As soon as the results of the first round of the 2016 Austrian presidential elections were announced, a map that shows the winning candidate by municipality went viral.

Austrian election maps are usually dominated by the colours red or black to indicate a winning Social Democratic Party (SPÖ) or Austrian People’s Party (ÖVP). Since 24 April 2016, the day when Norbert Hofer from the far-right Freedom Party (FPÖ) won the first round of the presidential elections, the political landscape of Austria looks almost entirely blue with some green, grey, red and black dots.

Despite five rival candidates, Hofer came out as the winner in the vast majority of Austria’s municipalities. The colours of the austromorph maps below indicate the winning candidate’s party affiliation: blue for Norbert Hofer (FPÖ), green for the runner-up candidate Alexander van der Bellen from Austria’s Green Party, and grey for the independent third-placed candidate Irmgard Griss. A few red and black dots indicate municipalities which were won by candidates from the ruling grand coalition parties (SPÖ and ÖVP respectively).

A runoff between Hofer and van der Bellen will decide Austria’s next president. And Norbert Hofer indeed has a good chance to become the country’s first president in post-war history who is not backed by one of the two major centrist parties, i.e. SPÖ or ÖVP. However, winning the second round of the elections won’t be as easy for Hofer as the upper map might suggest.

Traditional maps do not consider the population size of each municipality but rather the size of its territories. This can be quite misleading when mapping election results. Do the polygons of the small map in the upper left corner represent urban areas with a high population density or low-density rural areas? For this reason, the territories in the main map have been re-sized by the respective electorate (i.e. the number of persons eligible to vote). Consequently, the map represents the actual size of each municipality’s electorate rather than the size of its territory and therefore allows a weighted overview on the election results.

Weighted or not, Austria’s political landscape remains by and large blue. Nevertheless, the comparatively large green spots in the re-sized map suggest a rural-urban divide in the political geography of Austria. Norbert Hofer (FPÖ) was the winner in the vast majority of municipalities, especially in (less densely populated) rural municipalities, while Alexander van der Bellen (Green Party) won in most of Austria’s larger cities such as Vienna, Graz, Linz, Innsbruck, as well as in the rather urban Rheintal region (in the West of Austria).

It remains to be seen whether Norbert Hofer or Alexander van der Bellen will win the deciding second election round on 22 May 2016. Regardless of the outcome, make sure to check out the election maps from austromorph.space.

See also

• austromorph.space – A cartographic project reshaping Austria
• Detailed elections results provided by ORF (Austrian Broadcast Corporation)
• drawingdata – Visualisations of political data in Austria

How did the share of foreign nationals in Vienna evolve since 1971? What are the changes of the total population in relation to changes in the electorate since 2002? Are there any significant differences by age? This data-driven infographic by Ramon Bauer, Tina Frank and Michael Holzapfel provides answers to these questions – district by district.

Download the PDF poster!

See also:

• Interactive data map (coded by Clemens Schrammel)
• Blog post on Vienna’s electorate gap – district by district (by Ramon Bauer)
• Collection of Metropop contributions dealing with the topic of Vienna’s shrinking electorate

Although Vienna’s population is steadily increasing since the late 1980s, the growth of its electorate cannot keep up with population growth. Recent population gains are driven by an increasing influx of international migrants. The result is a growing democratic deficit. As long as foreign nationals are excluded from participating in citywide or national elections, Vienna’s electorate gap is getting wider and wider.

Check out the Metropop collection of related blog posts and data visualisations:

• Two faces of the 2015 Vienna elections (2015-10-12) – Maps by Tina Frank & Ramon Bauer.
• The citizenship dilemma (2015-10-09) – Blog post by Ramon Bauer & Markus Speringer.
• Vienna’s electoral gap – district by district (2015-09-20) – Blog post by Ramon Bauer.
• The Vienna electorate gap poster (2015-09-20) – Infographic by Ramon Bauer, Tina Frank and Michael Holzapfel.
• Interactive data map of Vienna’s electorate gap, district by district (2015-09-17) – Data visualisation by Ramon Bauer, Tina Frank, Michael Holzapfel and Clemens Schrammel.
• The shrinking electorate of Vienna (2013-09-02) – Blog post by Ramon Bauer.

