The whole data journalism team at Spiegel Online, as well as our collaboration partners at BR Data and Correctiv.
I\'m a data journalist at Spiegel Online since 2015. Working in a rather small team is a constant challenge of your skills and abilities. During the past year I\'ve covered a huge range of topics and have worked on every bit from data collection through analyzing and visualizing data to building dataviz front-ends and writing stories. These are the 9 stories I\'d like to pitch as my portfolio:
1) Commuting in Germany - a personalized story
2) Measuring rental housing discrimination in Germany
3) Explaining election results in a series of animated maps
4) Unusual islands of political coalitions
5) The socio-demography of party strongholds
6) Euros for doctors
7) Mapping nuclear reactor risk in Europe
8) The most timeless songs of the past decades
9) Series: measuring and visualizing football coach performances
What makes this project innovative?
What I would describe as innovative in my last year\'s work:
1) PERSONALIZATION: For our story on commuting in Germany there was not only one version, but literally hundreds of thousands. The text as well as content and order of the visualizations is dynamic and depends on the time of day, where the individual reader lives and works as well as on several more questions relating to his or her personal commute.
2) ENHANCE EVERYDAY REPORTING: Coach performance discussions in football are often anecdotic. I\'ve developed an automated workflow that helps our sports reporters to include simple, yet meaningful stats.
3) NO DATA? GENERATE YOUR OWN: How do you prove that there is discrimination on the rental market if there is insufficient data? For our project \"Hanna and Ismail\" we\'ve ventured beyond the ordinary borders of data journalism and conduced our own large scale undercover research. We sent out automated apartment inquiries, evaluated the answers and eventually proved that people with foreign names are clearly discriminated on the German rental market.
Where I think my reporting made the biggest impact:
1) INVESTIGATIVE: Data journalism is a powerful tool for uncovering otherwise hidden issues. This not only applies on our study on the German rental market but also on our ongoing project \"Euros for doctors\", where we\'ve been unveiling payments to doctors by pharmaceutical companies for the second year now.
2) ADDING CONTEXT TO BREAKING NEWS: One of the first duties for many data journalism teams was live election mapping. Fortunately, I had other duties during the 2017 German federal election. Together with our small team we\'ve focused on explaining the election results, rather than describing them. We\'ve worked a night shift and produced four very different articles, all of them becoming lead stories on Spiegel Online in the 36 hours after the election.
What was the impact of your project? How did you measure it?
Even as a big voice in journalism we usually don\'t change politics or people\'s lives. Our biggest impact is probably informing the public, helping our readers to better understand a topic. The best measurement we\'ve got in that respect: probably still individual reader feedback.
Source and methodology
Too many to name them all here. Every project is different. All of our bigger projects include at least a separate FAQ on this, if not a complete documentation and/or reproducible workflow on github.
I use the R programming language (relying heavily on tidyverse packages) for scraping, processing and analyzing data, as well as for producing preliminary visualizations.
The more simple final graphics are produced in Adobe Illustrator (static versions) or Highcharts (simple, interactive charts) whereas more advanced visualizations are realized with D3.js. For maps I work with a combination of QGIS (data processing, exploration as well as styling static maps) and Mapbox.js / MapboxGL (interactive maps).