Category Archives: Data

Data, Elections, EU, Maps

European Election results mapped by Degree of Urbanization

Leave a comment

Because people in urban, suburban and rural areas often vote in slightly different ways, I decided to map the results of the 2019 European Elections split by what Eurostat calls “Degree of Urbanisation” (or “DegUrba” for short). It’s a standardized EU indicator that uses the population grid to classify Local Administrative Units, “LAU” for short, (i.e. municipalities of member states) into three types of area:

  • 1. Cities (densely populated areas)
  • 2. Towns and suburbs (intermediate density areas)
  • 3. Rural areas (thinly populated areas)

As with all of my European Election maps and visualizations, the election data is grouped by European Parliament Groups, as they were at the inaugural session of the new parliament.

Image

Some notes on various countries and design choices:

Ireland – election data was available only by electoral constituency. This forced me to frame the visualization as a Dublin (city) versus the rest of the country (rural).

Malta & Northern Ireland (UK) – election data only available agregated for the whole country / province.

Greece – election data is available by Municipalities (325 in total) but DegUrba data is available by LAU (6133 in total, in Greece, below municipalities). I aggregated the DegUrba to Municipality level by looking at the dominant category but I’m not sure methodology is 100% watertight.

Scotland – election data by Council Areas (32) but DegUrba data by LAU (41). While about half of the LAU map onto Council areas, the rest do not, although the boundary differences aren’t that big, and I’m pretty confident the guesstimated DegUrba level is correct.

Slovenia – election data was only available by electoral constituency (88 in total) which do not map well onto municipalities. As with Greece, I used an aggregate and estimate approach.

Belgium – Since the political landscapes are so different between Flanders and Wallonia, I decided to split it by regions as well, because the country aggregate to me makes little sense.

Made with Python (Svgwrite) and Inkscape, for the “30 Day Map Challenge” of 2021 (my only contribution)

Data, Elections, EU

Coalitions in the European Parliament

Leave a comment

This post is inspired by a Spiegel article from late September 2021, right after the Bundestag elections, and before the current “Ampel” ruling coalition was decided. I’ve decided to map various coalition options for the European Parliament election results of 2019 and take a look at where those coalitions would enjoy a majority of the vote share.

A bit of background: every EU Commission must enjoy the backing of a majority of the European Parliament to be elected. While European Parliament Groups are the default mode of organisation of the myriad of national parties in the union legislature, their behaviour is more loose and less disciplined compared to what we would have if we had true EU-wide parties that functioned like national parties do in national legislatures.

That being said, coalitions do exist. To make it simple: If the votes for all the parties that are part of the “coalition” (e.g. all parties members of the EPP and S&D groups in the first map below) are above 50%, then the area is colored in on the map.

The Old GroKo

The grand coalition made up of the center-right EPP group and the center-left S&D was, up until 2014, all on needed to have the majority in the European Parliament

Image

The Chad (or Romania) Coalition

Since 2014, the EPP and S&D together have fallen under 50% of the vote share. So a new, centrist, grand coalition now exists with the inclusion of the liberal RE (formerly ALDE) group.

Image

The Current Majority

What actually happened, when the vote for the von der Leyen commission was held, is that the EPP+S&D+RE coalition was joined by Polish PiS, Bulgarian VMRO (both ECR), Italian M5S (NI) and the SNP (G/EFA). So the actual majority for the current EU Commission is a bit wider than anticipated.

Image

A wide centrist coalition: Mauritius

One could imagine quite easily a wider, more inclusive centrist coalition where the EPP (center-right), S&D (center-left) and RE (liberal) are joined by the G/EFA (green/regionalist) group

Image

Traffic Light (“Ampel”) coalition

Since there was so much discussion on Germany’s “Ampel” or “traffic light” coalition, which is the one that ended up coalessiong into a government majority, below is the same center-left-green-liberal coalition on an EU level.

Neither this coalition nor the subsequent ones can muster a majority in the current parliament though…

Image

The impossible Jamaica: a Rwanda / Gabon coalition

Because the center-right EPP is coloured blue, instead of black like the CSU/CSU, the German “Jamaica” coalition cannot function under this name and would have to be renamed to something like the Rwanda Coalition or Gabon Coalition, as both these countries’ flags are colored blue-yellow-green.

Image

Red-Red-Green – another German classic

The “Rot-Rot-Grün” or Red-Red-Green is another classic German coalition name, that united the left, the center-left and the greens.