Vienna Electorate analysis and visualisations featured in Wiener Zeitung:

• Wiener, aber keine Österreicher (2015-09-26) – Article by Matthias Winterer.

University courses in social sciences that deal with data traditionally have a strong focus on quantitative methods but do not consider how data-based evidence should be communicated, especially to a non-academic audience. Since most academics are not trained in communication, it is hardly surprising that there is a general lack of mind and skill sets to do so. However, graduates of the 4cities Master Programme are working on urban topics in sectors such as urban policy advising, planning, research, teaching, media and arts. Being a professional in these disciplines makes it a necessity to be an urban storyteller. In the digital age, which is also the age of data, a promising way of communicating quantitative information to a wider audience is data visualisation.

Besides data handling and analysis (which is the foundation of quantitative research) the course focused on the meaning of data and how data can be communicated and visualised. Since this is quite a lot for one semester we did not go into great detail, neither theoretically nor methodologically. The aim of the course was rather to enable students with different backgrounds to analyse urban data in order to communicate it visually. Well, considering the students’ feedback as well as my impression on the course, I can clearly say that teaching how to make sense of data and how to communicate quantitative information matters – and it is also rewarding.

To give an idea how the 4cities students approached the task of visualising a data-driven urban narrative of Vienna, find below a selection of projects elaborated during the course. The assignment given was to develop a draft for an interactive data visualisation or to deliver a static infographic (this particular assignment is obviously borrowed from Alberto Cairo‘s MOOC Introduction to Infographics, Data Visualization – a great course, which I attended in 2012). That means it was not required to actually code the interactive parts or to come up with a final design, but rather to elaborate und present a mock-up of the actual data visualisation project that represents the concept, the background of the story and the data used, as well as an idea about the interface functionality and design. For example …

Vienna in Squares – mapping Vienna’s building stock

by Bálint Halász, Elina Kränzle, Hala El Moussawi and Guillén Torres

A map of Vienna by sub-district level shows the proportions of private-owned residential buildings; a tree map illustrates the distribution by building period. Data source: Open Data Wien

Vienna in Squares is a visualisation that explores the spatial distribution of Vienna’s building stock. A gorgeous map based on squares (hence the title) displays three different levels (city, district, sub-district) along four characteristics (building type, property type, building period, amenities). When elaborated in detail and coded, this draft for an interactive data visualisation would definitely be a great online project alongside other visualisations that cover the characteristics of building stock – for example: Urban Layers, Block by Block – Brooklyn’s Past and Present or Buildings in the Netherlands.
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Bike Woes Wien – reporting, sharing and mapping pitfalls for cyclists

by Lorena Axinte, Sonja Dragovic, Lucie Rosset and Devon Willis – coded by Bojan Cincur

 Screenshot from bikewoeswien.com shows all reported woes on a map and also as a list.

Bike Woes Wien is an online platform that enables cyclists in Vienna to share their problems (“woes”). The project goes beyond a static draft for a data visualisation (i.e. the original assignment) and became a full website with an interactive map as a central tool (see also the neat heat map version). The original data comes from RadKummerKasten, an initiative of RadLobby that collected more than 1,000 complaints in Vienna since 2012. The website invites users to report and share their observations and experiences (see also promotion video), which makes Bike Woes Wien and related projects such as You Are Here or Bikenapped great examples for bottom-up initiatives that respond to urban needs and demands.

Vienna’s Underground World – mapping births and deaths along Vienna’s metro lines

by James Furlong, Sandra Jurasszovich, William Otchere-Darko and Maëlys Waiengnier

Main screen allows to choose (1) a topic (births, deaths, or natural population balance) and (2) a metro station or line.

Vienna’s Underground World is a project that maps birth and death rates as well as rates of natural increase across the different metro stations and lines of Vienna. The project was inspired by Lives on the Line (showing life expectancy and child poverty in London as a tube map) and by the Inequality and New York’s Subway visualisation. Using data from Open Wien, this project demonstrates the magic of spatial analysis. For example, allocating small-scale population data by proximity to metro lines and stations reveals that there is not a single neighbourhood surrounding the stations of the U1 metro line that exceeds the average birth rate of Vienna (see image below).

