Barton and Barton

Barton and Barton talk about the ideology and rhetoric behind map. This analysis is pretty new to me since I have never thought about the reasoning behind a map. The first idea argued in the article is the map as quintessentially ideological. The color used in map is for perception and recognition. For instance, Indiana is colored pink, but we all know that Indiana is not pink. The second idea of Barton and Barton is denaturalization of natural. When Barton and Barton are talking about denaturalization, they include the rule of inclusion and exclusion in their analysis, to make their argument more reasoning. Rule of inclusion determines when a thing is mapped, what aspects of the thing is mapped, and what representational strategies and devices are used to map those aspects. The first thing to be included in a map is phenomenon and aspects of phenomenon. Then, the second is to determine what kind of strategy or device to use to symbolize the particular phenomenon. And the last is to determine ordering. The practice of rule of inclusion needs accompany of rule of exclusion. In rule of exclusion, Barton and Barton talk about synchronic perspective and diachronic perspective.

The picture I used here is a statistical map to show the mean travel time to work among different states. From the map, we can easily understand what the map is talking about and easily see the statistical results. In the map, marker shows us that red means longest travel time to work, and dark green means shortest travel time to work. With those markers, we can easily derive that New York is one of the states that has longest travel time to work, and North Dakota is one the states has shortest mean travel time to work.


My question: What situation will cause the theory of Barton and Barton does not work for making map? Or, it is possible for the theory of Barton and Barton working negatively towards making map?


In this article, Dombrowski talks about a advertisement in 2005 from both rhetorical and ethical aspects. Before going to further detailed information, Dombrowski offers related background first. The TV commercial is Celebrex, and the TV commercial began to run in 2005. Celebrex was a pain-reliever and was under the tense pressure from FDA about related heart risks. Dombrowski argues that the major fault of Celebrex is to mislead audience. The way that Celebrex mislead audience is misleading visual presentation, called fine print. That is, what audience can interpret from the ad is that they can perform normal daily function without pain. However, FDA requires that associate warnings accompany such ad. So, the printed man and woman in Celebrex is the first misleading visual presentation. And the second visual misleading is the text. The text in ad is extremely tiny and unreadable, and most audience could not notice the line of text in that ad. So Celebrex communicated important technical risk in misunderstanding and miscommunication.

The example I used here is to show the exaggeration of a makeup commercial. The left half  picture glorifies the function of their new makeup product, the eraser, arguing that it will erase all winkle on your face and audience can barley see any winkles on Christy’s face. However, the picture on the right clearly shows that winkles on Christy’s face is obvious. It is not hard to see exaggeration from the comparison of two pictures.


My question: how can audience do to avoid being misled by misleading TV commercials?

Manning and Amare

In this article, author pays a lot attention on talking about what is visual ethics and how to realize visual ethics. The first visual ethics mentioned in the article is truth, that is never manipulating images and providing information as truthfully as possible. I cannot agree with this principle any more. Because truth is the rule for everything in out lives, especially for social media. The purpose of social media is to inform the public, get the public understood the truth, but not cheat them. In this part, author also summarizes six principles that apply for construction of visual ethics. The six principles are categorical imperative, utilitarian, hedonism, golden mean, golden rule, and veil of ignorance. The second part of the article talks about an approach to visual rhetoric and visual ethics. According to Pierce’ theory, visual and textual are more than a close integration, but they are in the same system, that is, they can be applied to similar principles. Author begins to discuss two of the most common principles, effectiveness and ethics. Effectiveness is in terms of communication goal, and ethics is in terms of purpose or choice. The third part of the article talks about visual rhetoric in the framework of epistemology ethics. There are three principles applied to explain this part. The first one is decorative. Decorative aesthetics is able to evoke feeling among the audience. Decorative includes font, color, typology and so on. The second principle is indicatives, which is able to evoke physical reaction among audience. And the last principle is call informative, which can be considered as a combination of decorative visual and indicative visual, but informative is not limited to those two. Informative communication statements can be considered valid by means of logic reasoning or experiment.

The picture I use here is a advertisement made by NASA called journey to Mars. I think this picture incorporates all principles of approach to ethics, decorative, indicative and informative. First, this picture looks good. The color used is appropriate and pleasing. Those aircrafts look delicate. Second, the purpose of advertisement is to call for action, indicative. This picture may provoke some audience to think about traveling to Mars. Third, this picture is informative, informing audience that people are going to explode Mars.


