Visualizing MoMA’s Exhibition History
The Museum of Modern Art, known as MoMA, is an art museum located in New York City. MoMA is one of the world’s largest and most important museums in collecting modern and contemporary visual arts, architecture, design, films, and new media. This project's motivation was to visualize and help others better understand MoMA’s Exhibition by analyzing MoMA’s open datasets available at GitHub.
Process
The dataset I created for this project was uploaded and maintaining by MoMA at GitHub. There are two research datasets available. The first one represents all of the works that have been accessioned into MoMA’s collection and cataloged in MoMA’s database, which contains 138,151 records so far. It includes basic metadata for each work, including title, artist, date made, medium, dimensions, and date acquired by the Museum. The second dataset represents all the artists who have work in MoMA’s collection and have been cataloged in MoMA’s database, which contains 15,222 records so far. It includes basic metadata for each artist, including name, nationality, gender, birth year, death year, Wiki QID, and so on. Once I downloaded two .csv files from GitHub, I used Microsoft Excel to clean up the datasets, removed some data, and fixed some inconsistencies. Then I uploaded the updated datasets to Tableau, an interactive data visualization software, for visualizing the datasets.
Description of some of the columns of the datasets I am interested in:
- Title: The title of the artwork.
- Artist: The name of the artist who created the artwork.
- Nationality: The nationality of the artist.
- BeginDate: The year in which the artist was born.
- EndDate: The year in which the artist died.
- Gender : The gender of the artist.
- Date: The date that the artwork was created.
- Department: The department inside MoMA to which the artwork belongs.
Beta Release
The main issue with the beta version is that the interface is not interactive enough. I decided to explore more ways and other tools to increase the interface’s dynamics and improve the visual design and layout.
Testing
I conducted three user sessions. Overall, the participants found the visualization was interesting and easy to understand. However, Participant 2 had more trouble than other participants completing the tasks because they were not aware of the second tab. Having two separate tabs seemed not really work well. The participant mentioned it would be great if the visualization allows users to scroll horizontally or vertically instead of having two tabs. Two of the participants mentioned that they would like to see more information in this visualization.
Improvements
- Add more understandable labels for the second tab, or combine two tabs into one larger visualization.
- Rearrange the graphics.
- Add more information the users wanted to know.
- Improve the visual design
- Make the visualization easy to share.
Final Release
I chose to focus on four graphs after analyzing the datasets: artwork acquisition per year, artwork acquisition by nationality, artwork acquisition by departments (seven categories of artworks), and artwork acquisition by artists’ gender.
- Artworks acquisitions per year, from 1929 to the present.
2. Artworks acquisition by nationality. Here I decided to use a stacked column chart to visualize MoMA’s artwork acquisitions by nationalities. MoMA’s collections were created by artists from a total of 65 countries, and the chart only shows the top ten of the nationalities.
3. Artworks acquisition by departments (seven categories of artworks, which are Drawings & Prints, Architecture & Design, Painting & Sculpture, Photography, Fluxus Collection, Media & Performance, and Film).
A word cloud visualization.
4. Artworks acquisition by artists’ gender.
User guide
My completed visualization is about visualizing artworks acquisition of The Museum of Modern Art (MoMA). There are four categories covered in this visualization, artworks acquisition per year, artworks acquisition by nationality, artworks acquisition by departments (seven categories of artworks), and artworks acquisition by artists’ gender. All of the graphs can be filtered using the two filters in the center of the screen, one is a timeline filter, and the other one is a filter by department. Timeline one is a standard timeline that allows users to find and filter a specific time frame. The other one is also a word cluster visualization. Users can click on one of the categories, and it will filter out any other categories for other visualizations and only show the category user clicked on.
Here is the final visualization.
Here is a video demo.
