To better evaulate the impacts of Covid-19, this project compares its effects with various other 21st century major events in the US like 9/11 and the 2008 Financial Crisis
The objective of this project was to understand the degree of the impact various different events have on the United States Economy, and the citizens of the United States.
First, we examined COVID-19 data on its own, to get a glimpse of its own consequences on the economy of the United States. Then, by examining economic and social data during different times in history, we hope to learn the relationships between certain major events, and understand the severity of each compared to others, thus giving us a better understanding of the pandemic as a whole.
We employed the use of several different types of data visualizations learned in this course, using a mix of standard and non-standard visualizations. Our goal with was to ensure that digital literacy was a key priority when visualizing for users who may have limited experience with data visualizations, but also provide a powerful interactive medium to explore the data.
Our data is mainly time-based, so we will use those visualizations which work well using timeseries or time-based data, including a timeseries chart and a PCP chart. Our goal with the visualizations was to give users a color-coded way to compare and differentiate between world events.
In addition to using time-series to see the large-scale impact of a variable, we also used a Calendar Heatmap to visualize the day to day and month to month impact on specific variables. This was especially useful to see when certain events increased in impact, and when they may start slowing down.
Lastly, we employed the use of an interactive Parallel Coordinate Map (PCP). Our PCP focused strongly on having a variable-to-variable comparison between each year. In addition, we color-coded this according to the specific national events that we are comparing, to give a better idea of the timelines, as well as potential outlier points. Note that we did perform some dimension reduction in order to make the visualization more readable.