Welcome to the EM-DAT Documentation

• 1: Introduction
• 2: Data Accessibility
• 3: Data Structure and Content Description
• 3.1: General Definitions and Concepts
• 3.2: Core Structure of the Database
• 3.3: EM-DAT Public Table
• 3.4: Disaster Classification System
• 3.5: Classification Glossary
• 3.5.1: Biological Hazards
• 3.5.2: Climatological Hazards
• 3.5.3: Extra-terrestrial Hazards
• 3.5.4: Geophysical Hazards
• 3.5.5: Hydrological Hazards
• 3.5.6: Meteorological Hazards
• 3.5.7: Complex and Technological Hazards
• 3.6: Hazard and Disaster Magnitude Units
• 3.7: Impact Variables
• 3.7.1: Human Impact Variables
• 3.7.2: Economic Impact Variables
• 3.8: Spatial Information and Geocoding
• 3.9: EM-DAT Sources of Information
• 4: Protocols
• 4.1: Entry Criteria
• 4.2: Encoding, Quality Control, and Validation Procedure
• 4.3: Economic Adjustment
• 5: Known Issues and Limitations
• 5.1: General Issues
• 5.2: Specific Biases
• 6: Additional Resources and Tutorials
• 6.1: Tutorials
• 6.1.1: Python Tutorial 1: Basic Operations and Plotting
• 6.1.2: Python Tutorial 2: Making Maps
• 6.2: External Resources
• 6.3: FAQ
• 7: About the EM-DAT Project
• 7.1: EM-DAT and the CRED
• 7.2: EM-DAT Partners
• 7.3: Ways to Contribute
• 7.4: Contact
• 8: Legal Texts
• 8.1: Use Of EM-DAT Database Data and Derived Products
• 8.2: Database License Agreement (Commercial Use)

1 - Introduction

Why Document EM-DAT?

Since its creation in 1988, the Emergency Events Database (EM-DAT) has undergone many changes. Tracing these changes over time was a difficult task that required combing through the archives to reconstruct the history of the database structure, concepts, and definitions. This versioned documentation gives you a rigorous tool which you can use to track its evolution and access up-to-date information.

EM-DAT Documentation

EM-DAT documentation 2023.09 was the first release with a Version Control System. As the version number indicates, it was released in September 2023. The current version is 2024.10. The 2023.09 release introduced breaking changes in the public table format, a backup of the legacy public table is available here. It is available under the same conditions.

Overview of EM-DAT

In 1988, the Centre for Research on the Epidemiology of Disasters (CRED) launched the Emergency Events Database (EM-DAT). EM-DAT was created with the initial support of the World Health Organization (WHO) and the Belgian Government. Since 1999, EM-DAT has been supported by the Bureau for Humanitarian Assistance (BHA, previously the Office of US Foreign Disaster Assistance, or OFDA) within the United States Agency for International Development (USAID).

The initial objective of the database is to serve the purposes of humanitarian action at the national and international levels. Today, EM-DAT is also used to rationalize disaster preparedness and decision-making while providing an objective basis for vulnerability and risk assessment.

The EM-DAT database records mass disasters as well as their health and economic impacts at a country level (see Data Structure). The database contains core data on the occurrence and effects of 26,000 disasters worldwide from 1900 to the present. The database is compiled from various sources of information, including UN agencies, non-governmental organizations, insurance companies, research institutes, and press agencies (see Sources).

How to Cite?

To acknowledge EM-DAT as a data source in publications (e.g., documents, PowerPoint presentations, posters, or image charts), we recommend the following citation syntax:

EM-DAT, CRED / UCLouvain, 2024, Brussels, Belgium – www.emdat.be

Since the Emergency Events Database (EM-DAT) is regularly updated, we advise including the access date to ensure reference accuracy. If you use EM-DAT for a scientific publication, we would appreciate citations to the following database descriptor article (preprint):

Delforge, D. et al.: EM-DAT: The Emergency Events Database, Preprint, https://doi.org/10.21203/rs.3.rs-3807553/v1, 2023.

