The county voting data provide unambiguous vote vs. Love the graphic but as a physicist with a little mathematical background, I have been trying to understand what is happening with a “simple as possible” numerical graph. I modified it in a number of different ways to try to make it more interactive and useful. This map was inspired by Karim Douieb’s cool visualization of 2016 election results. I typically use leaflet to do web-based mapping but I wanted the power of d3 which has functions for the circles to prevent overlapping. This was my second attempt at using d3 to generate visualizations. If the vote margin is small, the county will be colored light blue or red, whereas if a county strongly favors one candidate, it will be colored darker red or blue.
Selecting the Color by Margin checkbox will color the each county circle by the amount that a candidate won the county.The total displayed area of the county circles is the same in either land and population view, though if the circles are overlapping, you may see less total colors. Selecting the No County Overlap checkbox will spread out all of the circles so you can see them all.The counties are colored red or blue depending on whether Trump or Biden or Clinton won more votes in that county. More populated counties will grow larger while less populated counties will shrink. Show population circles – where the area of the circle matches the relative population of the county itself.The counties are colored red or blue depending on whether Trump or Biden (in 2020) or Clinton (in 2016) won more votes in that county. Show land circles – where the area of the circle matches the area of the county itself, though obviously shaped like a circle.Hide Circles – just shows the county map.The map has a few different options for display: This tool should be relatively straightforward to use. However, the population number is certainly closer to reality as Trump won about 48.8% of the votes that went to either Trump or Clinton. Neither of these percentages is exactly correct because each county is colored fully red or blue and don’t take into account that some counties are won by a large percentage and some are essentially tied. More than 75% of the land area is red, when looking at the map based on land areas, while shifting to the population view only about 46% of the map is red. As a result, the blue counties tend to be the smaller ones so blue is visually less represented than it should be based on vote totals. Less densely populated counties tend to vote republican, while higher density, typically smaller counties tend to vote for democrats. An interesting thing to note is that this view is even more heavily dominated by the color red, for the same reasons. This map looks at the 20 presidential election results, county by county. Thus, when your eyes attempt to discern which color prevails, the comparison is more accurate and attempts to replicate the relative ratio of electoral votes for each side. That cartogram changed the size of the states so that large states with low population, and thus low electoral votes tended to shrink in size, while smaller states with moderate to larger populations tended to grow in size. On a normal election map, Red states dominate, especially because a number of larger, less populated states happen to vote Republican. The idea for that map was that by portraying a state as Red or Blue, your eye naturally attempts to determine which color has a greater share of the total. Previously, I created a map (cartogram) that showed the state by state electoral results from the Presidential Election by scaling the size of the states based on their electoral votes. This interactive map shows the election results by county and you can display the size of counties based on their land area or population size. The map has been updated to include the latest 2020 results and also adds the option to color the circles by the win margin rather than just looking at the winner.Ĭlick here to view a visualization that looks more explicitly at the correlation between population density and votes by county.
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