Like many others, we realized that COVID 19 had hit minority and poorer neighborhoods across America the hardest. However, we wanted to support this hypothesis by analyzing empirical evidence scraped from many sources on the internet.
What it does
Scraping multiple COVID or Demographics related sites, we piece together empirical evidence to present a cohesive narrative of COVID's effect on the poor/minorities. In order to support our main thesis, we produced multiple graphs showing the relations between race, income, and COVID-19 vaccination rates in America.
How we built it
Using Web scraping and statistical technologies such as Selenium and Pandas, we collected demographics / COVID information on the population of Chicago.
This data was put into numpy's graphing features to create intuitive and comprehensive graphs.
The website was built using hugo, and timelines were embedded onto it using TimeLineJS.
Challenges we ran into
Organization and direction was a huge challenge. We had a lot of data but had to figure out a story we wanted to tell using said data.
Accomplishments that we're proud of
We're proud that we were able to effectively present our main thesis using graphs made from empirical evidence that we acquired using scraping techniques.
What we learned
We learned how to divide works amongst us in a way where everyone could contribute to the project.
What's next for COVID 19 Vaccination Disparities Among Races in Chicago
A more dynamic website and live tracking of how minorities are affected by COVID.