Monday, January 20, 2014

Where the bad guys vote

The German party NPD is the most known actor in nationwide politics that you can define as radical right-winged. The followers of this party are distributed heterogeneously in the country, which means that the NPD has much “better” results in the eastern part of Germany and especially in the countryside. Usually you can observe that in big cities small parties like the ecological Die Grünen and the internet phenomenon Piratenpartei tend to score better percentages than in the countryside.

I decided to visualize the results of the last federal election (Bundestagswahl 2013) with a focus on the bad guys which will here be defined by their vote for the NPD. The website offers detailed data related to the bygone election. For the visualization we need geodata which contains the information of geometry and attributes. Thus, we can use the provided Shapefile data of the 299 election districts. This data can be connected to our attribute data, which is the table of the election results that is in CSV-format. Both datasets are downloadable on the mentioned website.

After data cleaning with Excel or OpenOffice, the CSV table can be imported in QGIS and then joined to the election districts by using the Join function and connecting the data through the election district ID. A quick check whether the intuition about the heterogeneous distribution of the party’s follwers can now be made by visualizing the results with the graduated symbology option in QGIS.

As we can see, the data seems to reflect the thesis of an allocation of bad guys in the eastern part of Germany.

We could now stop our work and finish with a clear and simple output that supports our thesis. Nevertheless, this kind of visualization is quite boring and will not necessarily catch our audience’s attention.

Therefore, I decided to create a cartogram. Cartograms distort the geometry of each feature according to an attribute value. There are several beautiful cartograms that for example show the size of each country according to its population/wealth/beer consume and so on.

By using the awesome GIS programme ScapeToad 1.1 ( we can easily use the number of electorates and the number of votes for the NPD to create two cartograms. The cartogram that is based on the electorate shows the size of each election district according to the number of eligible voters, the other one should show the size in relation to the actual votes for the NPD.
ScapeToad offers also to generate a distorted grid for every cartogram and the option to export our outcomes in Shapefile format. Thus, we can now finish our map by working on the styling and using the new Print Composer. I decided to colour the election circles with high population to highlight major cities so we can examine the differences in distortion between cities and the countryside.

Click to enlarge

Our result shows each election circle’s importance for the nationwide outcome and in comparison the importance of each circle according to the NPD votes. The right map shows the blown up eastern part of the country quite well. Moreover, I find it interesting to study the major cities. For example, Munich, Hamburg, Freiburg and Cologne seem to have less problems with the bad guys than Dresden and Leipzig but also Berlin.

Download map in high quality


Anonymous said...

Hola! Me gusta mucho su sitio. La representación cartográfica que ha tenido éxito. También su tema "donde los chicos malos votan" es interesante, pero la pregunta sigue siendo: ¿por qué esta observó heterogeneidad se produce? GIS no sólo debe representar resultados, sino también para permitir suposiciones acerca de la distribución de los fenómenos en el espacio.

Gideon said...

Thank you for your feedback. There are different explanations that are discussed controversially according the hetergoneus distribution of extreme right-winged votes. The major factors seem to be the history of the German Democratic Republic (GDR) between 1949 and 1990 and a much higher unemployment in the east. I did not include explanations for this allocation because it is not really my expertise. The focus was more put on the workflow to visualize rather than finding an explanation.

If you are interested in further details, I can recommend this article: An East German Problem? Racist Violence in Germany