CW-RIS: Child Wind Risk Intelligence System

CW-RIS is a child-centered wind risk analysis app that overlays real-time ECMWF ERA5 wind forecast data with child population exposure (e.g., from WorldPop), school density, and hospital accessibility to compute and visualize risk zones. This Streamlit app enables faster, data-driven decisions for humanitarian response and preparedness.

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🚀 What This App Does

• ✅ Downloads live 10m wind forecast data (U & V components) from ECMWF ERA5 via CDS API
• ✅ Computes wind speed magnitude from U and V components
• ✅ Clips and reprojects child population raster to match wind data
• ✅ Multiplies wind × population to produce a child wind risk raster
• ✅ Aggregates exposure using zonal statistics (by admin boundaries)
• ✅ Enhances risk by factoring in school density and hospital proximity
• ✅ Displays an interactive map with high-risk areas highlighted

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🛠️ Features

• 📡 Live ERA5 Wind Data via Copernicus CDS API
• 💨 Wind Magnitude Calculation: sqrt(U^2 + V^2)
• 👶 Child Exposure Mapping using population rasters
• 🏫 School Density Scoring using HDX education site data
• 🏥 Hospital Accessibility Scoring using proximity buffers
• 🗺️ Zonal Risk Statistics per admin region
• 🖱️ Interactive Map with hover tooltips (Leafmap/Folium)
• 📈 Final Composite Risk Score for decision support

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📁 Project Structure

cw-ris/
├── app.py                         # 🔵 Main Streamlit entrypoint
├── requirements.txt               # 📦 Python dependencies
├── README.md                      # 📘 Project overview

├── config/
│   └── settings.py                # ⚙️ File paths and constants

├── data/
│   ├── downloader.py              # 🌐 ERA5 wind downloader via CDS API
│   └── validator.py               # ✅ File presence & CDS API checks

├── logic/
│   ├── wind_handler.py            # 💨 Wind speed calculation from U/V
│   ├── exposure.py                # 👶 Population × wind risk computation
│   ├── school_density.py          # 🏫 Compute school site counts per region
│   └── hospital_access.py         # 🏥 Compute hospital proximity score

├── ui/
│   ├── map_display.py             # 🗺️ Map rendering with risk score
│   └── sidebar.py                 # 📚 Sidebar with instructions

├── utils/
│   └── cleanup.py                 # 🧹 Temp file cleanup utility

├── assets/                        # 🗂️ Static geospatial inputs
│   ├── aoi_bangladesh.geojson     # 🟡 AOI polygon
│   ├── adm3.geojson               # 🟢 Admin boundaries for stats
│   ├── bgd_pop_2025_CN_100m.tif   # 👶 Child population raster
│   ├── schools_hdx.geojson        # 🏫 Education facilities (HDX)
│   └── hospitals_hdx.geojson      # 🏥 Health facility locations

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📂 Data Sources

Dataset	Source URL
👶 Child Population Raster	WorldPop 2025
🏥 Health Facilities	HDX - Bangladesh Healthsites
🏫 Education Facilities	HDX - OSM Bangladesh Education
🟢 Admin Boundaries (ADM3)	Provided by hackathon organizers
🟡 AOI for Bangladesh	Provided by hackathon organizers

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📦 Requirements

Install dependencies:

pip install -r requirements.txt

🔑 Setup: CDS API Key

1. Register at https://cds.climate.copernicus.eu
2. Go to your CDS API page
3. Save your credentials in a .cdsapirc file in your home directory:

url: https://cds.climate.copernicus.eu/api
key: your_uid:your_api_key

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▶️ Run the App

streamlit run app.py

Open in your browser at: http://localhost:8501

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🔄 How It Works

1. Fetch latest wind forecast (5-day lag)
2. Compute wind speed using U and V components
3. Reproject child population to match wind raster
4. Compute wind × population exposure per pixel
5. Aggregate by admin regions (zonal stats)
6. Adjust risk by school density and hospital proximity
7. Display interactive map with composite risk scores

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📊 Example Outputs

• Table ranking admin regions by Final Risk Score
• Interactive map:
  • Color-coded composite risk zones
  • Highlighted highest-risk areas
  • Hover to view region risk breakdown

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Notes

• All spatial layers are assumed in EPSG:4326
• Requires valid CDS API credentials and recent Python packages
• Raster alignment done using rasterio.reproject()

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Future Plans

• Add flood and landslide hazard overlays
• Connect to CCRI-DRM dashboards via GeoTIFF
• Enable time-based risk forecasting
• Extend for regional/global scalability

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