The black line shows the percentage of counties (or votes) won by the democratic candidate (Joe Biden) in each of those bins. You can toggle the graph to show the number of counties won by each candidate or the number of votes won in each of the population density bins. Note that they are on a log scale as well. ![]() This second graph is a histogram that specifically categorizes counties into discreet bins by population density. The densest counties favored Biden quite heavily as he won 45 of the 50 densest counties in the country, which also tend to have a fairly high population. the Borough of Staten Island) is the densest county (17th densest) in the country that Trump won. Dorling's Algorithm (Dorling 1993, 2011). There are also several counties with population density below 1 person per square mile (several in Alaska because of the size of their counties) but these are excluded from the graph. population density (Doll 2017) and electoral votes (Dominique 2005). This map shows the population density in the United States in 2022 in persons per square mile in a multiscale map by country, state, county, ZIP Code, tract, and block group. These locations are, Far West SA2, north of Hay SA2 and north-west of Griffith Region SA2 in NSW. The area of the centre of population is zoomed in to an SA2 level, showing the locations of the centre of population as at 30 June 2020 and 30 June 2021. This is done because there is one to two orders of magnitude difference in the densest counties (in New York City) and even moderately dense counties. This image shows the location of Australia’s centre of population on a map of Australia. Population density is calculated as people per square mile in the county and is shown in the graph on a log scale, where each major grid line is 10 time greater than the previous one. ![]() Vote margin is the percentage amount that one candidate beat the other candidate by in the county (0% means a tie while 50% means that one candidate got 75% and the other got 25% of the voteshare). It’s clear there is a very strong correlation between the vote margin and population density. Hover over (or click on) the bubbles to see information about the county. This raised the question of exactly how population density correlates with election results. As the counties were represented by population, it was clear that democratic-leaning areas on that map tended to grow in size, while republican-leaning areas tended to shrink. ![]() The visualization I made about county election results and comparing land area to population size was very popular around the time of the 2020 presidential election. How do 2020 presidential election results correlate with population density?
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