Image

Blue-Blue-Yellow – the right wing answer to the Red-Red-Green

The right wing counterpart to the left leaning Red-Red-Green coalition would be, the Blue-Blue-Yellow, composed of the liberals (RE), the center-right (EPP) and the conservatives (ECR). As opposed to the coalition above, inspired by Germany, this coalition would be a purely European invention, and with a larger number of MEPs, which sort of highlights how the European Parliament leans to the right.

Image

The Bahamas coalition

If the right wing and center-right groups above would hold their noses and include the far-right ID group into their fold, they could muster a majority akin to a Grand Coalition, but on the right.

The name comes obviously from the national flag of the Bahamas, amde up of two blue (albeit light blue) horizontal stripes, one yellow horizontal stripe, and a black chevron.

Image

The Nightmare Coalition

The last one that I decide to map out is what I call the Nightmare Coalition. It includes the conservatives (ECR), the far-left (LEFT, formerly called GUE/NGL), the far-right (ID) and the non-inscrits (NI), which in reality is just a collection of parties that could be included in any of the above.

Fortunately, this one falls below 50%.

Image

PS. The tweet that inspired this series of maps:

Data, EU, Languages

LANGUAGES IN THE STATE OF THE EUROPEAN UNION SPEECH – 2021

Leave a comment

The languages used in the EU Commission President’s 2021 “State of the European Union” speech in context. An update from last year’s analysis.

Image

Although fluent in French, President Ursula von der Leyen has not significantly increased the use of this language. Last year’s criticism from some quarters seem to have made little impact on her, as she still used English overwhelmingly.

She also seems, at first glance, to follow Juncker’s trajectory when it comes to speech length. First speech long (1h20m) then they become progressively shorter. In some sense it is understandable as the first SOTEU speech is also a way of introducing the presidency.

As a side-note, legth-wise, Barosso had stayed constant at about 30-40 minutes per speech throughout his mandate.

A worrying detail, is that I still see no use of any language from the EU’s Eastern half. This cannot help in countering the perception that the Eastern member states are seen, culturally too, as second-tier members. As an Eastern European I hope to see this rectified soon.

As an additional note: I took note of Jean Quatremer’s suggestion from last year and I’ve included Juncker’s and Barosso’s farewell speeches as well, even though technically they are not “State of the European Union” speeches.

Data, Elections, EU, Maps

EU Elections 2019 – Dasymetric Style

Leave a comment

The 2019 European elections mapped (by winner & turnout), using the 1km² population grid from Eurostat to exclude uninhabited areas.

Third attempt (after this and this) to take into consideration only populated areas, because “land doesn’t vote”.

Image

This is the first time I mapped the 2019 turnout by the way. Curious what people make of it.

I had made another attempt in 2018 for the previous (2014) European elections. Then as now, Belgium was the highest and Slovakia the lowest.

For those wondering about the >100% turnout, most of those municipalities are in Romania, one in Spain. The highest, at 123% is Bara, in Timiș county, Romania. There were 262 registered voters and 322 voters showed up to vote.

In Romania, if you are in a different municipality than your own, you can vote on “extra lists” (i.e. not on the local voter registry). In some rural municipalities, if enough people are from out of town, it can bump up the numbers like this.

Image

Now because we have 7+1 groups in the European Parliament, the “winner” does not always win a majority, only a plurality. The European People’s Party (EPP) Group, the “winner”, only got 21% of the vote.

Trying to distinguish between “majority wins” and “plurality wins” is worth exploring, but I’m not sure this version below is as readable as the original map. Still, a distinction worth keeping in mind when people talk about “winners” in the EU elections.

Image
Winners – plurality vs. majority

I also decided to make a map with just the areas where an EP group won 50% or more of the vote, to highlight it.

Image
Winners – by majority only
Data, Elections, EU, Maps

Who won here? EU Edition

Leave a comment

Animated map showing which European parliament group won the European elections of 2019, with an increasing level of detail, going by: EU-level -> Member state -> NUTS1 -> NUTS2 -> NUTS3 -> Municipalities or Constituencies -> (repeat)

Inspired by Lisa Charlotte Rost’s article on the Datawrapper blog titled “Different levels, different patterns“.

Image

Made with QGIS and Inkscape.

Data, Elections, EU

CArtogram of EUROPEAN Elections in the style of Balogh Pál (1902)

Leave a comment

I’ve been meaning to replicate the map style for over a year, ever since I discovered the 1902 ones.

Image

For those unfamiliar with Balogh Pál’s 1902 cartograms, the two he created together with Br. Proff Kocsárd, one on Hungary’s ethnic groups, the other regarding religious affiliation, can be accessed here.