Highlighted Publications

Check our latest EM-DAT-related research paper:

Donatti, C. I., Nicholas, K., Fedele, G., Delforge, D., Speybroeck, N., Moraga, P., Blatter, J., Below, R., and Zvoleff, A.: Global hotspots of climate-related disasters, International Journal of Disaster Risk Reduction, 108, 104488, https://doi.org/10.1016/j.ijdrr.2024.104488, 2024.

All CRED publications relative to EM-DAT are accessible from our online repository.

For a comparison of available disaster loss databases, we recommend reading:

Mazhin, S. A., Farrokhi, M., Noroozi, M., Roudini, J., Hosseini, S. A., Motlagh, M. E., Kolivand, P., and Khankeh, H.: Worldwide disaster loss and damage databases: A systematic review, J Educ Health Promot, 10, 329, https://doi.org/10.4103/jehp.jehp_1525_20, 2021.

For a disaster loss dataset based on EM-DAT with improved geocoding, have a look at:

Rosvold, E. L. and Buhaug, H.: GDIS, a global dataset of geocoded disaster locations, Sci Data, 8, 61, https://doi.org/10.1038/s41597-021-00846-6, 2021.

For a comprehensive global overview of natural hazards and disasters, we recommend reading the following online publication:

Ritchie, H., Rosado, P., and Roser M.(2022) - “Natural Disasters.” Published online at OurWorldInData.org. Retrieved from: ‘https://ourworldindata.org/natural-disasters' [Online Resource]

2 - Data Accessibility

How to Download the EM-DAT Public Data?

The public EM-DAT records are accessible from our online data portal public.emdat.be. Access for non-commercial use is free after registration and subject to the Terms of Use. Access for Commercial Use is possible on the basis of an annual paid subscription (see Commercial License).

After registration and login, the data can be downloaded using the EM-DAT “Access Data” Tab or Toolbox (see the figure below) as a flat table in Microsoft Excel format (.xlsx). The EM-DAT database is described in the Data Structure Description section. In particular, the Column Description section contains the description of each column of the public table found in the Excel file.

Humanitarian Data Exchange (HDX)

Country profiles

In addition, annual data summaries per country (country profiles) are available for download on the Humanitarian Data eXchange platform (see More on HDX here). These consist of aggregated figures in the following format :

3 - Data Structure and Content Description

3.1 - General Definitions and Concepts

EM-DAT was designed in 1988 based on an anthropocentric vision of disasters and emergencies¹. It considers disasters to be events involving an unexpected and overwhelming harmful impact on human beings. Formally, EM-DAT’s definition of a disaster is:

EM-DAT inventories only disasters that fit its Inclusion Criteria. EM-DAT records both disasters triggered by natural hazards and technological disasters. The latter are unintentional accidents, and not situations of conflict, violence, or terrorism. For more details, we refer to the Disaster Classification System.

3.2 - Core Structure of the Database

EM-DAT data model records disasters and maps their impacts at the country level. Accordingly, the EM-DAT database references disasters with a unique identifier (see Dis No. in Column Description). A disaster may affect more than one country. In EM-DAT, the country-specific information about the disaster is recorded as an “Impact.” The Impact is the level at which EM-DAT reports entries in the EM-DAT Public Table. To document each Impact, we cross-validate and select information from the sources that are available (see EM-DAT sources and Protocols).

The figure below provides a visual representation of the foundational structure of the EM-DAT database’s relational model. On the left is the core structure, and on the right, a hypothetical case serves as an illustrative example. To enhance clarity, the relations have been streamlined, omitting certain tables such as dictionaries, focusing solely on the main connections and the underlying logic of the database.

Cardinality, or the nature of the relationship between entities, is denoted by numbers or the letter “N” on each link. For instance, “1, N” signifies a one-to-many relationship. A clear example of this is the link between the “Disaster” and “Impact” tables. This implies that a single disaster entry might correspond to multiple impacts across various countries. Consider a flood affecting multiple nations, such as Belgium and Germany; the connection between these entries is facilitated by the shared Dis No. attribute.