My question: Should we or any company post a visual file, like a picture to social media if it is effective but not ethics?

Empathy and Data Visualization

Both articles are talking about the relation between data visualization and empathy. The topic is pretty new but it is really important, because the purpose of data visualization is not limited to inform audience the data, but to call for emotional reaction. In the web article “DataViz – The UnEmpathetic Art”, Mushon Zer-Aviv first defines what empathy is and how empathy is related to data visualization. According to his definition, empathy is an ability to understand those experienced people’s feeling. And the relationship between empathy and data set is that data set is simply a group of related data to inform the audience what the situation is, and it refers to more scientific reasoning, rather than emotional reasoning. In Zer-Aviv’s article, the example mentioned is the data visualization of 70th anniversary of Atom Bomb in Hiroshima. In my opinion, the data graphics is over pretty and is easy to get audience distracted, encouraging audience to generate other feeling instead of empathy to atom bomb. That is, the visual design of this data graphic is too pretty which is hard to connect it with an extremely serious topic. I would like to say that the design is good design, if it is used in other less serious situations. The second example brought up by Zer-Aviv is about US gun murder. This data visualization is more able to evoke empathy compared to the first example. The bright orange means the bright future of those victims, and grey shows that the age that they are supposed to live if they were not killed. In the second article, Dan Voss and Sam Dragga also use examples to show their concerns. The second graph shows that although the information is redundant, the emotion is not redundant. That is, there is no obvious contradiction between expressing information and calling for empathy.

The picture I used here is a statistical graph shows the number of execution by stats from 1977 to 2013. I would like to say that this graph is able to call for emotional appealing and empathy because the good use of color. States have 0 execution or less than 20 executions are marked in white or light yellow. But for those have 51 to 100 execution or more than 100 executions are marked with aggressive red. Aggressive red is always the best color to draw audience attention and evoke emotional appealing. This picture is not limited to evoke empathy, but it also offers enough information at the same time. Audience is able to derive that executions in south is more than that in north.


My question: How to avoid redundant information but call for enough empathy?

Kostelnick and Clarity

In this article, Kostelnick pays a lot attention on explaining clarity and apply clarity into responding Truft’s idea. At first, the author argues that graphical data visualization should be efficient first, then be accurate. According to author’s idea, efficiency is more related to audience instead of data. So, graphical designers are supposed to take audience into consideration when they design graphics. However, in order to appeal audience, some designers would like to omit something that is hard to understand or something that gets in the way of reader’s viewing. In other words, graphical designers maximize clarity of their graphics. However, sometimes clarity means inaccuracy because the graphics is going to appeal almost all readers. So, I would like to say that those graphics is reader-oriented instead of data oriented. Although I said that those graphics are reader-oriented, I do not mean that they do something wrong or improper. As I have said before, compared to researchers, designers have to take target audience into consideration since their job is to inform audience and get themselves understood. For researchers, they are more data-oriented or accuracy-oriented, because they do not have to appeal audience, they have to appeal themselves, to find out the truth. In order to draw all types of audience, designers have to add “chart-junk” into graphics to add visual decoration. The another reason of maximizing clarity is all for future research. Because todays’ report or discovery forms a solid context or background for future research, and the context will make the data more clear.

The picture I used here is a statistical chart about help of social application on job seeking. Personally, I do not think this chart is clear, or designed in clarity, because it is really hard to interpret even though it does not require too much professional knowledge. I have several suggestions to modify this chart. The first one is to add some text explanation to help illustrate. A little bit text would be helpful and make this chart more readable. The second suggestion is to separate the over complex pie chart into a group of smaller but more specific pie charts. And each pie chart can represent one social application. That would be more clear and more readable than wrapping up all information into one chart.


Question: What is the best way to evaluate the clarity of one graphic? Is there any standard about clarity to follow when designing charts?

