A data-driven story about the impacts of fast fashion and why it’s important to practice sustainable methods of obtaining clothing.
In the digital age, fast fashion has become very trendy. The fashion industry has always been extremely pollutive, but this trend has caused the growing middle classes of Southeast Asia to buy a lot more clothing and thus generate more waste and pollution in recent years.
Thankfully, a portion of consumers in the region have already been using sustainable methods of obtaining new clothing, including upcycling, recycling, clothes swapping, and clothes renting. These methods are just not that popular yet.
This story aims to bring attention to the issues that each country in Southeast Asia faces, as well as the methods in which we can become more sustainable with the way we consume clothing.
We used Python to plot the dot plots and bar plots as drafts and then moved them into Figma for further styling, as well as to add illustrations and annotations.
Python is helpful for scaling, sorting, and analysing large numbers, and is often a good foundation for data visualisations.
Figma is great for styling and adding small details manually.
What was the hardest part of this project?
One tough part was gathering data and information that would be “new” to the reader, as well as trying to write about a familiar topic from a different angle.
We also had to include an innovative way to call our readers into action, especially since the majority of our audience is from Singapore and sustainable methods of obtaining clothing are still not that familiar to this community.
What can others learn from this project?
The use of interactive data visualisations help to communicate important data in a manner that’s easy to observe.
For the last portion of the story, we added a map to showcase spatial data—specifically the nearest places where our readers can go to get clothing made of sustainable materials, secondhand shops, clothes-swapping places, and clothes rental areas. This provides the reader with the information that there are a few places around that they can go to, as well as the locations that are nearest to them.