Reading #3

Computational Information Design

By Ben Fry

The ability to collect, store, and manage data is increasing quickly, but our ability to understand it remains constant. In an attempt to gain better understanding of data, fields such as information visualization, data mining and graphic design are employed, each solving an isolated part of the specific problem, but failing in a broader sense: there are too many unsolved problems in the visualization of complex data. As a solution, this dissertation proposes that the individual fields be brought together as part of a singular process titled Computational Information Design.

→ read chapter 3 (pp. 33–50) of his dissertation


“The issue is about diagrams that are accurate, versus those that are understandable”

  • In the field of information visualization, is legibility more important than accuracy? Or vice versa? How does someone design for people who are unfamiliar with the subject matter while remaining true to the data?

  • Do conscious design choices make infographics less ethical? Is untouched data the only truly unbiased way to present information? At what point / in what situation does designing information become somehow more ethical or true than if it weren’t touched by human hands at all? Doesn’t any conscious choice in presenting data inject the bias of the designer?

“a designer cannot address every single design task in an era of always-on internet connections and ever- changing information.”

  • How is the pace of the digital age affecting information design? Are static visualizations a thing of the past? Can a static visualization capture the same authenticity as one that changes and adapts over time?

1. When defining the term “pre-attentive”, he mentions the term “active viewing”, implying that there are at least two different kinds of ways to view/look at something. What is the difference between active and passive (assumed other way of viewing)?

2. When is it more important for the viewer to be able to learn a pattern, ex. page 43 versus to be able to understand the chart immediately or at first glance?

3. On technology and its effects on visualization, does this possibly mean that only those who have grown up around these kinds of visuals generated by newer technology will understand? For example, are the images from pg 49 intuitive for most people?

The Computational Designer 3:Q

“The computational designer as more of a choreographer, whose task is to order and move the elements of a continually changing space. “

  1. This is essentially the core principles of dynamic or conditional design practices, rather than providing solutions to design problems you provide functions, these functions can change and evolve due to the interaction with them. sort of like a-b testing but in real-time. All of this ideology in relationship to data visualization seems mute. When we have massive neural networks that can sift, compile, organize information to create a visualization, interactions and/or spaces using datasets that are constantly changing. is the process of automatic design still a “bad” word?

  2. Is it necessary to “visualize” information for an audience if the data is constantly changing, could you argue that Facebook itself is data visualization at its best? Being that it looks at a large dataset and constructs a feed of information that is “most” relevant to its viewer. There are issues with this highlighted by the current “bubble” language.

  3. But aside from its adverse effects, could this method of data visualization be a step above the general understanding of data visualization?

Response to Ben Fry's Computational Information Design

"...the issue is not that the visual design should be 'prettier'. Rather, that the approach of the visual designer solves many common problems in typical information visualization," (40).

1) Are there any problems that "visual designers" solve in typical information visualization that are not related to clarity of information communication?

2) Assuming that Fry is referencing graphic designers as "visual designers," what separates someone with graphic design skills doing graphic design from a graphic designer?

"... the diagram itself shows that the representation of the data need not be visually intricate or over-designed in order to convey the data. The latter point is important to be consider with the emphasis on graphic design—that the design is not a matter of fancy graphics or visual tricks, rather that it is a means to an end for creating the cleanest, most understandable diagram possible," (43).

3) Is there ever an appropriate time to use fancy graphics or visual tricks for graphic design/information visualization?

4) What are the limits or guidelines to data mining?

Reading 3

Computational Information Design ~ Ben Fry 2004

  1. How does the computational designer select what information is included in a visualization without being reductive, too complex, or inserting their own bias?

  2. What innate biologic/homosapien conditions exist that computational designers should be especially aware of?

  3. At what point does the data or visualization technique become too abstract, layered, or artistic that the lay person can no longer understand it? Is it the amount of variables, colors, perspective (3-D), interactivity?


Reading Response

On Ben Fry's Computational Information Design Dissertation

The following questions are reflections of thoughts that occurred while reading Ben Fry's Dissertation.

  1. How do we (designers) select ideas to communicate? Are there ideas that we shouldn't communicate?
  2. In the context of the course, we have addressed data visualization as a method for aiding others to understand X data, yet the importance to utilize it as a tool for the understanding of the data from the designers perspective seems highly important.
  3. Is it okay as a data viz designer to have a bias? What role does this take to communicate an idea? are there any sorts guidelines, parameters frameworks, laws etc. to follow to be able to communicate information correctly?
  4. How can data viz actually tell the "truth" about something when there are a multiplicity of variables to be discussed about any given subject.