{"id":2778,"date":"2020-11-10T12:31:49","date_gmt":"2020-11-09T23:31:49","guid":{"rendered":"http:\/\/blogs.lincoln.ac.nz\/gis\/?p=2778"},"modified":"2023-05-07T03:11:21","modified_gmt":"2023-05-07T03:11:21","slug":"interesting-election-maps","status":"publish","type":"post","link":"https:\/\/blogs.lincoln.ac.nz\/gis\/interesting-election-maps\/","title":{"rendered":"Interesting Election Maps"},"content":{"rendered":"

This post looks at some alternative mappings of the recent 2020 US presidential election<\/em><\/p>\n

Election coverage seems to love maps, but they can sometimes misrepresent outcomes as we’ve seen previously<\/a>.<\/p>\n

The GIS Blog tries (but often doesn’t succeed) to be apolitical, even though there’s plenty of force behind the idea that political views have a spatial component.\u00a0 Like many, over the four or five days of uncertainty starting on (our) 4 November, I relied on information in the form of maps to get a sense of how things were going.\u00a0 Jeez, watch CNN for even a few minutes and you’ll easily be able to see how they used maps to tell the ongoing story.\u00a0 \u00a0Other networks were very much the same.\u00a0 It’s not often that map get to have such dramatic effect.\u00a0How would you rather see things summarised?\u00a0 Like this?<\/p>\n

\"\"<\/a>
https:\/\/edition.cnn.com\/election\/2020\/results\/president?iid=politics_election_national_map<\/em><\/a><\/figcaption><\/figure>\n

Or like this?<\/p>\n

\"\"<\/a>
https:\/\/edition.cnn.com\/election\/2020\/results\/president?iid=politics_election_national_map<\/em><\/a><\/figcaption><\/figure>\n

They’re telling different stories, of course, but in terms of a picture telling a 1,000 words, these maps are quite useful.\u00a0 But they don’t always tell the full story.\u00a0 There’s no doubt that the US electoral system is crazy, especially keeping in mind that there is no national body that oversees elections like this.\u00a0 As one pundit on Al Jazeera put it recently (I’m paraphrasing), the US has 50 state elections (and a few districts and territories) that just happen to be held on the same day – 50 different states, 50 different sets of rules for how things will proceed, from when votes can start to be counted to how the votes from electors in the electoral college are assigned.\u00a0 With the electoral college, most (but not all) states are a winner take all.\u00a0 The margin in Georgia (currently uncalled) may end up being as slim as a piece of paper, but all 16 will go to whomever wins that state (after the law suits have been settled, that is…).<\/p>\n

Who knows for sure what was in the minds of the “founding fathers” when they set up the electoral college, but part of\u00a0their\u00a0aim was to weight states’ votes by population – the bigger the state, the more influence it has.\u00a0 Let me refine what I just said: much better to say the larger the population<\/em> of a state, the larger its influence.\u00a0 As a result, a heavily populated state like California has 55 electoral college votes, but a larger state (by area) such as\u00a0Alaska (which is four times larger by area but 54 times smaller<\/em> in terms of population) gets only 3 – it’s down the population differences.<\/p>\n

What’s a bit misleading about these maps is that we unconsciously weight our interpreted levels of impact by the extent of the coloured areas.\u00a0 For instance, we might look at a map like the one below and be overwhelmed by all the red (2020 election results):<\/p>\n

\"\"<\/a>
https:\/\/www.core77.com\/posts\/90771\/A-Great-Example-of-Better-Data-Visualization-This-Voting-Map-GIF<\/em><\/a><\/figcaption><\/figure>\n

What’s driving this map is that when Americans vote, they vote in their local county so the shapes we see here relate to the county boundaries.\u00a0 What we don’t see here is how the underlying population varies by county.\u00a0 There may be a sea of red, but they are generally in places where there are fewer people.\u00a0 Karim Douieb<\/a> put together an excellent recasting of the 2020 election that, arguably, gives us a better sense of what really happened:<\/p>\n

\"\"<\/a>
https:\/\/www.core77.com\/posts\/90771\/A-Great-Example-of-Better-Data-Visualization-This-Voting-Map-GIF<\/em><\/a><\/figcaption><\/figure>\n

Using proportional dots at the centre of each area gives us a slightly better sense of how things went.\u00a0 The larger the dot, the higher the population.\u00a0 These then all get aggregated by state and hence we end up calling one state for a particular candidate.\u00a0 Here’s a similar effort for the electoral college standings:<\/p>\n

\"\"<\/a>
https:\/\/edition.cnn.com\/interactive\/2020\/11\/politics\/2020-vs-2016-election-map-charts\/<\/em><\/a><\/figcaption><\/figure>\n

This one is useful but it does rely more on the wetware making sense of colour and size and location – it feels a bit less informative than the county based one to me.\u00a0 In any event, these maps help us see a\u00a0country that is still split along (for one axis) a rural-urban divide.\u00a0 Let’s just hope there are some calmer days ahead for everyone.<\/p>\n

C<\/p>\n","protected":false},"excerpt":{"rendered":"

This post looks at some alternative mappings of the recent 2020 US presidential election Election coverage seems to love maps, but they can sometimes misrepresent outcomes as we’ve seen previously. The GIS Blog tries (but often doesn’t succeed) to be apolitical, even though there’s plenty of force behind the idea that political views have a […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2778","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/2778","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/comments?post=2778"}],"version-history":[{"count":1,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/2778\/revisions"}],"predecessor-version":[{"id":4092,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/2778\/revisions\/4092"}],"wp:attachment":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/media?parent=2778"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/categories?post=2778"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/tags?post=2778"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}