# Wahlland

_Election maps reinvented_

Published: 2013-10-01

Live project: [→ Deutsche Version](http://projects.truth-and-beauty.net/wahlland/)
[→ English version](http://projects.truth-and-beauty.net/wahlland/index_2013_en.html)

Wahlland presents a whole new kind of election map for the German Bundestag elections 2013, commissioned by [ZEIT Online](http://zeit.de). Instead of showing the electoral districts results on a geographic map, or in a table, I decided use the data to map a new land: Electionland, where all the districts which vote in a similar way are located nearby. This new kind of map allows us to see a Germany grouped by lifestyles, preferences and attitudes.

## Key visuals

- Wahlland 2013 map arranging German electoral districts by voting similarity into party-colored triangle clusters
- Zoom on the MDS map with CDU/CSU black triangles clustered lower centre and Grüne green triangles upper right
- Wahlland map with tooltip for Hamm - Unna II showing party vote shares, SPD 38.5 percent ahead of CDU/CSU 36.9 percent
- Map detail with red SPD triangles clustered upper right and faint Linke and AfD districts at left

## How it works

Here is some background on how I created this map. First of all, I need to normalize the data and make the parties comparable. To this end, I applied a [z-score normalization](http://en.wikipedia.org/wiki/Standard_score) on the data. This makes the relative differences in voting behavior comparable, even if we compare huge parties with smaller ones.

![Three districts shown as party-colored triangles sized by z-score-normalized vote share]()    

![Diagram showing MDS placing similarly-voting districts close together and dissimilar ones far apart]()

In the map, the triangle sizes correspond to these z-scores per party; I only show the results above average — these are the “characteristic” parties for a district. Each color and angle stands for one party.

![Wahlland 2013 map arranging German electoral districts by voting similarity into party-colored triangle clusters]()

I then use scikit-learn to calculate a 2-dimensional map of the resulting “electoral district space” using [Multi-Dimensional Scaling](http://en.wikipedia.org/wiki/Multidimensional_scaling) or short: MDS — a technique I know quite well since the days of my [B.Sc. Thesis](https://web.archive.org/web/20140824213618/http://moritz.stefaner.eu/write-talk/b-sc-thesis/).

It is an algorithm that you can feed a matrix of similarities between objects (in our case, how similarly the districts voted), and it will attempt to find an arrangement that expresses these similarities as good as possible on a two-dimensional map.

![Zoom on the MDS map with CDU/CSU black triangles clustered lower centre and Grüne green triangles upper right]()

## Interpretation

Some words about the interpretation of the map:
First of all, it is important to note that the rotation of the map is a purely aesthetic choice. The MDS algorithms defines only a relative space, where the positions of all elements with respect to each are important, but not on absolute axes. But within the data, some interesting axes seem to emerge nevertheless: top-to-bottom seems to be correspond quite well to the traditional left-to-right spectrum. The left-to-right axis to interpret is a bit trickier but definitely seems related to social status or income. Also interesting to see how the blue AfD party — a newly founded euro-sceptic protest party, sort of a wild card — seems to be all over the map in the vertical center. A bit shocking also to see how there still seems to be a gap in Germany (the island on the left in pretty much exclusively ex-east Germany), with only Leipzig and Postdam bridging the gap. A note on the huge orange triangles for the Pirate party — actually, their results were not that great, but this enabled them to be very much above average in some districts.

## Credits

Produced in collaboration with [ZEIT Online](http://zeit.de)

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[View on truth-and-beauty.net](https://truth-and-beauty.net/projects/wahlland)