I chose to visualize the most popular baby names in each year of New Zealand from 2001 to 2010. My goal was to show how the popularity of the most used names at the time changed across a period of time and to make year-to-year trends easy to identify. I found the easiest and quickest way to do this is to create separate bar graphs for each baby name, displaying the number of babies with that name throughout each year of the ten-year span. I decided to use bar graphs because they are best suited for comparing values across a time period without much visual clutter due to overlap.
Something that this visualization does well is that you can clearly tell which years a particular name peaked in popularity, and you can compare it with other popular names across the same time span. This would have been way harder had I put all that data into a single chart. What holds this visualization back is that the years in the x-axis overlap with each other, adding unnecessary clutter on the screen and making it difficult to determine which year correlates with which bar. In my second, revised chart, I resized it, increasing the pixel length and width. In turn, it made the values on both the x and y axes much more legible, allowing for greater pinpoint accuracy from first glance. Although the increase in size made the graphs harder to read, this can easily be counteracted by zooming in.
These changes stemmed from Lin’s lecture on Thursday when we learned the importance of making data visualization easy to interpret. Data visualization can take many structures, ranging from simple, like bar graphs, to complex, like Minard’s visualization of Napoleon’s 1812 March; however, after a solid couple minutes of looking at them, you should be able to determine exactly what is being shown.
In terms of digital humanities, this visualization uses numerical data to reveal cultural trends in baby name popularity. By presenting the data clearly, it made a large dataset more meaningful and interpretable to a larger audience.