Project description

To improve the public understanding on Taiwan’s latest ten referenda results alongside the midterm local election in November 2018, we built this interactive data visualization website right after the referendum. The referendum proposals included same sex marriage, gender equality education, power and air pollution. Although the referenda proposals were unprecedentedly broad and important, the major media’s data visualization work and articles focus on the local midterm election rather than the referenda.
On the website, we gave every referendum a brief introduction, plotted the total voting shares as a pie chart, colored local support rate on map, drew a set of scatter plots of demographic statistics versus support rate by county, and appended a detailed reasoning after the visualizations.
We hope the visualization helped the general public in Taiwan to learn the referenda results overview and explore the support rate distribution of each proposal among regions and its correlation with local demographic characteristics. While major media focused on the election results, we thought referenda could reflect citizen’s direct intention on different issues. Hence, the referenda results deserve more explorations and deeper studies.
The website was in both English and Chinese. We hope the English version to help people who want to learn more about Taiwan but do not read Chinese to understand people’s opinions in Taiwan.

What makes this project innovative?

Our project is the first one presenting the complete ten referenda results through interactive data visualization. Also the website is the first one focusing on referenda and its interaction with demographic data in Taiwan. It provided further research possibility based on the exploration instead of only presenting the results. The project was initiated two weeks before the election and referendum. Our members come from all over the world, in four cities across three time zones in North America and Europe. We succeeded to design, develop and release the website within two weeks. We set the project scope and execute the plan precisely to achieve our goal: present a thorough visualization of referenda for people to freely explore and release it quickly after the results coming out.

What was the impact of your project? How did you measure it?

The visualization was soon shared over a thousand times on social media after release. The share number and views were more than the number of many similar coverage story produced by major media in Taiwan. Many thought it provided a thorough overview on the referenda results and an initial idea for further research. Some academic professionals utilized our data to develop follow-up study based on our exploration.

Source and methodology

The referendum results were real-time scraped from Central Election Commission in Taiwan ( The demographic data were from government open data website. Marital status and education, 2017 is from Ministry of the Interior ( Age distribution, 2018-Oct is from Ministry of the Interior ( Income, 2016 is from Ministry of Finance ( Besides, we collect more demographic data such as divorce rate, average age, religion and so on before the referenda. We chose the more linear correlated, no matter positive or negative, variables in our final presentation.

Technologies Used

We are volunteer students studying abroad in four cities across the globe. Our communication for this project were all on Slack and even no video or audio calls but everything went smoothly. To collaborate non-coder team members, we create a json file for the website content. The content owner edited the json file and the website import this json file as its text content so we didn’t need to copy and paste back and forth for content updates. The scraper for the election and referendum results were in python. ( The interactive map were support by plotdb ( while we hacked its javascript codes to customize the map and fix bugs. Other visualizations were in D3.js. (, see: js/*)

Project members

Content & Translation: Linda Wu, Lily Wu Visual Design: Yu Lin Front-end Design: Ansin Lau Engineering: Ponan Li Interactive Design & D3.js: Claire Tsao



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