I chose to create a heatmap of the popular names because I felt it conveyed most of the information a person would find useful if they were to try to interpret the dataset. The heatmap makes it easy to distinguish the most popular names from the least popular in the dataset, with each name shown in an associated color. What I like most about it is that it compiles all of the years from 2001-2010 and puts them together in an easy-to-distinguish map where you can see which names over that time period were the most popular vs. the least popular in the dataset. I chose to contrast the heatmap colors differently, instead of a spectrum of blue, as I feel as if the sameness of the colors could make it hard for people to distinguish the ordinal popularity of the baby names. The graph’s biggest downfall is that it doesn’t give you an actual count of how many names there actually were in those years and instead is strictly concerned with general popularity. There is also a weird ranking score it gives each name, in which I believe the graph is trying to once again tell you one name is more popular than the other, but there doesn’t seem to be complete for some reason. Nonetheless, I believe the heatmap does a fine job of conveying the information people would want to know from the dataset. I think there are always trade-offs in DH when visualizing data, and Lin’s lecture certainly opened my eyes to the idea that sometimes not everything can be explained in one graph. But there are some graphs that are better than others for conveying specific information.
Wow Eli. I love the color choices you made with this graph. It really emphasizes the distribution of names well and is a creative alternative to a simple bar graph. I agree, it would have been nice to be given a count, but overall it looks fantastic, and the gradient scale helps mitigate that problem. Thanks for putting together such a fun and visually pleasing assignment!