The primary classification of a disaster, such as its main type, is housed in the “Disaster” table. Consequently, all impacts linked by the same Dis No. will have a consistent primary disaster type. However, EM-DAT introduces a nuance with associated disasters linked to the “Impact” table. Unlike the main type, these associated disasters can vary by country. They represent secondary disasters that either result from or occur simultaneously with the main event. An example would be a landslide triggered by a primary flood event, which is considered to be an associated disaster. It’s important to note that associated disasters are optional, leading to their zero-to-many relationship with the “Impact” table.

Each source can provide data for any given variable within the Impact category. During the validation process (as detailed in Encoding, Quality Control, and Validation Procedure), one value is chosen for each variable, which is independent of other variables. Some figures presented in the EM-DAT Public Table are aggregates of various variables and might originate from multiple distinct sources. For example, figures for deaths and missing persons might be reported at different times after an event. After evaluating and selecting from the available data, the Total Deaths column might consolidate information from various sources.

3.3 - EM-DAT Public Table

Overview

The EM-DAT Public Table is a flat representation of EM-DAT data in a single downloadable table. Most impact variables are part of the public table (see Impact Variables). The public table provides a flat view of the general structure in which each record (row) corresponds to a disaster impacting a country.

Column Description

3.4 - Disaster Classification System

A Brief History of the EM-DAT Classification System

EM-DAT’s classification system originally started with a simple 20-type list¹. However, in 1992, CRED and other international stakeholders proposed a hierarchical classification system² that distinguishes natural and man-made disasters (described as technological disasters in EM-DAT). A further distinction was based on the timing of disasters: slow vs. rapid onset disasters.

In the 2000s, CRED collaborated with Munich Re and other stakeholders on a common classification system³. Since then, EM-DAT’s main classification system has followed the logic of referring to the hazard or event triggering the disaster. Consequently, some disasters were reclassified, and some types were removed from the primary classification system. This was the case for famines reclassified as drought for the most part⁴. However, “famine” offers more information than “drought.” Therefore, in order not to lose this added value, EM-DAT has adopted the secondary classification system of Associated Disasters. This describes disasters that coincide with or result from the primary type.

In 2014, the Integrated Research on Disaster Risk (IRDR) working group, which included CRED, established a new reference called the Peril Classification and Hazard Glossary. This document is currently the primary reference for classifying natural hazards in EM-DAT, which divides them into six main groups: Geophysical, Hydrological, Meteorological, Climatological, Biological, and Extra-terrestrial. EM-DAT also includes more detailed subtypes.

Main Classification Tree

The main classification tree has four levels of depth, so disasters are divided into groups, subgroups, types, and subtypes, as presented in the EM-DAT Public Table columns. The two EM-DAT disaster groups are ‘Natural’ and ‘Technological’. The table below shows the complete tree for the ‘Natural’ and ‘Technological’ groups, with the occurrence for each subtype. Their corresponding definitions are available in the Classification Glossary.

Associated Disasters

In addition to the main classification system, EM-DAT makes it possible to refer to “associated disasters” to describe disaster events in more details (see Associated Dis in EM-DAT Public Table). They represent subsequent or co-occurring hazards that may have contributed to the disaster impact. These associated disasters may not fit into the main classification system and do not have a hierarchical structure. This additional tagging system allows for a better description of disaster events, particularly multi-hazard ones. The figure below shows the main associations found in the database.

About 14% of disaster entries in EM-DAT have an associated disaster type, and only 3% mention two associated types. The most common associations are floods with landslides (24% of associations), storms with floods (21%), and storms with landslides (8%). Earthquakes are sometimes associated with landslides (4%) and tsunamis (4%) when their damage is deemed negligible compared to ground movement damage.

3.5 - Classification Glossary

EM-DAT’s definitions related to the group of natural hazards mainly refer to the IRDR Peril Classification and Hazard Glossary (see A Brief History of the EM-DAT Classification System). These are reported in the following sections by disaster subgroups.

EM-DAT definitions related to the groups of complex disasters and technological hazards are listed separately in the Complex and Technological Hazards section. These are legacy definitions from the EM-DAT project and do not refer to a particular glossary serving as an international standard.

