🍚 Food Insecurity Early Warning System
Image credit: UnsplashOverview
At the 2022 United Nations Big Data Hackathon, Team 4SKA1, formed by young Indonesian data scientists including myself, took on the global challenge of Food Insecurity. Our solution was a scalable Early Warning System prototype built using open data and visualized via Google Data Studio. The project won Best Visualization Award in the Youth Track for its clarity, insight, and practical utility.
The Challenge
Climate change and socio-economic instability are increasingly affecting global food security. Yet, early warning systems are often absent, especially in vulnerable regions. Our task was to answer:
- Can we monitor and anticipate food insecurity trends globally?
- How does a country’s food insecurity affect others through trade flows?
Our Approach
We created a predictive model using data from:
- The UN Hackathon 2022 datasets
- World Bank, AtlasBig, and OurWorldInData
We engineered three key scores for each country:
- Socio-Economic Score (SES): clustering countries based on unemployment, undernourishment, mortality, etc.
- Climate Score: using PCA to combine metrics like CO₂ emissions, energy use, forest area.
- Food Score: integrating food production, consumption, waste, and trade patterns.
These were combined to train a linear regression model to estimate national food insecurity scores with ~10% error.
The Dashboard Prototype
We built an interactive Google Data Studio dashboard to visualize:
- Global food insecurity status by country
- SES, Climate, and Food score comparisons
- Country-level food trade relationships
- Exploratory visualizations to empower policy and humanitarian decisions
