Legislative Decision Chain: Beslutskedjan

Category: Open data

Country/area: Sweden

Organisation: Altinget Sverige

Organisation size: Small

Publication date: 19/03/2019

Credit: Altingets project team: Kasper Kaasgaard, digital editor, Christopher Bjerre, developer, Kristoffer Hecquet, Head of Projects, Amanda Bergland, project developer, Martin Lyngbæk Olsen, data journalist. J++ team: Leonard Wallenting, data journalist.

Project description:

Via open data from the Swedish Parliament, and scraping of the Swedish government homepages, “Beslutskedjan” collects, reformulates and package the entities of the Swedish legal desicion chain in a single view in a constantly updated visualisation and easily navigable taxonomy. The data is organised on a timeline, that is incorporated in all the journalism about legislation in the organisation (and vice versa).

Impact reached:

“Beslutskedjan” is a visualisation of the entire legal process in Sweden from the research stage in the government to the final adopted law. No other place in Sweden you will find this entire process on display digitally. Beslutskedjan gives every law proposal its own “chain” or timeline. The timeline also includes the journalism of Altinget and connects every other editorial elements that is relevant for the specific law proposal or “chain”.

The impact of Beslutskedjan is that it is bringing the Swedish democratic machine room closer to our readers; readers who are working with og very interested in politics. Since Beslutskedjan is autoupdated with new data, it will be helpful for legislators and political actors – as well as interested citizens – an ease their engagement in democracy continuously.


Techniques/technologies used:

The technical process wasn’t anything cryptic. The challenge of the project was mainly to understand the open data and to find a logic method where the complex legislative proces in Sweden match an automated technical structure, and to visualise it in a logical way, where readers easily can navigate and decode what is going on.

We started by mapping the data and the legislative process and hence we identified the best suited way to tap the open data, RSS feeds or scrape government pages. From here we started making autogenerated content. Then we tried to connect the dots partly with machine learning, and then visualise it through html and javascript.

Tools: Machine learning, Webscraping, regex, RSS feeds, C#, SQL, html, javascript. automation.

What was the hardest part of this project?

Firstly, it has taken a great deal of work to map and interpret the available data. Even if the data is open (at least for the Swedish Parliament) it does not always fit into the imagined purpose of the project. At som stages – mostly in the government data – there were some missings in the data, that made it necessary to make complex scraping coding to get the relevant information. And we have lobbied the Swedish parliament data-team.

It was also a challenge to develop a logical visualisation to display this complex data. Finally, every law proposal got its own vertical “chain” or timeline with all autogenerated legislative elements from the open data on the left side of the line. The timeline also includes the journalism of Altinget and connects every time other editorial elements is relevant for the specific law or chain. These are displayed to the right. All elements have a fixed date on the timeline to secure chronological overview.

Since Beslutskedjan is connected to all relevant articles at Altinget, we also had to find a visualisation that could be embedded in a simple display. This format did not at all fit to the concept of the vertical chains/timelines. Therefore, we had to develop a separate horizontal view to be embedded in the articles.

Another challenge is that the data is not stable. In government and parliament, people replace each other regularly and the whole setup changes every 4th year. But it’s worth the effort – at least to us. Beslutskedjan is highly appreciated among our readers and ask for more functions to be included. Next, we will introduce notifications on each chain, for the readers to stay oriented on a certain issue. Now we are developing a Danish version too. 

What can others learn from this project?

Working with open data on live basis can make very valuable journalistic products, but it is important to plan maintenance costs of the systems as data sources change over time. 

Another learning is that this type of content gives special value when it is broken down into smaller elements or views to be incorporated in the regular journalism. I. e. when a story on a certain law also contain the entire legal process as an “illustrative element” give great editorial value – and also contributes to a wholistic interpretation of the media as the article is more than the displayed text or content. – It’s also part of something bigger. In our case a “chain” in “Beslutskedjan” (helps to both acquisition on open content and retention on closed).


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