Lab 7

Step 2: Google NGrams/Ben Schmidt

I chose to compare the names of two flowers, a rose and a lily. As you can see, both graphs produced similar results, at least at first glance, with rose having a much higher frequency throughout time than lily. Of course, the information given is slightly different. Ben Schmidt’s Bookworm displays publication years 1820-1920, while Google displays 1800-2000. Also, Google shows ngrams as a percentage of their corpus, while Bookworm’s y-axis is words per million. I find words per million slightly easier to visualize because it’s a specific number, but I still don’t truly have a good grasp of how big or small that is admittedly. The differences in the axes might be important because it gives us different results, though the visualizations are similar. I also don’t know exactly what books are in these corpuses. I’m sure they have some differences. One thing that might be good to include in a line graph from a humanist perspective is some addition that displays changes in symbols over time, since flowers are generally symbolic of something. I imagine that a humanist line graph a la Drucker would be quite non linear, like how she breaks up the anxiety graph.

Step 3: Lexos

This topic cloud comes from the State of the Union address. I find it to be a pretty unusual one, as it includes many words related to terrorism, national security, and military, but the biggest word in the cloud is applause. It comes from a topic document fed into Lexos, which generates a word cloud from each topic. It’s easy to tell in this visualization which words have the highest frequency in a topic, since they’re enlarged and color-coded. However, it also tempts us to label the topic with the largest word, but that might not always be a good thing to do, as is the case here. I think word clouds can be considered humanistic because they allow for more loose interpretation than a rigid bar graph or line graph, but like anything, it depends on how it’s used. There’s always more we can say about a cloud than what we can show here, like what documents contribute most to this graph or what the political climate was like when this topic was most contributed to (it feels like a recent one with the mention of Saddam and Al Qaida, but I could be surprised).

Step 4: Network Graph

It took me a little bit of reading and playing around to figure out what this graph was for, but I think I get it now. It links authors by the book that inspires them the most. For example, several authors cite Ulysses as an inspiration, so they’re linked together with others who share Ulysses as an inspiration too. I don’t think it’s intuitive to tell what this graph is about by itself, but if you read the top paragraph, it makes it much more clear. I don’t know if the network graph contributed to the information that I got further down. I can’t say that it gave me more or less. It was simply different information. One thing I’d like to know more about after looking at this is why Lolita is the top chosen book. What about it inspires so many people?

Step 5: Google Fusions

This pie chart displays the birth country of some list of artists, given a larger piece of the pie based on the number of authors born in some country. Google Fusions generates tables, charts, and other data visualizations based on data fed into it. It could be useful in our class if we want to compile some set of data into a graph or chart. One thing that I really like is the mapping tool, where it displays countries of birth on Google Maps. I couldn’t get it to work at this time, but in the future, a map could be a neat feature to add to our course site depending on what we do. I think it would be a good thing to do as a helpful visualization tool. The visualization of the disease map from Drucker comes to mind though, where she talks about dots on a map as individuals, but I feel that’s a problem that should be solved in another place. We’re looking at a macro scale, after all.