Obsidian-Thinker/south-sudan-cattle-movement-early-warning

Google Earth Engine based early warning model linking water scarcity and vegetation stress to predictive cattle movement risk in northwestern South Sudan, for humanitarian and conflict-prevention planning. # South Sudan Cattle Movement Risk Early Warning Model A research-focused demonstration of using satellite remote sensing and Google Earth Engine to predict cattle movement risk in South Sudan, based on environmental conditions that drive pastoral communities to move their herds in search of water and grazing resources. **Target Audience**: Academic researchers, UN humanitarian agencies, conflict prevention practitioners, and development organizations working in pastoral conflict contexts. **Status**: Research/Demonstration Repository (Not Operational) --- ## Table of Contents - Overview - The Causal Logic - Datasets Used - How to Use This Model - Repository Structure - Limitations - Ethics and Responsible Use - Contributing - Citation - License - Contact --- ## Overview In South Sudan, pastoral communities depend on cattle for their livelihoods and cultural identity. During periods of environmental stress—particularly water scarcity and degraded grazing land—herders must move their cattle to survive. When multiple communities converge on limited resources, competition can escalate into violence. This model uses freely available satellite data to: 1. **Identify environmental stress conditions** (water scarcity, vegetation decline) 2. **Predict likely cattle movement patterns** based on resource availability 3. **Highlight potential convergence zones** where resource competition may occur 4. **Generate risk indicators** for humanitarian early warning and conflict prevention **This is a demonstration/research tool**, not a fully operational early warning system. It shows what is possible with Earth observation data and provides a foundation for further development in partnership with local communities and humanitarian organizations. --- ## The Causal Logic The model is built on the following evidence-based causal chain: ``` ┌──────────────────────────────────────────────────────────────────┐ │ Environmental Stressors …