The looming risk of tailings dams

Category: Open data

Country/area: Singapore

Organisation: Reuters

Organisation size: Big

Publication date: 19/12/2019

Credit: Moira Warburton, Sam Hart, Júlia Ledur, Ernest Scheyder, Ally J. Levine

Project description:

When a dam storing mine waste — called tailings — collapsed and killed over 240 people in Brazil, a spotlight was turned on the safety of such dams. The Church of England, an investor in mining companies through its pension funds, called for more data on them. Reuters gathered the firms’ public responses and visualized for the first time, tailings dams owned by 89 companies with mines in over 60 countries.

Impact reached:

At a time where there are currently no established global mining industry standards defining what a tailings dam is, how to build one and how to care for it after it is decommissioned, this project is a great example of service journalism as it provided a valuable first look at tailings dams on a global scale, increased awareness about these little-understood structures, and prompted further discussion among those working towards unifiying dams classification standards and preventing catastrophic dam collapses. This visualization was widely viewed and shared by industry experts, environmental groups, politicians, concerned parties and representatives of the Church of England’s pension board as it illuminated the potential dangers of tailings dams and the company stakes in a clear and concise way.


Techniques/technologies used:

The team made use of several data analysis tools to organize and geolocate the tailings dams. HTML, CSS, D3, Javascript, Adobe Illustrator and QGIS were used to produce the maps, graphics and the guided experience breaking down all 1,700 tailings dams. Lightwave was used to create 3D diagrams to explain the different ways a tailings dam can be constructed and why some are more dangerous than others. The team also analyzed a large amount of satellite imagery to identify distinct features in notable tailings dams, giving readers a bird’s eye view of these little-understood structures.

What was the hardest part of this project?

The hardest part of the project was organizing and factchecking the vast amounts of disclosures and data made available by the companies as they are often incomplete and/or inconsistent. The team spent months collecting and cleaning up the database to ensure that the data is accurate before we began analyzing and visualizing the information. After the data was organized, we had to figure out a way to structure the narrative so that the reader could easily digest the information about these little-understood structures and the potential danger they pose to communities around them.

What can others learn from this project?

When visualizing large datasets, it is important to provide the reader with a guided experience so that key takeaways can be easily understood. During the course of the project, the team and others who have seen the project were constantly shocked by the scale of the structures. It was therefore crucial that we balance data with imagery and show these dams in the context of their environment.

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