MadalitsoMaster/Flood-Climate-Risk-Analytics-Chikwawa-District-Malawi
Domaine:
climategeospatialCréateur:
Mad
Hôte:
Flood
**Flood & Climate Risk Analytics – Chikwawa District, Malawi**
**## Overview**
This project demonstrates a **flood and climate risk analytics workflow** for **Chikwawa District, Malawi**, conducted at **Traditional Authority (TA) level**.
The analysis integrates flood probability data, administrative boundaries, and population exposure to produce **decision-ready outputs** that support disaster preparedness, response, and planning.
The workflow reflects real-world analytical processes used by disaster management institutions such as the **Department of Disaster Management Affairs (DoDMA)**.
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**## Objectives**
* Analyze spatial patterns of flood probability in Chikwawa District
* Assess population exposure to flood risk at TA level
* Classify and rank TAs based on flood impact severity
* Produce SitRep-ready tables and professional maps for operational use
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**## Data & Methodology**
The analysis combines:
* Flood probability raster data
* Traditional Authority (TA) administrative boundaries
* Population exposure datasets
**Key analytical steps include:**
* Raster sampling and aggregation
* Geospatial joins and TA-level summarization
* Flood risk classification (Extreme, High, Medium)
* Priority ranking to guide response planning
All processing and analysis were conducted using **Python and GIS tools**, ensuring reproducibility and transparency.
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**## Tools & Technologies**
* **Python**: Pandas, GeoPandas, Rasterio, NumPy
* **Visualization**: Matplotlib
* **GIS**: QGIS (data preparation and validation)
* **Environment**: Anaconda, Jupyter Notebook
* **Version Control**: GitHub
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**## Key Outputs**
The project produces the following decision-support outputs:
* **TA-level Flood Impact SitRep Table** (CSV)
* **Flood Impact Risk Map for Chikwawa District** (PNG)
* **Reproducible Jupyter Notebook** documenting the full workflow
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**## Operational Relevance**
This …