News

Kartographische Arbeit in Florenz auf der ICC präsentiert!

Präsentation auf der ICC, Foto: Sabine Kirschenbauer
Präsentation auf der ICC, Foto: Sabine Kirschenbauer

[20|12|2021]

Understanding Election Results with Different Types of Maps


Congrats to Kristina!


Kristina Spörl untersuchte in ihrer Masterarbeit "Understanding Election Results with Different Types of Maps" wie Wahlergebnisse mit 15 ausgewählten Strukturdaten (wie bspw. Bildung, Einkommen, Arbeitslosigkeit, Religion, Alter) korrelieren.


Wie können statistische Zusammenhänge kartographisch korrekt und für User bestmöglich visualisiert werden.?

Wie versteht die breite Öffentlichkeit Karten, die Wahlergebnisse darstellen?


Die zentrale Frage ist: Werden statistische Zusammenhänge besser mit analytischen oder komplexen Karten kommuniziert?


Die Arbeit wurde für einen Vortrag auf der 30. International Cartographic Conference (ICC) eingeladen.


Die Masterarbeit wurde kooperativ von Prof. Dr. Jan Wilkening FHWS Würzburg und Prof. Dr.-Ing. Sabine Kirschenbauer begleitet.


Analytische Karten (c) Kristina Spörl
Analytische Karten (c) Kristina Spörl

Komplexe Karte (c) Kristina Spörl
Komplexe Karte (c) Kristina Spörl


Analyzing election patterns from a spatial view, interesting geographical patterns emerge. For instance, there often is a huge difference between voting shares for certain parties between urban and rural areas. One recurrent phenomenon in Europe and other places is that Green parties are stronger in urban areas, while in rural areas the conservative parties are ahead.


However, it goes without saying that there is a more complex pattern that needs to be analyzed. Often, demographic and socio-economic variables like religion, purchasing power, education or unemployment rate, which differ between certain regions, explain the differences in voting shares more precisely than classical rural-urban dichotomies. In this study, we focus on the following questions: How can such patterns be analyzed and visualized with interactive cartography? How do people perceive and interpret such patterns? How can cartography help in communicating such complex relationships effectively and efficiently?


While maps about voting shares are ubiquitous after every election, there are less examples of geovisualizations that show the statistical relationship between certain explanatory variables and voting shares (e.g. Lysek et al. 2020, Mansley & Demšar 2015, Skoutaris 2017). Maps that depict the spatial disparities in these statistical relationships are even harder to find. Creating a geovisualization for this purpose is a challenging task: The choice of appropriate methods of thematic cartography and the collection and aggregation of statistical data is sophisticated itself. Adding the complex aspect of statistical significance to these geovisualizations makes it even more demanding.


In this study, we collected and aggregated data about parliamentary elections in Germany and socio-demographic data from different sources (e.g., the German National Election Office and the geomarketing company Nexiga GmbH). The spatial level of our analysis are the 299 electoral districts, which contained 155,149 voters on average. We focused on the voting shares for the six strongest parties.


Further research is needed to establish the results of the study and to identify aspects of map design which help people to better understand voting results and the geographical pattern behind these results. With open data initiatives emerging, more and more election and demographic data is publicly available all over the world. Moreover, GIS tools are becoming more powerful in analyzing and visualizing statistical data with a geo-spatial component. The integration of open-source libraries in R and Python like ggplot2, seaborn or matplotlib provide further opportunities for such analyses. Enhancing similar results about the accuracy of answers with eye-tracking analyses provides even more possibilities. Another opportunity for geovisualization is using the third dimension for additional variables.


It remains to be seen whether our patterns – not only the patterns of statistical significance, but also the patterns of comparing and evaluating map types - can be replicated with results of further elections with similar election systems all over the world. Further analysis is also needed to explore which methods of data visualization and thematic cartography suit users of "election- results-maps" best, especially under consideration of our today's fast-paced society.