2021 Shortlist

Assessing Australia’s ecological disaster

Country/area: Singapore

Organisation: Reuters

Organisation size: Big

Publication date: 21 Jan 2020

Credit: Simon Scarr, Manas Sharma, Marco Hernandez

Project description:

Australia’s government called the bushfires crisis of 2020 “an ecological disaster.”  Reuters delivered the first data-driven analysis of fires and habitat data, showing how hundreds of species suffered.

 We processed massive amounts of satellite-derived fire data and habitat information. By calculating the intersection of those datasets we were able to reveal the animals hardest hit by bushfires.  

Many species, including some that are critically endangered, have seen large swathes of their environment destroyed. Some of these species have had the majority of their territory wiped out, raising fears of extinction.

Impact reached:

This was the most in-depth analysis of habitat damage published at the time. Other news organisations and government agencies were publishing estimates and approximate headline figures, but we were able to give a detailed account of every animal individually.

After publication we were contacted by individuals and wildlife organisations asking about access to the raw data and analysis. It was seen as important information as attention turned to rebuilding habitats and protecting the species that were most vulnerable.

Techniques/technologies used:

We processed massive amounts of satellite-derived fire data and slowly built up our own “burned area” analysis. We then took more than 1,400 habitat spatial files and ran batch calculations in order to find the intersection of the two data sets, revealing the burned percentage and acreage of each habitat. This exclusive dataset showed us which animals were hardest hit by bushfires. 

There was also a large amount of detailed cartography, satellite imagery analysis, and hand drawn illustration to tie the whole piece together as an immersive experience.

What was the hardest part of this project?

The preparation and processing of all of the data was a monumental task. There was a lot of work to do before processing the calculations.

What can others learn from this project?

This heavy data journalism exercise could have been presented as a straight forward exclusive story, but this level of production and the visual experience helped bring the data to life.

Project links: