Project description

I am 25 years old and working as a data journalism trainee at the German newspaper Süddeutsche Zeitung in Munich, Germany. I am also finishing my Bachelor program at the TU Dortmund, studying science journalism with emphasis on data journalism. I co-founded the Journocode UG, a young organization dedicated to forwarding data journalism and data literacy skills. In a team of young journalists, designers and programmers, I give workshops on those subjects and organized a one-day data journalism conference in Berlin in March 2018 (https://serve.journocode.com/journocon18/) as well as organized several data journalism advent calendars (http://advent18.journocode.com/, https://advent17.journocode.com/, https://advent16.journocode.com/).

In my role as trainee at the Süddeutsche Zeitung\’s Entwicklungsredaktion, I do long-term ddj projects and short-term data pieces on news topics. I try to apply the knowledge I gather in the longer projects on shorter deadlines. I am most often responsible for the data wrangling and coordinate with my journalistic as well as infographics colleagues to bring the best obtainable version of the story to our readers. While trying to do that, I also rely on my professional and experienced team, that is knowledge-thirsty as well and often has some advice to share with me.

The attached projects are supposed to show my interest in certain topics as well as different ways to report on them. An interesting topic for me in the last year was climate-change and the plethora of data about it. The other characterizing topic has been rent. It\’s something that almost everyone cares about and we wanted to approach the topic in a special way. Other projects show short-term articles on different topics. Getting data quickly, cleaning and understanding it on a close deadline is something that I\’m trying to get better at.

What makes this project innovative?

A strong focus of mine in the last year was on climate-change and weather. It fascinates me not only because it's probably the most relevant topic of the present and for decades to come, but also because there are thousands of people around the world who are producing, analysing and publishing an endless amount of data around it. To find and present this to the reader is a challenge that intrigues me (Additional Links #1, #4, #6, #8). Another big topic in the last year was rent. Together with my colleagues at the Entwicklungsredaktion and an ensemble from several departments in the house of the SZ, we crowdsourced a survey on the strain, that rent puts on people all over Germany. It was a big challenge from beginning to end: Conceptualizing a questionnaire, gathering, analyzing, understanding the data, and presenting the findings to our readers. Some 57 000 people took part in that survey (Main link + Additional link #7). Additional link #2 was a very quick piece on the situation in Venezuela. Together with a colleague I gathered data and used the tool Datawrapper to visualize it quickly. Additional link #5 is a piece on the federal state election of Bavaria, Germany in October 2018. I really liked doing this, because I prepared a programming script upfront, that had to work without any problems at the night of the election. This was very interesting because there was no extra design work to be done, neither in the infographics department nor with the online data visualization tool we use. The graphics that were published on the same night came straight out of R. (Even though there were some problems that had to be fixed quickly, of course, as there probably always are.) Additional piece #3 was a Twitter data analysis of Donald Trump, or rather @realdonaldtrump. Twitter analysis has been done before, by me and by colleagues, but this time I took the time to extract some more information from the Trump Twitter Archive than before: I used a library for Natural Language Processing in R to look at which verbs, nouns, adjectives he had used most often. He really likes the adjectice "great" — Suprise!

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

The projects I did performed rather above-average, though it fluctuates from topic to topic. To measure that, we don't just look at clicks but at a combination of variables like social interactions and spent time. For the project on rent in German cities, #MeineMiete, we got a lot of positive feedback from readers. And to repeat: 57 000 people took part in the survey. Even though Twitter is really small in Germany, we also get feedback on there regularly.

Source and methodology

When it's possible, I publish the methodology of pieces I work at. For the piece on rent in Germany, you can find it here: http://sz.de/mietmethodik (German only). The methodology of the piece about the trend of snow is together with the full longread behind the paywall (https://projekte.sueddeutsche.de/artikel/wissen/klimawandel-der-winter-wird-kuerzer-e900173/). Unfortunately, because of the timeliness of some of the projects, I didn't find the time to write down a detailed methodology. Important hints, however, are always included in the finished graphics. If there are irregularites in or open questions about the data, I contact the authors or people familiar with the data to clear it up.

Technologies Used

To wrangle with the data, I use R, the IDE RStudio and the tidyverse packages by Hadley Wickham, most importantly dplyr and ggplot2. Even though I seldom publish graphics directly out of R, with the exeption of the election night graphics (Additional link #5), I do research visualizations in ggplot2 to understand the data, look at outliers and trends. In the Entwicklungsredaktion we use a broad mix of technologies to present our stories pleasantly. There's Python for more data wrangling and Javascript as well as CSS for frontend beauty. For the graphic in the Munich aspect longread about the rent situation (Additional link #7) we used the Javascript library D3 to animate and explain it step-by-step. This step-by-step explanation is something we're trying more and more, because we find that it lets the reader understand the story and the point of the graphic more clearly. Readers could also personalize the graphic by selecting a zip code from Munich and explore the rent situation for different demographic groups for themselves.

Project members

Moritz Zajonz

Link

Additional links

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