I choose to create a data visualization pertaining to the Museum of Modern Art (MOMA) dataset which I was assigned to in my group. The MOMA dataset contains numerous files regarding artists and their artworks. I specifically chose to review the artist dataset and these files contain information that include where the artist was born, their nationality, what artworks are in the museum, their birth and death.
For my data visualization I chose to represent the artist nationalities. I created this bubble chart in order to clearly show the various nationalities and their representative sizes in the MOMA collection. I used the tableau application and selected all of the nationalities of the artists to create this chart. I thought this would be useful as the research question I proposed to my group was “Over the past 50 years, in what was has the museum expanded and incorporated non-western artworks into is collection in order to broaden the scope on a global perspective?”. Supporting the assumed nature of my question, it is clear that American artists overpower all other nationalities. Furthermore, the bubbles that are larger than others encompass countries of the Western world such as Germany, France and Great Britain.
As one scrolls directly on to each individual bubble, a pop up appears showing the name of the nationality as well as the number of records. It is interesting to note that the second largest bubble with 2446 records has no name for nationality. This aids me in my research as it shows that there are many artists in the dataset who do not have a nationality recorded. This suggests that I need to take out these artists out of my dataset or manually enter their nationalities in order to get a more representative graph.
Through this graph, one can clearly see nationalities that have been barely represented through the museums collection. This is evidenced by the majority as exemplified by countries such as Malaysia, Costa Rica, and Cameroon only having 1-2 artworks displayed in the museum. It is also interesting to see the integration of non western artworks as countries such as Japan, Brazil and Argentina that have a larger concentration of artworks compared to other non western nationalities.
Another issue that shows within this graph is the idea that only the larger represented nationalities have their names on display while the less represented do not. However, overall this bubble chart is helpful in determining data that needs to be cleaned as well as helping the narrative of my research question.
That visualization is great! I usually don’t like bubble charts because I feel as if they trick my eye, and I might not be that good at noticing which bubbles are actually bigger, but this one was easy to understand. I think you did a good job of choosing a blue hue, a color that humans tend to like a lot (from what I’ve read). However, I wonder why sone bubbles that are close to the size of others do not have any words on them. Might be because they are not a certain size or show the unimportance of smaller artists. Overall, still interesting to see a dominance in American artists over others.
Great job Sarah, this is an excellent way to display this particular data. And I think it is a bit of relief that we would be able to simply eliminate that big chunk of our data without a nationality. Makes the remaining amount of data seem just a little more manageable.
Great job, Sarah! You very effectively show that your biggest chunk of data comes from American artists. Hmm, how to show change over time? Perhaps a stacked area chart?