Visual Explanation

Compared to other articles read before, Visual Explanation is written more in a reasoning and professional way. As Truft introduces at beginning, this article, especially this chapter, is going to explore the use of statistical and graphical reasoning. That is, the function of statistical or graphical chart is not limited to simply show tendency of data, but help collect evidence, draw conclusion and make decision. I have heard about the cholera epidemic in London, but I have no idea about how people survived from this cholera epidemic before reading the article. Truft attributes the survival from cholera epidemic to statistical graphics. As we can see, the statistical charts used then is different from normal statistical charts. First of all, the main function of normal statistical charts is to show tendency of data. That is, if Snow used normal statistical charts then, it would be helpless since of course the amount of infected or dead person was accumulated. If they used this chart and published it to the public, the whole society would fall into panic. What they used then was a statistical map, showing relation between those dead person and the pump on Broad Street. As the evidence accumulated, Snow found out that those dead person had a close relation with Broad Street pump. What is, most of these dead person lived on or close to Broad Street. After finding out the cause, the next step is to intervene, to act. And the result showed that Snow was correct, and his theory was also been approved. I would like to say that this is the power of statistical reasoning. What we have to do is not limited to read the chart, but to interpret the chart, to get the information we want from the chart.

The accident is more famous, the space shuttle challenger. The reason to this accident is obvious that the O-rings did not work due to the cold weather. However, this question has been discussed before launching, but they failed to make a correct decision. After the explosion of space shuttle challenger, everyone attributed the failure to O-ring, but no one was able to make a direct relation between O-ring damage and the cold temperature. Before launching, engineers did draw some charts to convince officials not to launch Challenger, but they failed. The reason is that charts they drew were not convincing, that is, charts failed to show a direct relation between O-ring damage and cold temperature. As analytical graphics, they failed to reveal a risk that was in fact present.

The picture I used here is a bad example of statistical graphic. The chart failed to show the relation between each bomba. I would like to say that magnifying the height of a mushroom cloud is not a correct way to show the magnified destructive power of nuclear blast. What we can read from this chart is that the power of Tazr is n^2 times of the power of Bravo, but actually it is not. This picture is misleading. Misleading is also can be see from the first two super tiny mushroom cloud.


My question: Is there any method to analyze or to test if the chart is useful or not? Is there any standard for designers to follow to avoid misleading chart?


This article mainly talks about scientific research and scientific data. Author thinks that scientific reasoning or science is the root purpose of articles, that is, scientific reasoning is the original encouragement for readers to keep reading. The connection between scientific data and presentation is close, because the later one is the most common to inform audience. There are three different types of presentation. The first one is forensic, which talks about already happened event but is still debated, or argued. The second type of presentation is called deliberative, which talks about events happening in the future and the best access to the future event. The last one is named as epidemic, which talks about events happening now, including achievement done recently. After talking about three different types of presentation, author moves to introduce strategy used in scientific research. Using strategy in scientific presentation is really important, especially when audience is not quite familiar with, or even interested in the scientific topic. One of the most useful and most common strategy used in scientific research is to keep to them thinking about, and to keep them wondering, or asking. Once audience think about scientific topic by themselves, the purpose of the scientific presentation is achieved partially. For the next part of the article, author talks about scientific data and function of scientific data. The most common or most clear way to express scientific data is the use of charts or diagrams. Here are different ways to express scientific data. The first one is vague explanation, which means that the chart is going with decent explanation and most of the audience are able to understand that the chart is the talking about, that is enough. That is, we can interpret no or little deeper information from this type of chart. But, this type of chart is enough for audience who do have related information about the scientific data. The purpose of this type of chart is to familiar audience with scientific data. The second type is deep explanation, or detailed explanation. “Depth” means that this type of scientific explanation may be going with tens or even hundreds of paper to explain detailed information of the scientific data. This type of explanation is used for researchers or scientists to interpret detailed information or to use as recourses for future research.

I used two pictures here as my example of scientific data. Although the topics of these two charts are different, we still can easily see that , the first, obviously, is deep explanation used for scientific research and the second is used to inform audience. For the first one, I think some audience is able to read these two charts on the top of the picture, because these two charts are going with x and y makers which help read. At least, I think audience is able to understand the tendency of each two charts. However, for the image below two charts, I have no clue about what the image is talking about, and I even cannot interpret any relation between the image and two upper charts. Audience will definitely lose their mind and interest, If they are encouraged to see the deep explanation picture. For the second picture, it is much more readable for audience who have little related information about scientific data. That is the why the second picture is considered as vague explanation.


My question: Is there any transition between vague explanation and deep explanation? or how can we distinguish between vague explanation and deep explanation?