Project description

I am a data journalist and graphic designer from Switzerland working for the news site watson.ch. I just finished my certificate of advanced studies in data journalism at the swiss school of journalism (MAZ). Together with two other journalists I established the data team for watson.ch in April 2018 and we already produced many data driven stories for younger audiences ever since. Our readers live all over Switzerland and we always try to be close to them and write about topics that are relevant for each individual reader. We are doing this for example by creating detailed interactive maps in which you can look up data for your very own village.

I have a background as information technologist and therefore enjoy writing codes to scrape content from the internet, clean it and prepare it as static or interactive content for our stories. In my opinion being a data journalist is a dream job because of the ability to dive into new topics every day. I love to work with all the great storytellers in the newsroom and learn from them.

The stories listed below are a selection of what I’ve been working on in this first year. I was involved from the very beginning when we discussed the idea, but also in later stages of the project with getting and preparing the data to visualize them in the best way for our readers.

What makes this project innovative?

Project 1: The rise of Ava Max: How fast music stars reached their first number 1 hit in Switzerland Inspired by the young artist Ava Max who celebrated her first number one hit within four weeks (without ever been placed the the Swiss charts before) I took a look at how fast other famous artists reached the «number one». I scraped the data from the billboard charts website and did the calculation in python pandas. As a highlight I added an interactive part to the story where you can look up your own artists and take a look at how fast they made it to their first number one hit. Project 2: All games and goals of the World Cup: In advance of the Football World Cup 2018 in Russia we talked to Fifa and asked for datasets about every game and goal that was ever scored in a World Cup final round. It was quite a challenge to digitize and prepare the old data, which was only available on paper, for an analysis. But as a team we managed to get information about which nations are most likely to turn around a game last minute or who played most frequently against whom. An interactive graphic let you find out how your favorite team managed the previous World Cups. Project 3: How much CO2 does a flight through Switzerland produce? Because climate change is an important topic to us, we decided to create a new quiz format in which you can guess how harmful a domestic flight is in terms of CO2. Project 4: How is your neighbour called? We also work with open data sets, for example in this story here in which we created a map to look up the most common first names of your neighbourhood. We also created choropleth maps where regions with many occurances of a certain name are highlighted.

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

We are very proud of the discussion platform below every story on watson.ch. Every feedback is manually approved based on various criteria to create a meaningful discussion. It’s always nice to receive feedback about how people react. We also use Google Analytics to track if people even realize that a certain part of the story is interactive and also use it. Ultimately we carefully monitor feedback on Social Media channels and use it to improve details of future stories.

Source and methodology

Right before we start to research we usually contact experts on the chosen topic to ask for his or her opinion about the planned story. Their feedback already gives us a first insight to decide whether the story is worth the time to produce it. This is crucial to us, as our team isn’t that experienced yet and producing stories takes us a lot of time. For most of the stories we use data from open source platforms or data provided by the federal statistical office of Switzerland. We always try to verify the outcomes – in particular the surprising ones – of the story with experts within our newsroom and if possible also external ones. Also we put out code on GitHub to show people how we managed to get the final results and let them reuse code.

Technologies Used

We are working with a handful of tools such as Google Sheets, Python (including many libraries such as Pandas, requests, selenium, BeautifulSoup, ...) for the scraping and analysis, the JavaScript library D3.js for the interactive visualizations and Adobe Illustrator for static infographics. We also use Datawrapper, infogram, carto.com and other tools to prepare data for a story. We are constantly looking for new tools and try to invest in these to make the user experience even better.

Project members

World Cup project: together with Reto Fehr and Marius Egger Climate quiz: together with Fabio Vonarburg

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