Visualizing and analyzing the 2022 election
Using data from
- Harvard and MIT Election Data and Science Lab, “County Presidential Election Returns 2000-2020”, https://doi.org/10.7910/DVN/VOQCHQ, Harvard Dataverse, V10; County Presidential Returns
- U.S. Dept. of Commerce: Bureau of Economic Analysis
- U.S. Dept. of Agriculture, Economic Research Service
I wrote a program to visualize 2020 presidential elections results in 3d. Few of the findings are new, but it is educational to see the visualizations first hand.
There were 480 counties in which at least 80% of voters voted for Trump (restricted to counties with at least 1000 total votes). In contrast, there were only 29 counties in which at least 80% of voters vote for Biden. This suggests that Republicans are more partisan, but it’s skewed by the fact that Trump voters tend to live in rural counties, which would have lower population and be more homogeneous.
This following scatter plot shows that counties with higher income tended to vote for Biden. (Blue = Democrat = Biden)
For people who voted for Trump, there’s a small negative correlation between average income of the county and the percent of the county that voted for Trump. In other words, votes for Trump cluster at the upper left, where incomes are low and the percent voting for Trump is high.
The next scatter chart shows that counties with higher education tend to vote for Biden.
And counties with less education tend to vote for Trump. The correlation is strong.
The following scatter chart shows clearly that counties with a high percent of black residents tend to vote for Biden.
The following scatter chart shows clearly that counties with a high percent of black residents tend not to vote for Trump.