3.5.1 - Biological Hazards

3.5.2 - Climatological Hazards

3.5.3 - Extra-terrestrial Hazards

3.5.4 - Geophysical Hazards

3.5.5 - Hydrological Hazards

3.5.6 - Meteorological Hazards

3.5.7 - Complex and Technological Hazards

3.6 - Hazard and Disaster Magnitude Units

Some disaster types may have a reported magnitude in the Magnitude and Magnitude Scale columns of the EM-DAT Public Table. EM-DAT disaster magnitude scales vary depending on the disaster type. The table below specifies magnitude property and units for related disaster types.

3.7 - Impact Variables

3.7.1 - Human Impact Variables

Five variables describe the human impact of disasters in the EM-DAT Public Table:

The reported total number of deaths (column Total Deaths) includes confirmed fatalities directly imputed to the disaster plus missing people whose whereabouts since the disaster are unknown and so they are presumed dead based on official figures.

Aside from fatalities, the number of injured people (column No. Injured) is entered when the term “injured” is written in the source. Any related word like “hospitalized” is considered as injured. If no precise number is given, such as “hundreds of injured,” 200 injured will be entered (although this figure is probably an underestimate).

The number of affected people (column No. Affected) is often mentioned and is widely used by different actors to convey the extent, impact, or severity of a disaster in non-spatial terms. If only the number of families affected or houses damaged are reported, the figure is multiplied by the average family size for the affected area (×5 for developing countries, ×3 for industrialized countries, according to the UNDP country classification).

Similarly, the indicator No. Homeless is mentioned whenever it is found in reports. If only the number of families that are homeless or houses that are destroyed are reported, the figure is multiplied by the average family size for the affected area (x5 for developing countries, x3 for industrialized countries, according to the UNDP country list).

Finally, the indicator Total Affected is the total of injured, affected, and homeless people. For each disaster and its corresponding sources, the population referred to in these statistics and the apportionment between injured, affected, homeless, and the total is carefully checked by CRED staff members.

Usually, at least the field Total Deaths or Total Affected are found in EM-DAT records as these numbers are involved as entry criteria. However, records often contain incomplete impact statistics (see Accounting Biases).

3.7.2 - Economic Impact Variables

Six variables describe the economic impact of disasters in the EM-DAT Public Table:

These six statistics are the three same statistics (Reconstruction Costs ('000 US$), Insured Damage ('000 US$), and Total Damage ('000 US$)) repeated to also provide an amount corrected for inflation, i.e., “Adjusted” (See Economic Adjustment). Damage and costs are converted and expressed in thousands of US dollars (‘000 US$).

Reconstruction costs are different from total damages as they must consider the current construction or purchase costs of goods, as well as the additional cost of prevention and mitigation measures designed to reduce damage from future disasters. Hence, when reconstruction costs are specified, they are usually greater than the total damage.

Insured damage is usually reported by reinsurance companies that publish figures about disaster losses, e.g., MunichRe, SwissRe, or AON. When insured damage is reported, the total damage is generally reported from the same source for consistency.

3.8 - Spatial Information and Geocoding

Country Codes (ISO-3)

In the EM-DAT Public Table, the ISO column indicates a 3-letter (alpha-3) code representing a specific country, e.g., “BEL” for Belgium. This code is presented according to the international standard ISO-3166 determined by the International Organization for Standardization (ISO). Due to historical changes in the countries’ denomination or boundaries, you may find country codes that are not found in the current ISO 3166 alpha-3 country codes. These extensions are listed in the table below.

Country codes are particularly useful to link the EM-DAT tabular data to a spatial layer using a Geographic Information System (GIS), spatial database, or geoprocessing programming library.

United Nations M49 Standard Country or Area Codes

The UN M49, also known as the Standard Country or Area Codes for Statistical Use (Series M, No. 49), is a set of area codes formulated by the United Nations for data analytics. This standard is curated and upheld by the United Nations Statistics Division. The UN M49 alpha-3 codes largely overlaps with the ISO-3166 alpha-3 norm.

From September 2023 onward, the EM-DAT Public Table refers to the Country, Region, and Subregion names as found in the UN M49 standard. Codes used for the EM-DAT extensions to the ISO alpha-3 codes and UNM49 are listed in the table below.

