Bridging the gap between climate science and technology through data-driven research and geospatial intelligence.
National climate risk assessment platform scoring all 28 Malawian districts using the IPCC AR5 framework. Combines NASA POWER climate data, World Bank socioeconomic indicators, and EM-DAT disaster records into a composite risk index, visualised through an interactive Mapbox GL JS map with district-level breakdown panels.
Geospatial decision support system for Blantyre City Council mapping 333+ illegal dumpsites across city wards. Applies a custom 0–100 risk scoring engine weighted across health, environmental, and volume factors, and uses DBSCAN clustering to recommend optimal waste collection skip placements.
Python-based climate risk assessment platform applying the IPCC AR5 framework across all 28 Malawian districts. Processes 50,000+ daily meteorological records (2020–2024) from NASA POWER to derive SPI and rainfall variability indices, producing a composite risk score visualised as an interactive choropleth and per-district radar chart.
Interactive world map visualizing CO₂ emissions per capita across countries from 1990-2020. Built with Streamlit and Plotly, featuring dynamic year selection, global choropleth maps, and top emitters analysis. Data sourced from World Bank ESG indicators.
Analyzed MWK 16B+ in climate finance across 48 projects (2018-2023). The study revealed an 85% reliance on international grants, highlighting the urgent need for better domestic resource mobilization policies.
Re-examined UC Berkeley's admissions data to demonstrate Simpson's Paradox. The project illustrates how aggregate data can conceal underlying biases, emphasising the importance of granular analysis in social research.
Through the Femanalytica-DataCamp Scholarship, I completed several data analysis projects. I've featured 6 of them on my GitHub with detailed write-ups. View the project highlights below, or tap the icon in the bottom right to explore the full repo.
Analyzed historical warming trends across Malawi and compared them against future climate projections from the MIROC6 model to evaluate differences between observational data and model outputs.
A tool I wish I had back in my undergraduate days. It helps visualizing the earths energy balance and provides a foundation for understanding how models work in the background. It serves as a great learning tool for those looking to explore the earths energy balance.
A professional, Python-based climate analytics application built with Flask and Tailwind CSS. Designed to monitor real-time weather conditions across major Malawian cities with a premium, glassmorphism UI.