Report on Novel Methods for Detecting Empirical Evidence of Dynamics & Feedbacks of Risk Drivers

This report (Deliverable 4.3) provides a comprehensive overview of innovative methods that have been identified to assess multi-hazard risk dynamics. These methods consider the spatial and temporal dynamics in exposure and vulnerability resulting from interactions between multiple hazards and disaster risk reduction measures. The subsequent research supports risk managers in understanding risk dynamics and the effects of DRR measures, enabling decision makers to be better prepared for and recover from multi-hazard risk events.
The report discusses existing databases and vulnerability statistics, as well as novel methods and data sources, highlighting opportunities and challenges in using these methods and data sources. Notable methods include developing a comprehensive vulnerability database for urban areas, utilising novel data streams like Google Trends and newspaper articles for understanding impact durations, using night-time light satellite data for recovery pattern analysis, implementing Machine Learning for multi-risk assessment, and employing Disaster Forensic Analysis to learn from past events and the impact of risk reduction measures.