For more on Hungarian dataviz history, see Attila Bátorfy’s article in
the DataVizSociety’s “Nightingale”.

Made with 10% Excel, 60% Python, 30% Inkscape:

  1. Grid layouts done in LibreCalc (including cartogram layout).
  2. Automated .svg file based on data and grids, including cell labels.
  3. Post-production in Inkscape (legend, country labels & borders, title, map border)

Some similar cartograms, see the tweets of Alex McPhee. He took this style and really ran with it:

Data, Elections, EU, Maps

US Elections: who would be the EU’s favourite?

Leave a comment

If given the chance, Europe would preferred Bernie Sanders or Elizabeth Warren. A light blogpost based on dubious data.

I took the score for each candidate in the 2020 US election from this site, mapped it on the political compass and using Voronoi partitions, I assigned a zone corresponding to each candidate (see below).

Image

I had previously calculated the average score of each municipality based on the 2019 elections and each party’s Chapel Hill Expert Survey score.

I mentioned “dubious data” because CHES and PoliticalCompass weigh parties differently (PC is more left wing, thus their scores are skewed to the right). But for lack of better data on candidate I made a colorful map based on the data I had.

First up is the version with multiple Democratic candidates (anyone over 10% in the primary polls) plus the Republican, the Green and the Libertarian candidates:

Image

But we could always just remove primary candidates and leave the four actual candidates who ran. Then we get a Biden vs. Hawkins (Green Party) race. Plus a Libertarian Malta.

Image

Or go even further, just remove anyone but the two major candidates who had a chance.

Then we get an EU that is more blue than the European flag. 100% Biden! Not one village for Trump. (In reality, we’d probably get a fair few pro-Trumpy areas, I’m sure).

Image

Anyway, this thread should not be taken seriously. The EU and the US are different beasts, with different political systems, candidate profiles and electorates.

Plus this whole exercise in map coloring is based on sketchy data to put it lightly. Just some weekend fun.

Image
Data, Elections, EU, Maps

Land Cover Doesn’t VOte

Leave a comment

Any cool or quirky US election map should ideally have a response in the form of a similar EU election map. So inspired by a Tim Wallace twitter thread (see at the end), here is a series of maps on how various land types “voted” in 2019:

Agricultural areas:

(i.e. arable land, permanent crops and other heterogeneous agricultural areas)

Image

Pastures:

Image

Forests (don’t always vote green):

Image

Shrubs and Grasslands:

Image

Wetlands:

Image

Barrens:

(i.e. beaches, bare rocks, badlands, tundra, glaciers, perpetual snow)

Image

And finally Built Areas, the most boring, because people actually live there. Includes roads and mines and industrial areas.

Image

Inspired by:

All land cover data from Copernicus Land Monitoring Service.

Data, Elections, EU

The European VOTE on the Political Compass in 2019

Leave a comment

Profile of each municipality or constituency according to the infamous political compass, going by the 2019 European election results.

The score is the weighted average of the Economic (“LR_Econ”) and Social (“GALTAN”)score from the 2019 Chapel Hill Expert Survey of each party that ran in a municipality, weighted by the result of said party in that municipality. This allows us to calculate how economically liberal/protectionist and socially conservative/liberal the vote was in a municipality or constituency with some degree of accuracy. From the survey:

LR_Econ = position of the party in 2019 in terms of its ideological stance on economic issues. Parties can be classified in terms of their stance on economic issues such as privatization, taxes, regulation, government spending, and the welfare state. Parties on the economic left want government to play an active role in the economy. Parties on the economic right want a reduced role for government.

GALTAN = position of the party in 2019 in terms of their views on social and cultural values. “Libertarian” or “postmaterialist” parties favor expanded personal freedoms, for example, abortion rights, divorce, and same-sex marriage. “Traditional” or “authoritarian” parties reject these ideas in favor of order, tradition, and stability, believing that the government should be a firm moral authority on social and cultural issues.

What we see is a socially liberal north-west versus a largely conservative East:

Image

When broken down by country we see the following patterns:

  • Distribution in the West seems to be along the right-upper to left-lower axis (i.e. socially conservative and economically liberal vs. socially liberal and economically protectionist) while in the East the distribution seems to be left-upper to right-lower (socially conservative and economically protectionist vs. socially and economically liberal)
  • Belgium is cursed to be forever internally divided
  • Malta & Northern Ireland are slight outliers
Image

Below is an animated 3D scatterplot with the same two dimensions as before for the base, plus “EU sentiment” on the Z axis.

Image

The scatterplots (including the animation) were made in Matplotlib with some extra text and graphics added in Inkscape.