An Exploratory Analysis of COVID-19 in Chicago
By: Yianni Mercer
Tags: Data Analysis, Data Visualization, Chicago, Data Science, Analysis

Photo by Pedro Lastra on Unsplash

It’s going on nearly three weeks now since I first developed COVID-19 symptoms. Don't worry, as far as I know I’m healthy and have recovered perfectly fine from this virus. While I was fortunate enough to not only recover, but to have relatively mild symptoms, not everyone else was. From severe respiratory problems, to being hospitalized, and sadly, dying. The COVID-19 Global Pandemic has taken away nearly everything that some people took for granite. Concerts, eating out, simple things like going to the movies, playing at a park, all were taken from us.

Like I said before, I was fortunate, extremely fortunate. I didn’t want to take my time in quarantine for granite. Give this post a read if you’re interested in everything I was able to do while being stuck in my room.

Project Intentions

This project aimed to analyze COVID-19’s effect on the City of Chicago through different categories of citizens, such as age groups, gender (if-applicable), and race/ethnicity. While I developed this project to highlight my EDA skills, I additionally believe it can used to identify the groups of citizens that were affected the most by this awful virus, and how we can be better prepared to alleviate the burden that was placed on them.

Data Collection

Using Chicago’s Data Portal, I downloaded two datasets that would be useful for my analysis.

The data ranges from March 1st, 2020 to December 23rd, 2020.

Analysis

FYI: The linked visualization will give you a redirection notice from Medium, just click the first link that it’s trying to send you to.

Please take a look at the above visualization I was able to create as the summarize the entire dataset rather well.

The first dataset separated days into three categories, number of positive COVID-19 cases, number of hospitalizations due to COVID-19, and the number of deaths COVID-19 caused.

My first observation that grabbed my attention for a while, is the fact that the number of cases visual versus the number of deaths visual are practically mirror images of each other. With the peak number of cases in one day occurring on November 10th, with 3264 cases, while the number of deaths peaked much earlier in the year on May 5th, with 57 people dying from COVID-19. It’s intriguing, however makes sense the more you think about it. The weather being far nicer in April versus November, and more importantly the push back local, state and federal governments first received in the beginning of the pandemic, compared to the higher level complying with the new policies as the cases rose and the pandemic progress into the Summer and Fall months.

John Hopkins University of Medicine: https://coronavirus.jhu.edu/data/mortality

According to John Hopkins University of Medicine, the observed case — fatality ratio in the United States is 1.8%. With a little feature engineering, I checked the mortality rate for COVID-19 in Chicago, and as of December 23rd, the amount of deaths in Chicago from COVID-19, over the number of positive COVID-19 cases in Chicago was 2.8%. Although just a single percentage higher, it still is a big deal. The higher mortality rate can be explained by many things such as,

Incorrect Data — The Chicago Data Portal made it very clear that the dataset is being updated constantly and this included additional data, but also updated current data. Chicago being as large as it is — Chicago most likely makes up the majority of deaths from COVID-19 in the entire state, meaning the state mortality rate would be much lower compared to just focusing on Chicago.

The last step of the project was mapping these COVID-19 cases, to the different parts of the city. This is where the second dataset came into play. Please see the linked visualization of COVID-19 cases delineated by Chicago ZIP codes.

Chicago COVID Cases Map by ZIP Code

While there wasn’t too much analysis to be done here, at least nothing I couldn’t do with the earlier dataset, it was still really interesting to see the vast difference in COVID-19 cases across the city. I originally thought the center of Chicago would be a hotspot for the virus. Of course I thought those ZIP codes would be the vast majority of COVID postive cases. I couldn’t have been more wrong. In fact, the the ZIP code 60603, right in the center of Downtown had the lowest number of cases with 46. Additionally, focusing solely on my ZIP Code and seeing actually how terrible my neighborhood did relative to others was quite disappointing, but pretty believable, given the people I know.

Visualizations

Instead of listing of the findings and insights I was able to uncover, I thought I would just post the graphs themselves and let you learn for yourself!

Not suprising that the majority of positive Coronavirus cases are from individuals whose age range from 18–29, as many people who belong to this group are considering the least at risk relative to older inviduals. As we know from social media, and my experience in the City of Chicago, younger people have a tendency to push against the new orders and policies that are in place to slow the spread of COVID-19.

A next step for this project, would be to collect demographics on zipcodes of Chicago and cross check the above results against the ZIP Code Graph we have above.

While slightly more individuals who identify as male test positive, a noticeable higher percentage of females actually die due to COVID-19.

Just For Fun

As a test of my visualization ability, I also developed the below Choropleth map that shows the Global Case Count of COVID19 (data from 1/22/2020 to 11/15/2020). You can view that below!

COVID-19 World Map

GitHub Project