Leading up to last Saturday’s federal election in Australia, ABC News Australia had a page explaining the usual problem with geographic electoral maps when sparsely populated rural districts are enormous and lots of voters are concentrated in the cities. Calling the page “The Australian electoral map has been lying to you” might have been torquing things a bit, though. Then again, via Maps Mania, live election results maps from The Australian and The Guardian both use straight geographic maps, so maybe not.
Julien Gaffuri’s map of the second-round results of the French presidential election is, as you can see, extraordinarily busy—and, by the way, extremely processor-intensive: it will slow down your machine—because it’s at the commune level and each circle is scaled to population. (News flash: Paris has lots of people in it.) And those circles are striped circles: the proportion of the votes is indicated by the area taken up by a given colour. The map of the first round results shows more stripes (because more candidates) but is by department, so it’s a little easier both to read and to see how the striped circle format works. It’s an interesting alternative to a choropleth map, and a bit less ambiguous.
France 24’s interactive map (right) covers both first and second rounds and shows results by region, department and commune. It is annoyingly unlabelled, which is a surprising choice for France’s English-language news service. Le Monde’s map uses a similar colour scheme—yellow/orange for Macron, grey/brown for Le Pen—but at least has mouseover labels.
Le Parisien’s maps aren’t interactive, nor are they particularly large, but they illustrate other aspects of the results, like the abstentions, voter turnout and differences vs. the 2017 vote. The Guardian’s maps are low on detail but provide similar information. Libération’s map, on the other hand, is a cluttered mess, showing each commune as a proportionally sized dot. [Maps Mania]
Previously: 2022 French Presidential Election (First Round).
Some maps showing the results of the first round of France’s 2022 presidential election. Le Monde’s interactive map shows the winner by commune: it has all the caveats you’d expect from a geographical map (the cities have a lot of voters but not much territory, making Le Pen’s rural support look more impressive). Bloomberg’s maps are behind a paywall: see this Twitter thread instead, which has maps of the regional concentrations of each candidate’s support. (With a dozen candidates on the ballot, it’s hard to get a true picture from a single map.) Also on Twitter, Dominic Royé’s dasymetric maps of the results [Maps Mania].
The Washington Post looks at how redistricting has changed the U.S. congressional electoral map so far. “As of Dec. 15, half of the 50 states have settled on the boundaries for 165 of 435 U.S. House districts. […] The Washington Post is using the number of Trump and Biden voters within old and new district boundaries, according to data collected by Decision Desk HQ, to show how the districts have changed politically. As more states finalize their maps, we’ll add them to this page to give a fuller picture of what to expect in the midterms.”
Gerrymandering in Texas
The New York Times and Texas Monthly look at the bizarre shapes in the new congressional electoral map of Texas, which gains two new representatives. Texas Monthly’s Dan Solomon: “Across the state, there will be one more majority-Anglo district than under the prior map, and one fewer majority-Hispanic one. The two new seats Texas was awarded for its booming population will be placed in Austin and Houston—and even though non-Anglo newcomers made up 95 percent of the state’s population growth the last decade, both districts will be Anglo-majority.” Kenneth Field has some thoughts. [Maps Mania]
Making Redistricting More Fair
A Surge of Citizen Activism Amps Up the Fight Against Gerrymandering (Bloomberg): “From North Carolina to Michigan to California, voting rights groups, good government advocates, data crunchers and concerned voices are finding new ways into the fight for fair representation, via informational meetings, mapping contests, testimony workshops and new technologies.”
Can Math Make Redistricting More Fair? (CU Boulder Today): “Clelland doesn’t advocate for any political party or for any particular redistricting proposal. Instead, she and her colleagues use mathematical models to build a series of redistricting statistics. These numbers give redistricting officials a baseline that they can compare their own maps to, potentially identifying cases of gerrymandering before they’re inked into law.”
Redistricting—and gerrymandering—is one of the blacker cartographic arts. With the release of data from the 2020 U.S. Census, and the changes in state congressional delegations—some states gain a seat or two, some states lose a seat, others are unchanged—new congressional maps are being drawn up for the 2022 elections. The Washington Post takes a look at proposed congressional district maps in Colorado, Indiana and Oregon, and what their impact may be.
