Check out my midterm exam project!

Introduction

Welcome to my midterm exam webpage! Here is a map I created in ArcGIS Online using artist data for artists with their work at Tate that were born between 1950-1969. The locations on the map represent the place of birth of each of the artists. The symbol shape shows the artist’s gender and the symbol color represents the artist’s year of birth. In this project, I hoped to visualize where the majority of artists in the dataset were from as well as to see if there were any differences in the proportion of male and female artists. Additionally, I hoped to see if there were any differences in the number of artists born per year in the dataset. As you can see, there are many more male artists than female artists, and the biggest clump of artists were born in the UK, where Tate is located.

Sources

For this project, I used the Tate Artist Data 1950-1969 dataset, which I downloaded as a CSV file. After downloading, I cleaned the data in R/RStudio by removing any rows that were missing values in the gender or placeOfBirth columns. I then converted the yearOfBirth column to a character variable and filtered the dataset to create two separate datasets, one for males and one for females, and downloaded these new datasets as CSV files.

Processes

After cleaning my data using R/RStudio and creating two separate datasets, I uploaded both datasets into ArcGIS Online as separate layers. I decided to use mapping and ArcGIS because I thought it would be best to visualize large amounts of location based data and I liked how I could visually show two additional variables. I also liked how the other variables and the URL to each artist’s webpage on the Tate website could be easily accessed by clicking on one of the individuals.

I chose to upload the data as two separate layers so I could use symbology to display two different categorical variables at the same time. When I tried to upload the data as one layer, yearOfBirth automatically switched to a numerical variable and used size to show the year the artists were born in. Since I uploaded the male and female data separately, I could assign all males a square symbol and all females a circle symbol. I then color coded based on yearOfBirth, making sure to have the same colors representing each year in each layer.

I used WordPress for my midterm subdomain website largely because it is what I am most comfortable with. I decided to keep the visuals of my midterm subdomain website very basic so that my embedded map would be the main focus. In my embed, I tried to have the initial view be that of a zoomed out version of the whole world so that viewers could see the overall trends right away and then decide for themselves where they wanted to zoom in and explore first. I chose to put the legend as a pop-up panel on the left side of my embedded map so viewers can choose to interact with that additional context when they wanted to but not have the map be crowded with the legend from the start. I also used a fairly basic basemap so that the embedded map would not seem overly busy or crowded.

By looking at the Tate artist data through this approach, we can see fairly easily that there are far more male artists born in this timeframe than female artists. We can also see that there are no other gender identities represented in this dataset. We can see that the UK was the most frequent birthplace for the artists in this dataset, which makes sense because Tate has the national collection of British art.

This approach relates to Digital Arts and Humanities as opposed to data science more generally because it encourages viewers to take a more critical, humanist lens while interacting with the data. It makes viewers ask the questions “why are there more male than female artists represented?” “what does the spatial distribution of places of birth suggest about the backgrounds of the artists at Tate?” “what do these trends suggest about the artist diversity and selection methods at Tate?” “what implications does this have for the types of art displayed?”. I also think that linking the artist profile on the Tate webpage allows for the connection between various types of data that is characteristic of DGAH projects.

A potential pitfall of my approach is that there are so many artist years represented that it is hard to visualize the differences in frequency between each birth year. If I were to redo the project, I think I would either focus on visualizing just trends in gender or just trends in birth year. An alternative would be creating bins for a section of years, such as 1950-1954, 1955-1959, and so on to make the trends easier to see. Another pitfall of this approach is that it uses data that reinforces the gender binary. While I avoided using the stereotypical pink and blue symbols for females and males, my work could still be critiqued for this reason from a data feminism lens. Despite this, I hope my work can help to emphasize areas where diversity is lacking within this group of artists.