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.
Click here to view a visualization that looks more explicitly at the correlation between population density and votes by county.
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.
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. 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. On a normal election map, Red states dominate, especially because a number of larger, less populated states happen to vote Republican. 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. 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.
This map looks at the 2020 and 2016 presidential election results, county by county. An interesting thing to note is that this view is even more heavily dominated by the color red, for the same reasons. Less densely populated counties tend to vote republican, while higher density, typically smaller counties tend to vote for democrats. 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. 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. 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. 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.
This tool should be relatively straightforward to use. Just click around and play with it.
The map has a few different options for display:
This was my second attempt at using d3 to generate visualizations. 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 map was inspired by Karim Douieb’s cool visualization of 2016 election results. I modified it in a number of different ways to try to make it more interactive and useful.
This visualization does not actually simulate the collisions between the circles on your browser. It is a bit computationally taxing and causes my computer fan to turn on after awhile. So instead I ran the simulation on my computer and recorded the coordinates for where each circle ended up for each category. Then your browser is simply using d3 transitions to shift positions and sizes of the circles between each of the maps, which is simpler, though with 3142 counties, it can still tax the computer occasionally.
Data and Tools
The Market Timing Game simulation is premised on the idea that buying-and-holding index investing and index funds are a no-brainer investment strategy and market timing (i.e. trying to predict market direction and trading accordingly) is a less than optimal strategy. The saying goes “Time in the market not timing the market”. In this simulation, you are given a 3-year market period from sometime in history (between 1950 and 2018) or you can run in Monte Carlo mode (which picks randomly from daily returns in this period) and you start fully invested in the market and can trade out of (and into) the market if you feel like the market will fall (or rise). The goal is to see if you can beat the market index returns.
Updated: Lots of folks on Reddit pointed out some mistakes in the Spanish calculations, and helped me figure out the solutions, so the Spanish graphs are now updated. The Spanish calculator is now live!
Building off of the last post about Counting to One Million in English, I received some comments about looking at other languages. That seemed like a very good idea, so I looked at a list of the world’s most popular languages and saw Chinese and Spanish listed with English in the Top 3. Having a little experience with both of those, I set out to compare how long it’d take to count in each of these languages, if you had to pronounce every single number from one to one million.
Again, here’s the plot of the number of syllables per number for English. The longest word is seven hundred seventy seven thousand seven hundred seventy seven (20 syllables).
It’s March and for those who follow sports, that means college basketball and March Madness. The tournament is mainly interesting because of two reasons: (1) filling out brackets and (2) watching and hoping for upsets . This interactive March Madness matchup visualizer helps do both of these things by showing you the history of the tournament (since 1985 when the tournament expanded to 64 teams) in terms of matchups between teams with different seeds (1 through 16 in four regions).