that. Zer-Aviv provided an excellent example of how empathy can be used in an interactive data visualization, but it was not the basic graph set we are all familiar with. I feel it was a special case.

One example of trying to create empathy by using the power of one story versus a huge statistic is the Girl Effect video, found here.


I think this video does a better job of trying to get a story across about one person included in a large statistic than all the examples we were shown in the reading. This would be an effective way to get that feeling across, and is easier than trying to make a huge number look relatable in a static image.

My question is about the appropriateness of empathetic graphics in a visualization focusing just on the numbers. Is it appropriate to add something to try and make the reader feel sad for victims in every statistic? What would be a more effective type of data visualization to add an empathetic spin on than a static chart – video, animation, website?

Kostelnick Class Activity

Group #3 – Liz Benson, Drew Kempin, Bryan Miller, Nick Jones


Line Graph; No fluff; All about the data and presenting it accurately; Clearly labeled and easy to read by all audiences


CAD Drawing for Engineers; Focus is a specific audience; infographic easily understood by who it’s meant for


Focuses on social media Girls = pink, blue = boys –> cultural norms People make up a bar graph


If The Moon Were Only 1 Pixel

Kostelnick and Clarity

Kostelnick uses his article to discuss multiple types of rhetoric as they apply to visual data and uses clarity to compare and contrast them. He also discusses changes in technology and how this has effected each type of rhetoric.

The first rhetoric mentioned is the rhetoric of science. The designers are very audience focused, believing all persons to be of equal capability of interpreting the data from the visual. This makes the visuals predictably received by the readers. Rhetoric of science comes from a culture of graphs, which are appealing to almost all readers. This makes the visuals more inviting because readers are more drawn to good display and it boosts their credibility because of easy reading. The rhetoric of science also emphasizes a clean, uninhibited communication of getting the data across. This makes the visuals very clear because there are no distracting colors, designs, or other elements to prevent the reader from understanding a graphic or other visual data.

The second rhetoric mentioned is rhetorical adaption. Depends highly on readers coming from all different backgrounds and having different interpretive abilities. Designers have to take into account high and low motivation in readers, and will have to add what Tufte calls “chart junk” into the visuals to add decoration and draw the eye of all types of audiences. The purpose and context of presenting the information also defines its clarity. Technology has tremendously altered reader expectations. The typical Powerpoint template presentation is the norm and what readers will be measuring against for clarity.

The third rhetoric mentioned is social rhetoric.One form of this is a company that does reports in one certain way, setting the context for all future reports. This effects the social norm of the company, and that experience within that context will make the data more clear. Another example is the rise in infographics and statistics within the American public. Oftentimes, data visualizations are left without text explanations because these visuals have become so commonplace that the public has become visually literate. Readers also bring with them the context and past experience of reading all kinds of other data visualizations whenever they read a new one, which effects clarity.


My example is a data visualization of taxi cab rides taken in Manhattan, showing the route and using colors to represent each district. This graphic is definitely lacking some clarity. I had to stare at it for a while before understanding what it was representing. It could be made better by using some text explanation of the colors, the size differences, and the to and from of the graph. I also deceivingly looks like a pie chart but is not one upon closer inspection.

How could we correct the graphic in my example to make it more clear using the data, adaption, and social rhetoric that Kostelnick talks about?

Tufte and Visual Explanation

In Tufte’s article, he sets up two examples of how real life stories were turned into data and then made visualized data in order to come to a conclusion. Both of these data visualizations came in the middle of the research and did not reflect the results.

The first example Tufte discusses is how John Snow helped stop the cholera epidemic in London in the Fall of 1854. Tufte describes the story of Snow mapping each confirmed case of cholera and pinpointing the cause to a common water source in the area. Tufte talks about 4 different factors of data visualization that helped Snow come to the conclusion of the well causing cholera. The first is placing the data in an appropriate context. It made the most sense to put the cases on a map to pinpoint a physical cause, rather than making a bar chart timeline or cause of death. The second is making quantitative comparisons. Tufte expresses that showing why something happened compared to why it didn’t happen gives clear, strong evidence for the cause and effect, but this was not used in the second example. The third is comparing your findings to alternate choices and explanations. The fourth is to examine your graphics and look for possible errors in the data.

The second example that Tufte discusses is the failure of the 1986 launch of the Challenger. His main cause is identified as a failure to observe the second and fourth factors of data visualizations. If the administrators had correctly observed that there were past accidents involving a correlation between temperature and O-ring connection, they could have made the correct no-launch choice. Tufte also expressed that the administrators did not look for possible errors in the engineer’s observed data, and a pre-launch analysis would have led to a no-launch.


This shows the migratory data of people moving from a given state to Travis County in Texas. I think this puts the data in an appropriate context because it is based on mapping the country, so using a map is the best choice to visualize the idea. This graph doesn’t really have a need to take into account all the people who haven’t moved to Austin, Texas, because this data is specifically focusing on where people are coming from in the county. It wouldn’t make sense to say where everyone else in country is moving. It would be possible to make a similar graph showing where people move after living in Austin, but wouldn’t be very useful to either party. I assume this data is from a census or similar document about population, so it makes sense that there is no data on temporary residents – tourists, visitors, business people. I think this data was properly studied to look for errors before they made the graphic. There may be errors now due to time passing and population changes.

My question is which of Tufte’s 4 factors of visual representations is the most important? How would you rank them if you can’t pick the most important?