This was the country’s first-ever data-driven story about the sexual violence epidemic in our midst.
We worked with Women Unbounded, a feminist grassroots collective, to do this project.
Singapore’s society and mainstream media has often perpetuated rape myths and unhelpful attitudes about sexuality and gender that fail to constructively discuss and resolve the problem of sexual assault and violence. The advent of the #MeToo movement brought survivors’ voices to the fore, but skeptics often saw a trickle of one-off cases—mistakenly thinking the blame lay with a ‘problematic woman’—rather than understanding that this was a systemic problem. This story hence fills a much needed gap in public discourse by confirming key insights to spur policy changes: (1) that victims are often very, very young; (2) that they’re often victimised by people they know and trust; (3) that victims’ vulnerability is key, and a web of complicity by institutions and bystanders enable sexual violence; (4) that internalised shame and misplaced blame means most cases are never reported, and those that do report often do so at others’ behest and long after the incidents occured.
For the web development and data visualisation work: We used a combination of front-end tech, the scrollama.js library, and Flourish stories to build the data viz scrolly feature.
For general design:
Our illustrator used a mix of Procreate and Adobe Illustrator for the cover image; the visualisations were done by drafting on Figma first before refining it with Adobe Illustrator.
For data collection: We did it manually, delegating different time periods worth of data to different people to input into Google Sheets. We kept doing this for multiple rounds to ensure veracity.
What was the hardest part of this project?
The data cleaning and analysis was the hardest part of the project. Due to the lack of official data on this important issue, and the lack of mechanisms like Freedom of Information legislation, we had to gather data from publicly-available news reports. This involved a lot of manual searching of the articles and reading to isolate the right data.
It was also difficult to decide the relevant parameters for binning data, e.g., what age ranges to set and highlight, as that would affect the story angle and the readers’ sense of what is important.
What can others learn from this project?
This project shows how important three things are: collaboration, method, and topic selection.
This piece may not have happened if we hadn’t attracted the right collaborators. This means it’s important to figure out your organisation’s long-term values and goals so that it sends signals to the right people over time, both would-be consumers and collaborators, and they come to you. For us, that meant strongly signalling our commitment to doing cause-driven data stories, which then built up visibility and credibility for work with activists much later.
Another learning point was how important early stage of the data pipeline is—the challenge of collecting, cleaning, and analysing data—so allocate time and energy for that early stage. The dataviz world often enthuses about the final presentation aesthetics but, at least for this story, that wasn’t the hardest part. Especially when working with many data collectors who have varying experience levels with data, it’s so important to clearly communicate and systematise data collection, ensuring everyone understands the methodology, to prevent redoing and overlaps. This includes creating a definitions list or glossary, collectively discussing categorising and labelling, and version control.
Finding a good topic and marrying it to your strengths is key. This means finding the neglected aspect of the conversation, the niche that you can fill. For example, there’s been countless reports about sexual violence in the country over the years, but there hasn’t been a single piece that looks at it systematically. I think that’s why our story found such resonance: it used the core power of data visualisation—the ability to communicate scale and show patterns—to fill that gap in societal understanding of this topic. Data also lends truth and reliability to a topic that is commonly beset with perceptions of ‘subjectivity’, which is unfortunately often laced with misogynistic assumptions about women’s reliability.