flawiya/East-Africa-Worldpop-Spatial-Growth

Spatial analysis of population growth in East Africa using WorldPop data ## East Africa Demographic Shifts: Spatial Analysis (2015-2025) ### 🌍 Project Overview This project leverages geospatial data to analyze population growth patterns across **Uganda, Rwanda, Burundi, and Tanzania**. By classifying demographic shifts into Decline, Neutral, Growth, and High Growth categories, this analysis aids in identifying urbanization hotspots and resource allocation needs. ### 📊 Key Geographic Insights Based on the spatial analysis output (Map 2): * **High-Density Growth:** Intense population pressure ("High Growth") is concentrated in the **Lake Victoria basin**, particularly impacting **Rwanda, Burundi, and Southern Uganda**. * **Urbanization Corridors:** Tanzania exhibits a more dispersed growth pattern, with distinct clusters likely correlating with major urban centers and transit corridors. * **Population Decline:** Specific pockets of population decline (Green) were identified, potentially indicating migration out of protected areas or rural-to-urban shifts in the Southern Highlands of Tanzania. ### 🛠️ Tech Stack & Methods * **Tools:** Python (Geopandas, Rasterio, Matplotlib), QGIS (for final cartography). * **Data Source:** WorldPop / National Census Projections. * **Methodology:** 1. Raster calculation to determine pixel-wise population change. 2. Reclassification of growth rates into categorical tiers. 3. Cartographic layout with scale bar (EPSG:xxxx) and North orientation. ### 🚀 How to Run the Analysis 1. Clone the repository: ```bash git clone github.com ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Run the Jupyter Notebook: ```bash jupyter notebook notebooks/analysis.ipynb ``` ### 📊 Data Sources The datasets used in this project were downloaded from the following sources: - **ICPAC Geoportal** (African Administrative Boundaries) - geoportal.icpac.net - **WorldPop** (Glob …