Week 8.

This week, we continued our discussion about data visualization. Nathan Yau in his article, “Representing Data”, provides a comprehensive text about the variety of ways to display data, as well as their strengths and weaknesses. Data visualization involves a variety of steps and choices. Yau’s analogy related cooking to data visualization. Like how a chef picks ingredients, prepares them, and combines them to create a meal, data visualizers make specific and intentional decisions about how to encode data, choose the best graph, and pick appropriate colors and fonts. Software can play a big part in many of these decisions, but there are still many ways that the information can be tailored to make personalized choices that best represent the data. Understanding the many, many aspects of visualization and how they can be combined and changed allows you to use the software in the most effective way, rather than letting it take the reins on the process.

Like cooking, visualization, depends on the skillset of the creator. Knowledge is power in this situation. Those who are knowledgeable about the process and ingredients and invest time and energy into learning will produce better results. Alternatively, Yau mentions that someone who relies too heavily on the computer to figure it out might end up with a less coherent or successful outcome.

Data visualization incorporates four main components: the data itself, visual cues, scale, and context. Data is the foundation of the project, but the other aspects relate to how the data is interpreted and understood. A visual cue is the way that data is mapped out making sure that the core of the data is not lost during the translation from the numbers to the visuals. The choice of visual cues relates to the purpose of the visualization and how the reader will perceive shapes, sizes, and shades. 

It’s easy to lead people astray with data, and Yau suggests many ways that mapping data can lead people astray and provides examples of how to show your data in a clear way. For example I really liked his section on color-blindness and how many graphs are less accessible or difficult to read for people who struggle to differentiate between colors. Yau’s article would be really helpful for anyone who hasn’t had a lot of experience with graphs, which frankly, is most art historians. It’s not quite a recipe but Yau’s article clearly spells out the ingredients that you can use to create a successful dish. 

The Software Studies Initiative was an interesting read, and the way that they highlighted different Data Visualization projects was cool to see all the ways that this is being used. I’m not sure I fully understand the academic potential of these, but I’m sure someone is using ImagePlot for something! The tools seem more geared for the museum sphere, and I can see curators and archivists using these as another way to show art or see connections. Having a program that could organize saturation or hue by year would be an interesting way to look at artists’ phases. Like Picasso has a blue period, it would be really easy for scholars to see similar trends or inclinations in an artist’s corpus. Without this technology, you would have to create your own graph, but a computer could do this with greater efficiency without the flaws of human eyes. Maybe there was a short green phase before the blue phase that scholars haven’t recognized because they’ve been seen as being “blue enough” by human eyes. 

When I was creating my WordCloud, I used Chip Colwell’s article, “Curating Secrets: Repatriation, Knowledge Flows, and Museum Power Structures”. I thought about many of the elements that Yau brings up in his article. I chose the shield form, because it wasn’t a distracting motif but I also felt like it related to the themes of cultural heritage protection that the article discusses. I also used a blue gradient for the text since blue seemed like a safe color that could be easily differentiated, rather than some of the other default options. I couldn’t figure out how to add a title, which is one of the suggestions that Yau says can add clarity for the reader. I also chose different fonts, a serif, a sans serif, and a monospace. I hope this adds clarity through different textures. 


1 comment

  1. I agree with your statement, ” The Software Studies Initiative was an interesting read and the way that they highlighted different Data Visualization projects was cool to see all the ways that this is being used.” Data Visualization allowed them to present their data effectively. I also like using Word Cloud because it is a helpful tool that maps Word for audiences. I like your image and the way you present color and font.

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