Blog Post #4 - Information visualization & distant reading - Jack

  I really enjoyed exploring both of these chapters in this week’s reading. We start with Chapter 6 on Information Visualization, where Drucker explores how we visualize data and info with graphs and charts, and finding what makes most sense for what we’re trying to prove or show. If you’re trying to show patterns (which becomes a big part of the next chapter), you would collect the data in a table, but that table isn’t as easy to read and follow the data you’re showcasing than it would be if you put it into a graph (like a bar graph). She reminds us of all the different types of charts, like a pie chart, continuous graph, scatter plot, and tree map, and the goal of how even though you can fit data into any of them, you want to “understand how the information visualization creates an argument and then make use of the graphical format whose features serve your purpose” (Drucker, 90). Overall, you want the point you are trying to make to be proven by your visualization. However, data can also be used in visualization to exaggerate your point to make it appear that your evidence is overwhelming. She uses the “cockscomb” example that nurse and activist of the 19th century Florence Nightingale used to prove her point that more deaths occurred in those wounded in field hospitals than on battlefield, an argument she won due to the dramatic contrast. This reminded me of my past experiences in statistics classes, even simply using Google Spreadsheets to create charts with our data, and seeing how although every chart contains the same information, each can have a vast visual representation than others.

Chapter 7 looks at data mining, a term I’ve heard before but never truly known exactly what it means. Before reading, from my understanding, it was digging through tons of data and picking things out - which is kind of the case. True data mining is doing just that, extracting meaningful information in digital files, but also looking for patterns. We then dive into “distant reading”, which I had never heard of before, but it’s the idea that texts can be “read” at a scale that exceeds human capacity - which aligns with data mining. Programs of today can dig through texts, books, and any information online at incredible speeds and retrieve any information you need (especially relevant with AI of today). Yesterday, Today, Tomorrow was a perfect example that brought together not only the data mining part of this read, but the visualization as well. The website lets you look through the past of the pandemic, and hundreds of thousands of Tweets have been searched through to find patterns of emotions and our “collective feelings” during the pandemic. It really brought all of these ideas together of finding patterns and how to visualize them to a viewer and is such a cool idea to me. You can look through the months of 2020, and not only get to read how people were feeling as the pandemic progressed, but see the level of emotions each month - Joy, Confidence, Sadness, and Fear - and the percentage of those emotions shown across Twitter. Unfortunately, I couldn’t seem to figure out Six Degrees of Francis Bacon, which looks like another cool site to play around with to see patterns and connections between people(?) in the world.