![]() For Pennsylvania r0 is about 0.38 and p0 is about 300,000. After playing around with Excel, I found the result that a log-log plot fits reasonably well the curve rD=r0 exp(p/p0), where r0 is the ratio of votes in small populations and p0=ln(1/rD) is the population where the Democrats overtake republicans as population grows. I took Pennsylvania (manually copying data from state web site) and plotted the ratio rD as a function of p. I would sure be interested nation wide in the relative size of Democratic votes to Republican votes (or the reciprocal doesn’t matter) plotted as a function of population. population as you have utilized in the graphic. 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.
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