Futuro Aqui

Country/area: Portugal

Organisation: Público

Organisation size: Big

Publication date: 16/9/2021

Credit: Rui Barros, Sofia Neves, Nuno Ferreira Santos, Inês Chaíça, Claudia Carvalho Silva, Francisco Romão Pereira, Adriano Miranda, Filipa Almeida Mendes, Duarte Drago, Loraine Vilches, Gabriel Sousa

Biography: Rui Barros is a data journalist/ journocoder/ news nerd currently working at PÚBLICO, a daily newspaper in Portugal.

Project description:

The housing problem in Lisbon, how the pandemic affected Algarve’s tourism industry, and climate change. Portugal went to the polls in September to elect 308 mayors. At Público we wanted to answer a simple question: is it possible to have a future here? We used data to find the municipality that was more affected by these issues – even though they were simply representative of a national problem. Then, a team of reporters went to these places to understand the local reality, and how it is affecting people’s lives. And what the different candidates were proposing to solve it?

Impact reached:

The three articles all began with a close-up on the data available in Portugal, highlighting the region of the country that has a deeper problem – and thus begins the reporting there. We decided that using scrollytelling we were able to write a “story before the story” – explaining that the issue is being felt all over the country, but that some regions are being affected way more than others. Every piece also included a small news app that allowed people to explore the data to their local municipality, showing them how close the place they live is similar to that place.

That was the most praised thing by our readers: suddenly, a local story became way more interesting to the whole country because 1) we were showing that the country, as a whole, was being affected by it and 2) that the place where the reader had the same problem, but probably in a smaller scale.

Techniques/technologies used:

For the data, we used R to collect and analyze the data and do some quick charts to discuss with the team why that specific municipality was the one being chosen. This programming language was also used to publish that data into JSON files that were used on the data visualizations We then used flourish and some custom data visualizations to develop the scroller and the custom news applications inside the stories. The web development was done by the data journalist in charge of all three stories and it was built using archieml to parse the text from the Google Docs where the reporters were writing the stories and Svelte.

What was the hardest part of this project?

Finding the municipality was the most changeling thing about it because it meant scrapping, searching, and often doing the good old call to a lot of public institutions to find the right place where reporters would be doing some ground reporting. It was also a challenge coordinating a team with so many reporters.

What can others learn from this project?

When faced with so many potential stories, pick the outlier that you believe is representative of the population. It is a task that we, data journalists, tend to find hard: because numbers seem more robust when they describe a trend with multiple data points. And even though that is important, it’s always way more efficient to pick a specific data point and explain to your reader: even though this happens in way more places, we picked the place where the problem affects way more people/lives.

That leads to a lesson that I believe many data journalists already understood: data is a wonderful way to tell a story, but having a human face behind those numbers makes it way more impactful.

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