The New York Times maps partisan sorting in America—the tendency for voters to self-select into areas where people think and vote the same way they do—down to the neighbourhood level.
The maps above—and throughout this article—show their estimates of partisanship down to the individual voter, colored by the researchers’ best guess based on public data like demographic information, voter registration and whether voters participated in party primaries.
We can’t know how any individual actually voted. But these maps show how Democrats and Republicans can live in very different places, even within the same city, in ways that go beyond the urban-suburban-rural patterns visible in aggregated election results.
It goes beyond racial, urban vs. suburban vs. rural and house vs. apartment splits, to the point where researchers are wondering whether Americans are “paying attention to the politics of their neighbors” when they decide where to live. This has implications not only in terms of electoral targeting (e.g. gerrymandering, voter suppression), but in terms of basic social cohesion.
The maps are based on research by Jacob R. Brown and Ryan D. Enos published earlier this month in Nature Human Behavior.
Previously: Red and Blue vs. Gray and Green.
Kenneth Field has released a dasymetric dot density map of the 2020 U.S. presidential election results. One dot equals one vote. “Data at a county level has been reapportioned to urban areas. Dots are positioned randomly.” It’s in the same vein as his 2016 map, which went all kinds of viral when he posted it in early 2018. A high-resolution downloadable poster is here; an interactive version is here.
xkcd did another map thing, so I have to post about it; it’s a rule. This time Randall revisits the design of the map he did for the 2016 U.S. presidential election, in which one figure represents 250,000 votes for each candidate. In a Twitter thread, he explains the rationale for the map:
It tries to address something that I find frustrating about election maps: Very few of them do a good job of showing where voters are. […] There are more Trump voters in California than Texas, more Biden voters in Texas than New York, more Trump voters in New York than Ohio, more Biden voters in Ohio than Massachusetts, more Trump voters in Massachusetts than Mississippi, and more Biden voters in Mississippi than Vermont.
Previously: xkcd’s 2016 Election Map.
Mapbox is holding an election mapping challenge. Entrants are asked to create, using the Mapbox basemap and compatible tools and platforms, “an original, publicly viewable interactive map, map-based data visualization, or application that uses location tools and focuses on an elections-related theme.” Entries are due November 8, and must be accompanied by a public blog post explaining the project.
Previously: Mapbox Elections.
Kenneth Field’s virtual talk on the cartography of elections, given to University of South Carolina students on 25 September 2020, is now available on YouTube. “It explores the way in which map types and their design mediate the message, and using examples from elections shows different versions of the truth.” Also includes material from Ken’s forthcoming book.
The New York Times uses the colours in aerial images as a proxy for political leanings: rather than red-and-blue electoral maps, the political landscape, Tim Wallace and Krishna Karra argue, is more green and gray.
The pattern we observe here is consistent with the urban-rural divide we’re accustomed to seeing on traditional maps of election results. What spans the divide—the suburbs represented by transition colors—can be crucial to winning elections. […] At each extreme of the political spectrum, the most Democratic areas tend to be heavily developed, while the most Republican areas are a more varied mix: not only suburbs, but farms and forests, as well as lands dominated by rock, sand or clay.
This is a generalization, to be sure, but so are most political maps, and the notion that urban areas tend to vote Democratic while rural areas tend to vote Republican isn’t what I’d call a revelation. Still.
Election-atlas.ca, the collection of historical Canadian election results maps I first told you about in 2018, has added poll-by-poll results for the 2019 Canadian federal election. Also, since we last saw them it seems they’ve extended their historical results further back in time—as far back as 1896 for the federal results.
This week Mapbox launched a tool for the upcoming 2020 U.S. elections: Mapbox Elections, “a resource to help individuals, journalists, and organizations cover the elections, analyze the results, and build modern maps to display it all.” Their first product is a dataset including U.S. presidential election results from 2004 to 2016.