Context-sensitive analysis of disaster resilience and equity through geospatial explainable machine learning

Key Points

• Disaster resilience is significantly influenced by geospatial factors, showing a complex interplay with equity indicators.
• Machine learning techniques provide an explainable framework, enabling better understanding of factors affecting resilience.
• Analysis incorporates multiple contexts to assess how equity impacts disaster readiness and recovery outcomes.
• Findings highlight the importance of integrating machine learning into disaster planning, but further empirical validation is encouraged.