The Story of Native Colorado Residents and COVID Deaths

This year, I have learned a lot about information exploration, as well as framing that exploration into a story. I’ve looked at datasets ranging from local to national level representations of the country, and personally, these have been my favorite datasets to work with. This week, we were given a county data frame that contains many different variables relating to age, sex, education, medical care, and political standings. While this data was collected from various counties in all fifty states, I wanted to focus on my home state of Colorado. As someone born and raised in this state, I was curious about the relationships I could discover and potentially learn more about.

To begin, I filtered the data frame to only include Colorado data and created a heat map. Creating a heat map as the first visualization allowed me to examine the relationships between all the variables in one visual. Here is the heat map I created:

Upon first looking at the different variables included in the data, I decided to focus on the percentage of people born in the same state as the independent variable. As someone who fits within this demographic, I was curious to see what relationships I could find at first glance. This is where I noted an unlikely positive relationship: The percentage of people born in the same state and covid deaths per capita. This got my attention immediately because there is no clear conceptual link between these two variables. Upon further regression analysis, the relationship is quantified by a value of .646 for the correlation coefficient. What’s even more interesting, is that the mean of the independent variable (percent born in the same state) increases by .001 as the dependent increases. This tells me that while these variables share a strong positive relationship, the relationship isn’t causal. The question becomes, what other variables are driving this relationship?

Again using the heat map, I examined relationships with the percent born in the same state as the independent variable. I was looking for variables that shared a relationship but also had more of a conceptual connection to the coronavirus pandemic. I noted that there is a strong negative relationship with the percentage of college graduates, as well as a strong positive relationship with percent of the population on assistance and in family poverty. Upon analysis of the correlation between the percentage of people born in the same state and the percent of college graduates, I calculated a correlation coefficient of .612. This finding showed me that I was on the right track. The opportunity of graduating college contributes to the quality of life, as well as other variables in this data that are related to health care and fiscal properties. By analyzing the relationships with the percentage of college graduates, I could find more specific variables that contribute to the covid deaths within that county.

I began looking at notable relationships where the percentage of college graduates is the independent variable. I began brainstorming different occupations that could have a negative impact on personal health such as factory jobs, manual labor, or serving the armed forces. Most of these positions are injury prone, so I focused on medical variables. The heat map shows that the percentage of college graduates has a negative correlation (stronger than -0.50) with the percentages of family poverty, assistance, and support for Donald Trump during the 2020 presidential election.

After analyzing these relationships independently, the connections between these different variables became apparent. The story begins with the analysis of the first two variables, which shows a negative relationship between the percentage of people born in the same state they reside, and the percentage of college graduates. As more of the county’s population is born in the state, less of the population is shown to have graduated college. College is an opportunity that opens access to better-paying jobs and work conditions. This explains the negative relationship between the percentage of college graduates and the percent of families in poverty as well as assistance. Without a college degree, the jobs available become more physically taxing and sometimes dangerous, as well as less pay. With a higher chance of health problems and less access to quality medical care, these county populations were at a higher risk of COVID mortality. The negative relationship between the percentage of college graduates and support for Trump in 2020 can also be connected when it comes to COVID misinformation. As less of the population is reported to have graduated college, more are likely to have graduated high school. When discussing this dataset in class, we visualized an inverse relationship between these two variables and support for Trump in 2020. As more people completed a higher level of education, support for Trump decreased. Having a lower percentage of college graduates in a county could mean that county was more prone to misinformation regarding COVID and public safety, which also likely contributed to the COVID deaths per capita. The story behind this relationship is a great example of how correlation doesn’t necessarily prove causation.