Description

The above sketch "This Just In," juxtaposes two datasets that are frequently used: The New York Times top stories of the day and the Center for Disease Control's API on coronavirus numbers in the United States. Enter a month and day from 2020 in the top left-hand corner to see what Americans were paying attention to that day. Hint: You'll start seeing COVID numbers from March 18th (3/18) and beyond.

Design Process

I began with quite a few ideas for this project. Since I was new to working with APIs, my main goal the first few days was to become familiar with querying. I did this through multiple sketches, when my idea was still unclear:

I pulled an NYT API first just to mess with the top headlines of the day:

I then found the CDC's API which I thought would be an interesting visualization.

However, with both these ideas I recognized that I wasn't doing anything particularly novel or groundbreaking. There are several COVID-related data visualizations and I wasn't exactly sure how to move about creatively with the NYT API. I briefly toyed with the idea of having various word clouds derived from different news sources to show the disparity in the media channels Americans consume (from left to right leaning). This idea unfortunately ended up not panning out as I needed to pay for different news.apis in order to be able to grab them from the browser... #studentlife.

Going off of this theme of comparison I had, I decided to merge the two APIs I took interest in to create "This Just In".

Reflections

"This Just In" aligns most closely to the fifth principle of data feminism: embracing pluralism. Data feminism insists that the most complete knowledge comes from synthesizing multiple perspectives. This translation device recontextualizes the two APIs they sourced from by revealing a new question: "What are we actually paying attention to"? A few things to note here: I recognize that I am pulling from a predominantly left-leaning, American-centric news source. Additionally, the CDC's data fails to account for other countries besides the U.S. In doing so, this translation device is only telling an American story. Given additional time and resources, I would like to source from various media channels and COVID numbers from around the globe.