Data Visualization

The project requires students to formulate a data analysis and presentation goal and to generate (1) a series of “data questions”; (2) corresponding data transformations and models; and (3) a set of initial sketches of the associated visual representations.

mottos in the course is that data visualization is not only about visual representation, but also about asking good questions and generating appropriate information from the data

I do not have a definite answer to these questions, but I can’t help but notice that we, as a community, may be at fault here. With our obsession with whether a pie chart is “better“ than a bar chart, is it possible that we have lost sight of what actually really matters? Let me be blunt: it does not really matter how pretty a visualization is and whether it uses the latest technology or the fanciest graph. These are nice things to have, for sure. But at the end of the day, what really matters is thinking. Appropriate and effective thinking.

I believe there are two distinct phenomena in this space that often get conflated: novelty and something I’ll tentatively call cognitive incongruence.

While, for sure many representations are confusing only at first sight and have a learning curve, it is also clear to me that some representations are confusing no matter how many times you use them

If we set aside the clear case of visual representations that are confusing only at first because they are novel, it would be interesting to understand better what makes some graphs confusing even after multiple exposures. It seems to me there could be interesting insights to gain from such an exploration.

An example of the second case is the burning embers I mentioned above. To me, they look a lot like bar charts, so when I see them, I try to read them as bar charts, which is the wrong mental model for them. A similar problem exists with connected scatter plots. I want to read them as line charts, but this is the wrong way to read them, so I get confused.

One hypothesis I have is that they evoke the wrong analogies, either because there is a mismatch between the visual representation and the information they represent or because there is a clash with mental models of similar representations we have acquired in the past.

The reason is that by making the message explicit at the design stage of visualization, there are many choices one can make to facilitate the communication of that message (Cole has some great ones in her book). Without a specific communicative intent, we are left with this abstract idea of using the “right” visualization for the data we have, which is not particularly effective

great

The reason is that by making the message explicit at the design stage of visualization, there are many choices one can make to facilitate the communication of that message (Cole has some great ones in her book). Without a specific communicative intent, we are left with this abstract idea of using the “right” visualization for the data we have, which is not particularly effective.