Through analysis of COVID deaths by state, and foreign travel, we a linear relationship, with outliers that provided insights about temperature. These temperature insights can be furthered to understand other socioeconomic data as it relates to deaths in areas with high temperature variance.
Through analysis of risk jobs and population density, we were able to eliminate the % of COVID-19 risk job employment and amplify the impact of population density on number cases and deaths. With further analysis on a demographic level as it relates to race and age, this could be a potential explanation to the disparities.
With interest in understanding the reasons behind the drastic disparities and division that COVID-19 was causing amongst different socioeconomic classes, we attempted to look at the count of COVID-19 deaths in relation to other factors such as population density, travel frequency, employment and unemployment, land surface temperature, comorbidity and others.
Leveraging excel and pandas dataframes to build master data sheets from NASA, CDC, USA Facts, NYTimes, and many other sources, we then examined and visualized the data creating various dashboards in Tableau.The project was then developed using Figma wireframes as a design base for HTML/CSS generated by Webflow integrated with a Django Python application.