Project description

\”We create visual and data-driven stories and build tools that power them.\” This is our claim. And our goal is to transfer the excellence of 250 years of journalism (NZZ was founded in 1780) into the present and future. As a team of designers, videojournalists, datajournalists and developers, this means that we:
…build and maintain a toolbox that allows editors to build simple charts by themselves right when they need it
…cover breaking news situations using scripts.
…we investigate and explain complicated matters with data, graphics, videos and illustrations.

What makes this project innovative?

1. Our toolbox Q has empowered all editors of the newsroom to do visual storytelling. 2. We covered the various aspects of the midterms election ranging from the general democratic shift in the house over the record number of elected women to especially interesting races for certain seats. 3. We developed a new, more accurate, script-based way of visualizing hurricanes. 4. We used the cryptic measure of "expected goals" and explained it to the average reader by presenting the "fair last 16" of the football world cup. 5. We showed how and why regional dialects in Switzerland start to change between generations with videos and graphics that visualized diverging sounds of dialects. 6. We found out that islamist terror attacks are more deadly and function differently than other forms of terrorism and narrated with graphics, data-driven illustrations and video. 7. We investigated in how many conflict zones Switzerland is exporting weapons despite having rules that should prevent that.

What was the impact of your project? How did you measure it?

1. Thanks to Q, we have now 13500 graphics in use and visual storytelling has become a regular part of news stories and helped to make readers engage more with our content.
2. The midterms article was the most-read article of the month and was also featured in multiple discussions in the data viz community about how to break this event down for non-US readers.
3. We created 16 hurricane graphics within minutes compared to a tedious process that meant everything had to be handcrafted.
4. The alternate best 16 football article was one of the most-read article of the sports-section and a long runner with above 45000 engaged minutes and 1:07 minutes of average engaged time.
5. This was one of our most-read features of the month and sparked a discussion in our newsroom how data journalism does not have to end up in a lot of graphics but can be a data-based piece.
6. The terrorism article clocked at 78500 engaged minutes and was praised by experts such as Peter Neumann. To our knowledge, it is also the first example of a data-driven investigation that examines whether separatist or islamist terrorism is statistically different in its modus operandi.
7. The article was part of the national debate that led to the parliament not loosening its current rules regarding export of weapons to conflict-involved countries.

Source and methodology

1. The documentation for Q is here https://nzzdev.github.io/Q-server/ 2. Our visual overview for the midterms was based on a dpa data stream and r-scripts to process them 3. The hurricane data comes from NOAA https://www.nhc.noaa.gov/gis/ and the methodology is explained here https://github.com/nzzdev/st-methods/tree/master/1825-hurrikankartenmethodik 4. The expected goals story was based on data provided by Opta. 5. The dialect data was provided from an app. The pros and cons of the data are explained here. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143060 6. We used the Global Terrorism Database https://www.start.umd.edu/gtd/ and coded terrorist groups using four sources ourselves. The code for the further processing is here: https://github.com/nzzdev/st-methods/tree/master/1823-Terrorexplainer 7. The investigation on export of weapons is based on official figures published (but unfortunately in PDF format) and the Uppsala Conflict Program https://ucdp.uu.se/ . The code, which also uses prior work of our colleagues from SRF data, can be found here https://github.com/nzzdev/st-methods/tree/master/1834-waffenexporte-konfliktparteien

Technologies Used

1. NPM, CouchDB, Hapi.Jsm Node.js just to name a few 2. R-Scripts and Sketch 3. R-Scripts and Sketch 4. R-Scripts and Sketch 5. R-Scripts and Sketch 6. Python Pandas, Illustrator 7. R-Scripts and Sketch

Project members

David Bauer, Anna Wiederkehr, Markus Stein, Beni Buess, Manuel Roth, Philip Küng, Sharon Funke, Marie-José Kolly, Andreas Rüesch, Alexandra Kohler, Stefanie Hasler, Anja Lemcke, Christian Weisflog, Michelle Feer, Fabian Urech, Michael Surber, Andreas Rüesch, Daniel Steinvorth, Andres Wysling, Patrick Zoll

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