For this lab, I had to transform the data of the 10 most popular baby names in New Zealand from 2001 to 2010 from a CSV file into graph form. After much difficulty, I found that the best way to visualize the data provided would be by using two graphs, one for the male baby names and one for the female baby names. The x-axis shows the year the baby names correlate to, with the y-axis showing the ranks 1–10, starting from the bottom to the top. The default diameter of the bubbles was set at 15, but with the bubbles displaying the baby name and count, I had to change the diameter to 50, or else it would’ve been illegible. While I wanted to experiment with the colors of the bubbles to get them to correlate by name, I was having difficulties. Despite having the option on the customization tab to set each name to a specific color, the bubbles would not update. Another difficulty I ran into was getting the x and y values to clearly show. I could clearly show the x-axis by setting the y-axis to start at 0, but I couldn’t do the same for the y-axis, as it made the graph only show the 2001 values.
One of my main takeaways from Lin’s lecture was finding and showing the stories within the data. Looking at the raw CSV file, you can see that a majority of the names throughout the years in the data are the same, with just the rank changing. But, in order for the reader to notice that, they will have to scroll back and forth, remembering whether the same names reappear. This brings me to how my two graphs relate to the digital humanities. There are simple ways one can store data and give access to the public, but why not make the data better? I’m not saying you have to do your own research to back up the data or expand on it, but you could use technology to enhance the data in a way that allows the public to better understand its significance or point. Therefore, while looking at a CSV file may be boring and seem like a lot of work, converting the data into a much easier-to-read, straight-to-the-point visual graph is more appealing and interesting to the eyes.
4 thoughts on “Data Visualization”
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This is a brilliant way to visualize the names! What led you to decide on name counts instead of percentages? I can not see it from the graph, but was there a y-axis title? I was wondering how others might be able to interpret this visualization without having to read your descriptions. Overall, I think the visualization clearly layout the top to bottom names.
I really like this way to visualize the count for the baby names, and I certainly agree that if you are choosing to show every single data point you definitely need more that one graph. Keeping them next to each other with the count also shows how different the distribution is between male and female names. One thing I think could be improved on is that it this display is a little noisy with all the gray data circles on the screen. I was having a hard time changing some of these things myself, so I sadly don’t have any concrete things to point you toward.
I think that this is a really good way to present the rankings for the baby names in a readable fashion. I think it was a good idea to split the charts by gender, as that made it much easier to read. It’s unfortunate that you couldn’t figure out how to individually change the color for the data circles, because that would have helped the readability of this graph a lot. Other than that, great job!
I really like this visualization! This is definitely easy to read and very straightforward. I only have a couple critiques. In my opinion, I think the highest ranking names should be at the top, because that’s where my eyes generally land first. Also, I think that changing the color of the #1 name for each year would be a good way to draw the viewer’s attention directly to the top name for each gender and each year.