EM-DAT Disaster Database Analysis
Also known as: EM-DAT Analysis, Emergency Events Database Analysis, Global Disaster Loss Data Analysis
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.
Key highlights
- Offers the most comprehensive, long-running, and globally consistent open record of significant disasters.
- Standardized hazard taxonomy and impact fields enable comparison across event types, regions, and decades.
- Supports baseline and reference analysis for risk assessment and for validating national loss databases.
- Open access for non-commercial use makes it a common foundation for disaster epidemiology and policy research.
Intuition
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How it works
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When to use it
Use EM-DAT analysis when you need a long, globally consistent record to study the frequency, distribution, human toll, or economic cost of disasters across hazard types, regions, income groups, or time, or to provide baseline loss data for risk and policy work. It is well suited to cross-country comparison of significant disasters, to trend and burden analysis, and as a reference for normalizing and cross-checking national loss databases. EM-DAT is less appropriate for analyzing small or hyper-local events below its thresholds, for precise economic-loss accounting where damage fields are sparse, and for naive trend claims that ignore reporting bias. Its great value is breadth and comparability; its discipline is constant attention to inclusion criteria and data quality.
Strengths & limitations
- Offers the most comprehensive, long-running, and globally consistent open record of significant disasters.
- Standardized hazard taxonomy and impact fields enable comparison across event types, regions, and decades.
- Supports baseline and reference analysis for risk assessment and for validating national loss databases.
- Open access for non-commercial use makes it a common foundation for disaster epidemiology and policy research.
- Entry thresholds exclude small events, so the database is not a complete census of hazard impacts.
- Reporting completeness has improved over time and varies by country, biasing naive long-run trends.
- Economic-damage fields are frequently missing, especially in lower-income countries, complicating loss analysis.
- Attribution and classification conventions can change, introducing inconsistencies across eras and hazard types.
Common pitfalls
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Applications
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Frequently asked
What criteria must an event meet to be in EM-DAT?
An event is entered if it meets at least one of four criteria: ten or more reported deaths, one hundred or more people affected, the declaration of a state of emergency, or a call for international assistance. These thresholds mean EM-DAT records significant disasters rather than every hazard occurrence. The practical implication is that the database is excellent for comparing notable disasters but cannot speak to the many small events that fall below the criteria, and analyses must be framed accordingly.
Why can't EM-DAT trends be taken at face value?
Because the way disasters are recorded has changed over time. Reporting infrastructure, media coverage, and data-sharing have improved markedly over the twentieth and twenty-first centuries, so more events are captured now than before. An apparent increase in recorded disasters can therefore partly reflect better reporting rather than more hazards. Rigorous analysis addresses this by focusing on recent, well-reported periods, on high-impact events that were reliably captured throughout, or by modeling reporting explicitly, and by normalizing losses for inflation, population, and exposure.
How should economic losses from EM-DAT be handled?
With care, because the economic-damage field is often incomplete, particularly for lower-income countries. Missing values should never be treated as zero, since that biases totals and trends downward. Recorded damage should be deflated to constant prices and, for trend analysis, normalized for the growth of population and exposed assets so that increases in losses are not mistaken for increases in hazard. Where loss completeness is essential, EM-DAT is best combined with dedicated national loss databases and reinsurance datasets.
Sources
- 1.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.
- 2.United Nations Office for Disaster Risk Reduction (2015). Sendai Framework for Disaster Risk Reduction 2015-2030. UNDRR, Geneva.
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Cite this page
ScholarGate. (2026, June 23). EM-DAT Disaster Database Analysis. ScholarGate. https://scholargate.app/disaster-studies/em-dat-disaster-database-analysis