For our group, we were given the data sets that correspond to MoMa’s collection of artworks. The data includes the artists name, artwork, the age of the artist, the gender of the artists and the nationality of the artist listed. One of our groups main focus points is to explore the relationship between the artists gender and the time period in which they were working. I chose to use Google Fusion Tables to create a data visualization describing this discretion between male and female artists. I used a bar chart to represent this data, showing how there was a total of 1,635.369 female artists working in the 1930’s that are present within the collection. In opposition to a total of 3,612.951 males within the collection.
As you can see by the alluvial graph there is a a large gap between the male artists kept in the collection and the female artists obtained. Not only can we see a disproportionate difference between male and female artists in the collection, but we can see that there is a higher concentration of women artists working in the late 90’s than any other period. Furthermore, we see instances where gender is unclassifiable due to collaboration.
This data visualization allows a more direct visual understanding of the data represented within the spread sheets. We are able to see a physical transition throughout 1943 to 2012 as to the gender of artists collected. With this information we are able to infer our own ideas about what this information can mean. Without a visually intuitive framework, such hypothesis would be difficult to draw.
Given that I am also in a group working on the MoMA dataset, I found your visualization and analysis very interesting! We too noticed a large difference between the number of artworks from male artists and number of artworks from female artists but our data visualizations didn’t show the increase of female artists overtime. Excellent job!