I'm a data science enthusiast, but I also love investigating international relations and human rights. The human rights violation that affects me the most is the acts of the Myanmar government against the Rohingya in the Rakhine state. There were two United Nations General Assembly resolutions against the Myanmar government, and although both of them passed, there were nine nations that voted no and several other nations that abstained from voting. My background inspired me to investigate which nations voted no and what is their history.


Countries with higher refugee population tend to have greater human rights violations. The countries that voted no, or abstained from voting, in the two UN resolutions had high refugee populations and human rights violations. The human rights violations against the Rohingya have forced them to become refugees in neighboring countries, such as Bangladesh, India, Thailand, and Malaysia. Therefore, the purpose of this project is to reveal whether these countries, that voted no, stand against human rights violations against refugees since they have a high refugee population, or defend human rights violations to avoid the spotlight on their actions.

How I Built It

I imported and cleaned the datasets using Pandas (Python) in Google Colab. I created the visualizations in Infogram.

Challenges I ran into

The major challenge for me was figuring out what datasets to use and actually cleaning the datasets. I have a visualization for each nation's human rights score, but I had multiple datasets for that topic. When I narrowed down which one to choose, by credibility and relevance, I noticed the dataset had excess columns and it was over a large span of time. It took me more time than expected to clean that dataframe.

What I learned

My project revealed that the countries voting no, and abstaining from voting, in the two United Nations resolutions against the Rohingya defended human rights violations to avoid the spotlight on them. My final visualization shows a more in-depth human rights record of the nine nations voting no. Since nations with low human rights scores defend human rights violation resolutions in the United Nations, if all nations have multiple human rights violations, all violations will be defended in the UN. Russia and China, two permanent members of the UN, are live examples of this. Although the two resolutions concerning the Rohingya passed, based on the findings of my project, in order to stop this from happening in the future, I propose the United Nations should either implement a new system to measure human rights violations or stop nations with current violations from voting on human rights related resolutions.

What's next for Afifa Tanisa

An investigation of other resolutions concerning human rights and noticing if this trend continues

Datasets Used

World Bank Data

Data of UN Resolutions

Human Rights Data

Built With

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