Lab 7

Google Books’ Ngram Viewer is a word frequency visualization tool that allows specific words in literature to be tracked over time. In the above visualization, I chose to compare the frequency of the word “class” with the frequency of the word “race” used in literature from the beginning of the 19th century to the beginning of the 21st century. The blue line tracks the frequency of the word “class,” and the red line tracks the frequency of the word “race.”

Another similar word frequency analysis tool is Ben Schmidt’s Bookworm tool shown above. In this data visualization, I have chosen to track the same words (“class” and “race”) in literature throughout the same historical period, from the beginning of the 19th century to the beginning of the 21st century.

While these two data visualization tools are similar and I have chosen to track the frequency of the same words throughout literature from the same historical periods, these graphs do not appear to be identical. Google’s Ngram Viewer represents the frequencies of the usages of the words with percentages, whereas Schmidt’s Bookworm represents the frequencies of the usages of the words with “words per million.” Additionally, Google’s Ngram Viewer tracks the frequency of the words over time using increments of 20 years, and Schmidt’s Bookworm tracks the frequency of the words over time using increments of 10 years. It does help that the blue line in both graphs represents the word “class,” and the red/orange line in both graphs represents the word “race.” Despite the similarities, the differences are important to consider as they encourage the people interpreting these graphs to interpret the data differently because of how they are represented. A very interesting difference is the in way that both graphs begin at the begin of the 19th century. While Google’s Ngram Viewer shows “class” as used more frequently at the beginning of the 19th century, Schmidt’s Bookworm shows “race” as being used more frequently at the beginning of the 19th century. The difference in these two data visualization graphs could be accounted for because of the difference in corpora used to track the frequencies of these two words over time. Schmidt’s Bookworm is likely to have a slightly different selection of works that make up its corpora in comparison to Google’s Ngram Viewer. Schmidt’s Bookworm claims that it analyzes “hundreds of thousands of books.” While Google’s Ngram Viewer probably also analyzes hundreds of thousands of books, it might honestly analyze even more because of its capabilities to do so.

This data visualization from Lexos allows me to see the words that make up Topic 13, words used most frequently in one of the specific State of the Union addresses that I uploaded. “Mexico,” “mexican,” “texas,” and “war” seem to be used the most frequently; this is displayed by the larger font and the yellow and light green colors of the font. This data visualization allows me to see what topics were most important in this specific State of the Union address, which allows me to make a guess as to the time period that this State of the Union address was given. The advantage of this kind of visualization is that key words from the speech are highlighted, which gives the audience a brief understanding of the speech and the most important topics in American history at that time without them having to actually listen to, watch, or read the speech itself. However, the disadvantage of this kind of data visualization is that key concepts, topics, and ideas may not be conveyed to the audience. If this type of data visualization only highlights words that were used explicitly in the speeches, key concepts and ideas can often not be encapsulated in one word, so this kind of data visualization could potentially lead the audience to make false conclusions about a specific time in American history.

Without reading the text above and below Fred Benson’s data visualization, I would not know what the data visualization was representing, much less what to do or how to interact with the data visualization. Is it “intuitive” to use? I would say it is not intuitive, but the bolded text instructions directly above it help to provide some guidance as to how to interact with it. I don’t know if interacting with this network graph necessarily provides more or less information than other forms of data visualization do, but it definitely provides a different kind of information or information in a way that is different from the other forms of data visualization that we have studied. Network graphs focus on relationships–relationships between characters, authors, audiences, etc.

This pie chart shows the distribution of artists in terms of their home countries from the Tate Art Gallery in the UK. This chart shows which countries have artists featured in the gallery and the percentage of the art in the gallery represented by each country. Google Fusions is exceptionally versatile as it allows for data to be displayed in many different forms of visualization. We could use Google Fusions in our final class project in lots of different ways. Right now, because we’ve still yet to decide exactly what our project will be focusing on, it’s a bit hard to say exactly how we might use Google Fusions as a part of our project, but if I had to speculate, I would think that we could use Google Fusions to show different trends in the same data set(s).