Climate change poses real and present danger to the future of the world. Also at stake is practically every major life form’s existence that resides on this planet. Primarily, addressing climate change requires developing awareness in peoples’ minds. We want to devise a time-series model on the socio-economic factors contributing to climate change, and predict future conditions if human actions proceed as they do today. More gener- ally, we plan to create an interface which provides visualizations to trigger climate-aware thought-processes and provides some useful insights through basic statistical and machine learning methods.
We will be using various datasets from the World Bank’s OpenData bank [1] and Berkeley Earth[2]. Some of these include:
- CO2 emissions (metric tons per capita)
- Electric power consumption (kWh per capita)
- Energy use (kg of oil equivalent per capita)
- Forest Area
- Improved sanitation facilities (% of population with access)
- Mortality rate, under-5 (per 1,000 live births)
- Population growth (annual %) and Population, total
- Renewable electricity output (% of total electricity output)
- Literacy - School enrollment, primary and secondary (gross)
- Urban population (% of total)
- Average Annual Temperatures of all Land Area across countries - Berkeley Earth • Registered vehicles - Data by country (WHO)
- Backend: The backend will involve using Python, scikit-learn, Pandas, NumPy, SciPy.
- Frontend: The frontend will be in HTML, CSS, Javascript and D3.js for visualization.