I chose to use a line graph because I thought it would be the best way to show trends in baby names over time. To make the graph easier to read, I gave each name a different color. This brought up a challenge about how to represent gender. I didn’t want people to think the colors were too connected to gender, so I used cooler blue tones for male names and warmer red tones for female names. I also decided to split the graph into two separate charts—one for males and one for females—because combining them would have made the lines too hard to follow.
I believe my graphs do a good job of showing the idea of Digital Humanities (DH) by turning raw data about Australian baby names into a clear, easy-to-understand visual. These graphs don’t just show numbers—they tell a story. They show the debates parents have when choosing names and the common trends they follow. Names that might seem “common” or “ordinary” are more than just numbers in a dataset; they are given color and meaning. For example, people might start to think about a name like Jack differently when they see its slow decline in popularity since 2006. Similarly, people named Joshua might see that in 2001, many parents were going back and forth on names just like they did before choosing Joshua.
Reflecting on Class Lecture
Reflecting on Lin’s lecture, I thought a lot about her point that data visualization helps people understand data quickly. It makes it easier to absorb information compared to other ways of presenting data. By turning data into a visual, it becomes easier to understand in a way that doesn’t exhaust the viewer (consumer).
4 thoughts on “Lab 3: Visualizing Baby Name Trends”
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I really like how you choose to use the line graph to represent this data- since we are trying to look at trends, lines are a really good choice! I like how you attempted to represent the different names with color- some of them do tend to merge with each other as you’re following the data since they are similar hues, but it’ still readable since this isn’t a huge dataset. I would also recommend changing the values on the y axis to start closer to the data numbers so there isn’t so much white space and instead more concentrated on the data. Overall great job!.
Nice decision to separate the two graph into gender. Doubling the number of names on one graph would be visually overwhelming. I also appreciate your choice to use line graphs for the data. Having the count, name, and ranking overtime is a lot of information in a visually compatible format. Good choice!
Hi! Your data visualization is fascinating. I really appreciated how you described the graphs as more than just numbers—they tell a story. That perspective on data visualization is truly unique, and I completely agree with you. I also feel that using a line graph is an excellent choice for presenting this data, and your consideration of gender-stereotyped colors adds a thoughtful layer to the presentation. One suggestion, though, is that some of the names appear to overlap, which makes it a bit challenging to distinguish between them.
Hello! I like that you thought about the readings when it came to choosing the colors for your visualization. I think that the color separation is a really good idea, but I do wish the names were not on top of each other (I experienced problems with this in my graphs as well). I like the point that you brought up about how visualization makes it easier for the consumer. I’ve been thinking about data visualization as a way to make it easier for the person doing the actual research/project so I’m glad you brought up this point.