Blog Post 3: Data Visualization

For my data visualization assignment, I chose to graph the rank of each popular girl’s name for every year on a line graph. I wanted to explore how the popularity of each name changes over the years, so I knew I wanted to use a line graph, with the years on the y-axis and either the number of babies with that name per year or the name’s rank in the list on the x-axis. I ended up choosing to graph the data by rank instead of count in order to emphasize the ways the names move by year through the rankings, switching places and “competing” with each other. Essentially, I wanted to focus more on the name’s popularity on the top 10 list rather than just the raw names per year data. I also thought graphing the names by ranking would make for an interesting looking graph.

To improve the presentation and clarity of the graph, I did a couple of things. First, I changed the colors of the lines to make sure each line was distinct and able to be followed by the viewer. I also reduced the line weight, label font size, and data point radius of the data and expanded the total width in order to create more negative space and make the graph look a little less cluttered. For easier use by the reader, I added a legend that allows the user to click each name to hide and unhide each line. This allows the user to compare two or three names, or to track one name’s progression through the rankings, without becoming confused or distracted by the other datasets. This interactive element also emphasizes another advantage of graphing by rank– even when looking at only one name, the viewer can still get a sense of the name’s comparative popularity. To emphasize the rank once again, I also increased the thickness of the horizontal gridlines to draw the viewer’s attention to each “step” of the ranking.

I thought the lecture in class was very interesting, particularly the discussion of bad or misleading graphs. Particularly in the Digital Humanities, effective and readable visualizations of data are super important. Since the topics of humanities projects can often seem dense, using a graph, infographic, or other data visualization to argue a point or advocate an idea can clarify information for a viewer who may not have the time to, for example, sit down and read a paper. Because of this, overly complex, hard-to-read, or misinforming graphs can cause huge problems for Digital Humanities projects, and for research in general.