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Us electoral population density map 20163/24/2024 However, a drawback of all the above examples is that they use States or Counties as a basic unit in which to represent the data visually. We can also see counties with predominantly blue or red dots and those where the dots are mixed (giving rise to the illusion of purple hues because of the close proximity of dots), showing a more balanced vote between the candidates. If we consider voter turnout a proxy for population then we can see where the more densely populated counties are and vice versa. Density of voter turnout is reflected in the different dot densities seen in each county. Each blue or red dot represents 1,000 votes. ![]() A dot density approach is illustrated in Figure 2f which goes some way to showing the mix of vote per county. The value by alpha approach is shown in Figure 2e which uses saturation of colour so areas of low population fade to black. Because the map shows percentages of votes by total vote, the data is normalised using a consistent denominator and the map becomes a true choropleth allowing for visual comparisons to be accurately made across the map. This accommodates the visual problem of red dominating the map but modifies the map shape and can cause difficulty for readers unfamiliar with this map type.įigure 2d shows the percentage of vote with vivid red showing areas of strong Republican support, vivid blues showing strong Democrat support and shades of purple for areas with a strong balance between voting. A population density equalizing cartogram can be used to modify the sizes of the states by rescaling according to the number of electoral votes (Figure 2c). Figure 2b shows how at County level, the red/blue approach seems to magnify the wrong visual message even further. The use of a Mercator projection (or Web Mercator on a web map) further distorts the areas of the states and compounds the visual problems seen in Figure 1 (Figure 2a). At least Figure 1 used an Albers equal area conic projection…the key part being ‘equal area’ so it ensures that each of the states is in its correct proportion compared to all the others.įigure 2: 2012 US Election results mapped using different techniques (sources: Newman, 2012 Axis Maps, 2012 Nelson, 2012) The data should be normalized to take account of the population distribution because it doesn’t allow for the fact that the large red states generally have smaller populations. The other problem here is that the different sized areas alters the visual prominence of some States relative to others, regardless of how many people or voters live there. Large states become visually dominant and while they might not contribute many electoral college votes to the total they appear visually prominent. The electoral college voting system means that a state will be mapped as either red or blue. ![]() There is more red than blue which suggests a win for the Republicans when the opposite was the case. You only have to look at the map and ask the question ‘who won the election?’ to see the problem. This blog entry explores different ways to map election results and describes a different type of map we made to show the 2012 Presidential election results…it’s a multiscale dasymetric dot density web map (viewable on ArcGIS Online).ĭebate about how best to represent election results is nothing new, fuelled principally by the default two-colour map commonly used to show red vs blue States (Figure 1).įigure 1: 2012 US Election result at state level as a unique values map (source: Mark Newman 2012) The patterns we see are as much a product of the boundaries as the voting patterns of real people in real places. We can warp them into cartograms but these sometimes distort geography too much for them to make much sense. Counties) are arbitrary, exhaust space and dictate the visual pattern we see. E very time an election occurs, maps become a key component in telling the story, but what type of map best tells the story of the winners and losers? Red/blue choropleths? Areas shaded in an array of purples? Value by alpha maps? Dot density by County? Ultimately, the areas used (e.g.
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