Anh Mai

  1. Look at Data: What makes bad figures bad

By giving a variety of examples, author explained what make a figure bad. According to the author, good and bad are not subjective but can be explained based on how human's visual perception works. Also, when design a figure, we need to think about who we are designing for. There are three problems that we can see in bad figures:  strictlyaesthetic - figures have bad design aesthetic, substantive - the data is incorrect or badly designed, areperceptual - figures mislead human's perception. The author explained the correlation between data visualization and human's perception by listing rules of how human perceives a figure. This includes Edge, Contrast and Color, Pre-attentive Search, Gestalt Rules, Proximity, Similarity, Connection, Continuity, Closure, Figure and Ground, Common Fate.

My thought after reading this chapter is that I can see some of the rule of human's perception of data visualization is similar to Graphic Design Principles. It is very fascinating that human perceive something as good or bad subconsciously. Therefore, this emphasizes a fact that design is not subjective. There are certain rules and principles for designers to create good design. I also like the examples that the author demonstrated. Some examples I could not have thought of as bad designs but when the author explained, it makes sense.

I would love to ask my classmates about how they define a "bad taste" in term of designing a figure. What makes them "tacky"?

2. Misleading axes on graphs

The Misleading axes on graphs discussed when data visualizations are misleading and sometimes used to conceal the real data by using the wrong axes set up. By giving detailed examples of common misleading graphs and how to correct it, the authors explained rules of how to set up axes. Some of the rules were Bar chart axes should include zero, Line graph axes need not include zero, When line graphs ought not include zero, Multiple axes on a single graph, An axis should not change scales midstream, and An axis should have something on it.

My thought after reading this article is I love how detailed the rule is. The examples are interesting because they are actually graphs that appeared in articles of big and well-known companies, online newspapers and magazines. This places a question of are those misleading charts were just badly designed or intentional to manipulate the information. This also makes me think of the influence that data visualization have on people and the importance of integrity in designing data visualization.

I would love to hear what my classmates think about this article and this question: Is it always necessary to be completely honest with the data or sometimes manipulating the graphs is for a good cause?

3. The Principle of Proportional Ink

This article explained a  rule for data visualization design, the principle of proportional ink. According to the authors, "The rule is very simple: when a shaded region is used to represent a numerical value, the area of that shaded region should be directly proportional to the corresponding value. In other words, the amount of ink used to indicate a value should be proportional to the value itself." In this article, authors led us through different type of charts and graphs to explain when the principle of proportional ink was violated.

My thought after reading this article is that I can see the connection between the principle of proportional ink and the previous article "Misleading axes on graphs". These rule goes together and makes sense of the whole theme. Again, this article used examples of misleading charts taken from well-known companies websites and magazines. This shows that it is really easy to create a misleading chart. Therefore, designers always have to keep in mind how to visualize true data.

I would love to see more examples of misleading charts that my classmates encountered in their everyday life/work.

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