Compare methods

	EM-DAT Disaster Database Analysis	Disaster Recovery Curve Analysis
Field	Disaster Studies	Disaster Studies
Family	Process / pipeline	Process / pipeline
Year of origin	1988	2006
Originator	Centre for Research on the Epidemiology of Disasters (CRED), UCLouvain	Scott B. Miles & Stephanie E. Chang
Type	Database-driven descriptive and trend analysis of disaster occurrence and impact	Time-trajectory model of functional recovery and resilience
Seminal source	Delforge, D., Wathelet, V., Below, R., Lanfredi Sofia, C., Tonnelier, M., van Loenhout, J. A. F., & Speybroeck, N. (2025). EM-DAT: the Emergency Events Database. International Journal of Disaster Risk Reduction, 124, 105509.	Miles, S. B., & Chang, S. E. (2006). Modeling Community Recovery from Earthquakes. Earthquake Spectra, 22(2), 439-458.
Aliases	EM-DAT Analysis, Emergency Events Database Analysis, Global Disaster Loss Data Analysis	Recovery Trajectory Analysis, Resilience Curve Analysis, Community Recovery Modeling
Related	3	3
Summary	EM-DAT, the Emergency Events Database maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at UCLouvain, is the most widely used global compilation of disaster occurrence and impact, and its analysis is a standard empirical method in disaster studies. The database records mass disasters from 1900 to the present according to explicit entry criteria, classifies each event by a natural or technological hazard taxonomy, and captures human and economic impacts — deaths, people affected, and damage. Analyzing EM-DAT means querying these records, adjusting economic losses for inflation and exposure, normalizing human impacts by population, and examining trends and patterns across hazard types, regions, and time. Because the data carry known inclusion thresholds and reporting biases, rigorous EM-DAT analysis is as much about understanding what the database can and cannot say as about the statistics themselves.	Disaster recovery curve analysis represents the recovery of a community or system after a disaster as a trajectory of functionality over time and uses that trajectory to quantify resilience. Building on the resilience-triangle concept and formalized for community recovery by Scott Miles and Stephanie Chang in 2006, the approach tracks a performance indicator — population, employment, housing occupancy, service capacity, or composite functionality — from its pre-event baseline, through the abrupt drop caused by the disaster, along the path back toward (or beyond) the baseline. From the curve, analysts read the magnitude of the initial loss, the speed and shape of recovery, the time to return, and the cumulative resilience loss represented by the area between the baseline and the recovery path. Comparing curves across communities or scenarios reveals what drives faster, fuller recovery and complements loss-estimation models that stop at the moment of impact.