Result for a particular station (left) or for a metro line (right) – originally displayed underneath the main screen – see above. Data source: Open Data Wien

The course was an attempt to make an interdisciplinary group of urban studies students familiar with different quantitative methods (including GIS). The results prove that digging into a data set does not necessarily yield boring results and bored students. Focussing not exclusively on methods but also on the meaning of data and how to communicate it visually seems to be a promising way to spark the interest of students to work with data. I am already looking forward to the next course starting in March 2015.

Based on data from five Austrian censuses, researchers from the Wittgenstein Centre for Demography and Global Human Capital reconstructed Vienna’s demographic structure by age, sex and religion between 1971 and 2011. Their research project WIREL addresses the role of religions in shaping the social and demographic structure of the population of Vienna (I already reported on that in an older metropop post).

The increasing religious diversity of Vienna since 1971 was visualised by Ramon Bauer, Tina Frank, Anne Goujon and Clemens Schrammel. Their data visualisation connects the city’s changing demographic and religious landscape by highlighting changes in the religious composition as well as population size and age structure. The visualisation also shows how secularisation, migration, fertility and marriage patterns are linked to religious and demographic change in Vienna.

Try it yourself  by hovering over the chart area to change the period. Click on the four forces to see how they affect the population structure over time. Or even better, visit the webpage of the dataviz at www.wirel-project.at/dataviz – where you will find loads of additional information, as well as a more pleasant layout (compared to the slightly distorted iframe-version below which I had to squeeze into the slim metropop layout).

According to Anne Goujon, head of the WIREL research group, the aim of the reconstruction of the city’s religious composition is to produce a sound data basis for population projections of Vienna by age, sex and religion. Goujon and her team are already working on the scenarios for these projections which are planned to be published by the end of 2014.

Additional information

The WIREL dataviz website www.wirel-project.at/dataviz provides loads of additional information on the research project, the reconstruction of the religious composition, as well as links to a poster version of the data visualisation, the WIREL dataset and also to the source code of the interactive online visualisation at Github.

Measuring global migration flows is a difficult task because data on bilateral international migration flows often does not exist. Available migration data is usually based on information on foreign-born population stocks. New estimates developed by Guy Abel and Nikola Sander quantify bilateral international migration flows based on migration stock data from the United Nations. In order to communicate the new dataset on international migration, researchers from the Wittgenstein Centre for Demography and Global Human Capital together with coders from Null2 jointly developed a new interactive data visualisation, which illustrates the global flow of people (at www.global-migration.info).

Visualising global migration flows is also rather tricky. Commonly used migration maps that visualise place-to-place flows often fall short of conveying the complexities of human movement in an effective and visually appealing manner. Based on a circular plot, this new approach for visualising migration flows allows to explore the global flow of people between and within regions, as well as for individual countries, for five-year periods between 1990 and 2010. By hovering across the plot, the data can be experienced intuitively – allowing to explore complex flows that are otherwise hidden in a table with some thousand cells (see screenshot below from www.global-migration.info).

For example, were you aware of the globalised nature of out-flows from South Asia, …

… or how strong migration flows are within Africa relative to flows from Africa to more developed regions?

Try it yourself! … and also watch the short animation (produced by Nikola Sander) that explains the complexity of estimating and visualising the global flow of people in just 4 minutes.

Links, references and data sources:

• The Global Flow of People – Online data visualisation by Nikola Sander, Guy Abel, Ramon Bauer, Johannes Schmidt, Andi Pieper, and Elvira Stein.
• Download the data: global migration flows (1990 – 2010) – Dataset by Wittgenstein Centre of Demography and Global Human Capital (IIASA, VID/OEAW, WU)
• Global Migration Data Sheet (2005–2010) – Poster (pdf) by Nikola Sander, Guy Abel and Ramon Bauer. Vienna Institute of Demography (Wittgenstein Centre).
• Quantifying Global International Migration Flows – Article by Guy Abel and Nikola Sander. Published in Science, 343 (6178) (Accepted Version).
• Visualising Migration Flow Data using Circos, R and d3.js – Article by Nikola Sander, Guy Abel, Ramon Bauer and Johannes Schmidt. VID Working Paper 02/2014, Vienna Institute of Demography.