Climate risk means different things to different people. For a farmer in Nsanje it means flooding. For a planner in Blantyre it means both flooding and drought, happening in different places at the same time. The challenge is not understanding that risk exists. It is quantifying it in a way that supports decisions.

This was my attempt to do that systematically across all 28 Malawian districts.

The framework is the IPCC AR5 risk model, which defines risk as a function of hazard, exposure, and vulnerability. I operationalized each component using

measurable climate indices derived from daily meteorological data. The primary indices were the Standardized Precipitation Index for drought hazard and the Coefficient of Variation for rainfall variability. Normalization used 5th to 95th percentile clipping to handle extreme outliers without distorting district-level comparisons.

The pipeline processes over 50,000 daily meteorological records from 2020 to 2024, sourced from the NASA POWER API for all 28 districts. Each district gets its own time series, risk score, and index decomposition.

The Streamlit dashboard visualizes risk scores geographically and breaks down the hazard, exposure, and vulnerability components per district through radar charts. The goal was to build something a non-technical decision maker could open and understand immediately.

Working with district-level aggregation confirmed that spatial scale matters enormously. A national average hides everything. Nsanje ranking highest is consistent with what the climate record shows about the Lower Shire Valley: flood exposure, extreme heat, low adaptive capacity. The data reflects what the geography already tells us.

This version has since been rebuilt. The methodology has been refined, the map is a proper choropleth with 3D terrain, and all weighting decisions are documented explicitly. The full story of what changed and why is in the v2 post.

Live Demo: Streamlit v1 | v2 Dashboard | Why I Rebuilt It