See my work here: https://public.tableau.com/profile/mia7687#!/vizhome/PopularBabysName2011-2016/Dashboard2?publish=yes
Introduction
What’s the most popular baby’s name these years? Are there any relationships between different names and ethnicities? Which name is used by both male and female?
It’s always good to know people’s preferences and tastes to pick names for babies. By connecting name, gender and ethnicity, there are some stereotypes and exciting findings that we even didn’t notice before. For example, 90% of the name Angel belongs to boy, and also 90% of them are Hispanic. In my country’s culture, Angel is a girl’s name since we describe beautiful girls as an angel. However, in Spanish, angel was derived from the name of the heavenly creature (itself derived from the Greek word αγγελος (angelos) meaning “messenger,” and is a common male name.
Giving a name is always a tough and challenging job to do. This popular name’s visualization is handy for people who have this issue. By learning what’s the most popular names these years and their popularity trends, it will be easier to give a name with both uniqueness and universality.
What’s more, it will help foreigners to give themselves a common and proper name instead of Cherry, Bunny or Apple.
Inspiration:
Inspiration 1: Six Decades of the Most Popular For Girls, Step by Step
I love this map because it shows me the trend for the change of most popular name by year. It’s super bright and easy to understand. Some names might look old-fashion today, but very common in old people, and I could find the reason here. However, I could tell that this map is built on millions of data. My data is way smaller, and I need to find another way to visualize it.
Inspiration 2: Baby Name Trends
This is a visualization website to help people give names. It is pretty comprehensive with almost every kind of data for the baby’s name. It gave me some general thoughts, and also exclusive some visualization that already had.
Materials
- NYC Open Data : I found my raw data and downloaded it here.
- OpenRefine : I combine some same data with different names here, to make it more ready for Tableau.
- Tableau Public : All the Data visualization work is done here, and easy to share online.
Process
Data Selection and cleaning
At first, I did not have a clear direction of the topic. Through exploring the NYC Open Data’s popular data, I found this NYC popular baby’s name. Compared to other options like the crime, this one sounds more understandable and fun to me.
After choosing the data, I started to clean it to make it ready for Tableau. To be honest, it is quite good data without big issues. Only the names of babies had some capital problem. Like Jacob, there are two different rows with exactly the same data instead of the name Jacob and JACOB. Therefore, I made all names in the same format with the first capital alphabet and others lower case. Then, all the same data combined and ready to go next step.
Visualization
At this stage, I was struggling for how to explore something interesting. Two thoughts came up to my mind: 1. show the trends of the ten most popular names 2. Show the preference through different ethnicity.
I prefer the second thought because I was personally curious about it. Are there some names people would imagine the white face immediately? Lots of reasons made these stereotype, like the famous movie or movie star, and different culture. When I heard Jack, Leonardo‘s face in Titanic always showed up. However, I had some concern at the same time. Will it cause some issues about race? Is it right to get lots of stereotype？That’s why I gave up this idea at the beginning.
Then, I started to try the first thought. It went pretty well. I had some finding from my chart like Emily has a stable flow, but Olivia became popular suddenly in 2015. The problem here is that I didn’t see the meaning in this chart. What useful information or interesting finding people could get from it? I was confused, so I went back to the other idea.
I built the popular names’ combination of ethnicity and sorted by the total count from 2011-2016. It displayed as bar chart at first due to the disadvantages of pie chart professor Sula talked in class.
However, when I tried the pie chart(img 5), It looked more clear. I guess because It was more about the general proportion of different ethnicities, so the pie chart would give a great first impression of which ethnicity is the most group of this name. This might be the exception of the pie chart that professor discussed in class.
Something surprised me is that as an Asian, lots of preferences did match mine. The names with a bigger blue part are the name I or my Asian friends consider more. The names with only one ethnicity mostly are from their culture or language.
Like the name Chaya(img 6) which only be given by white people. Let’s see the origin of the name Chaya: Hebrew name meaning “life.” The name, Anglicized as Eve, is borne in the Bible by the first woman created by God, the “mother of all the living.”
Or the name Camila which is given mostly by Hispanic people: derived from the Latin Camilla (virgin of unblemished character).
What’s more, during this process, I found the names given by both male and female are unique and proper names for people who want a neutral name. Thus, I made a bar chart for these names and showed the proportion of both genders. Like the name Angel I mentioned on the beginning, it is interesting to know the difference thought through a different culture.
Results
Here is the complete visualization’s look(img 8). I put them together in one page, including the 10 most popular boy and girl babies’ names, the ethnicity proportion of different name, and the popular names with both genders. Personally speaking, the right part is more interesting for me. I couldn’t get some funny finding on the left part.
Reflections and Future Directions
I asked my friends’ feeling while switching my topic. They were curious more about the ethnicity part and the names for both genders. Also from the classmate feedback on today’s class, he kept scrolling down to see more about ethnicity which made me realize that part was more fun and meaningful. He told me that he did not think that part would have some racist issue, because that was based on the real data instead of something I made up, and he was interested in the cultural things behind the names like Angel and Camila.
For this work, I think I did better than the last one. At least I love the topic I chose. If I have more time, I would like to explore more interesting cultural knowledge behind these names and find a proper way to show them on the visualization. Or see the celebrities who have those names for both genders and make something fun.
One more thing I learned on this work, we should always be confident about ourselves. I viewed other classmate’s visualization, and I thought they all look so cool, colorful and fun. However, in class, they walked by my work and stopped, and say exactly the same sentence. I realized that when we learn and explore something totally new, we all don’t know the standard and don’t feel confident about it, but that’s fine, people are in the same situation.