GAUL Index and Admin Levels

Since 2014, EM-DAT has relied on the Global Administrative Unit Layers (GAUL) implemented by the Food and Agriculture Organization (FAO). EM-DAT provides loss statistics at the country level, which corresponds to GAUL Admin-0 level. In addition, the EM-DAT Public Table mentions in the Admin Units column which region is affected by the disaster up to the Admin-2 level (e.g., districts). The mapping of EM-DAT disaster events at this higher level of geographical precision has only been completed for data since 2000. Nevertheless, the Admin-0 human and economic impact variables at the country level are not disaggregated between regions at the Admin-1 or Admin-2 level. Hence, only the occurrence is available at a more precise administrative level, and the impact variables remain representative of the country level.

3.9 - EM-DAT Sources of Information

The EM-DAT database is compiled from various sources, including United Nations, governmental and non-governmental agencies, insurance companies, research institutes, and the press. As of September 2023, the most common sources are included in the table below. For futher inquiries on the data collection and selection process, see EM-DAT Protocols.

4 - Protocols

4.1 - Entry Criteria

The EM-DAT definition of a disaster considers unintended hazards with a substantial impact unforeseen by a community (see General Definitions and Concepts). For management and operational purposes, EM-DAT has a set of entry criteria that specify what substantial impact means. EM-DAT disaster records related to natural and technological hazards meet at least one of the following inclusion criteria:

There are, however, secondary criteria, especially for past events where quantitative data were not available (e.g., “the worst disaster in a country or region” or “an event that resulted in considerable damage”).

4.2 - Encoding, Quality Control, and Validation Procedure

Source identification and collection can be facilitated and partially automated thanks to online services offered by specific sources (e.g., email alert systems, news feeds, or APIs). However, data collection and encoding are always supervised manually by the database manager. The database manager controls the sources that are selected, the classification of the event, its spatiotemporal delimitation, and the identification of the impact figures. Data encoding and validation in EM-DAT is a three-step process:

In addition, Automated Procedures and Constraints prevent or check for abnormal values or data formats in the database.

Daily Encoding

The database manager checks daily information using the preferred source list (see EM-DAT sources). Whenever a new disaster is identified based on a source, it is added to the database. In the first stage, the event is not made public. It becomes so when an entry criterion is met and confirmed by at least two sources. The figures remain subject to changes. Any publication or modification made on a public disaster entry will be visible to the user after the weekly update routine. This routine is usually executed on mondays but may be triggered by the manager if deemed necessary, e.g. in the case of faulty figures or typos.

The published impact variables could be selected from one or more sources. An event can therefore be validated from several sources of information. For example, the human impact can come from an OCHA report and the economic data from a reinsurance report, depending on specific expertise. If the figures differ between the sources, the database manager decides which ones to attribute to the disaster. The choice depends on several elements: the figure itself and the area and period to which it refers, the sources’ chronology, and its degree of reliability. Because this task is complex and case-dependent, there is no pre-determined rule for selecting figures, and the database manager makes the final choice. Some examples of general, however, not systematic, decision rules are illustrated in the box below.

The rules here are only informal and the database manager may disregard them. For example, suppose a report mentions 200 deaths and a news article says, “regional authorities estimate the death toll now stands at 243 dead and missing”. Although this is a newspaper article, the precision of the statement suggests that this figure is more reliable than the one in the official report.

Quality Control and Annual Validation

Quality control and annual validation are systematic checks of all the entries starting in a specific year. It typically takes place at the beginning of the following year. During this validation, all the disasters that took place in the previous year are reviewed to consolidate the data, identify possible additional sources, and modify the published figures accordingly. In addition, the georeferencing, i.e., the more precise attribution of the disaster to GAUL level 1 or 2 zones, is also finalized during the annual validation period (see GAUL Index and Admin Levels).

Thematic Reviews

The CRED periodically conducts thematic reviews of disasters to mitigate the database’s weaknesses (see Known Issues). This task involves systematically checking entries for a type of disaster over a given period or region. The revision can be a data analysis for further quality control, a systematic review of the scientific literature, or a comparison with other existing databases.

From September 2023 onward, these substantial database content updates are planned to be notified on the EM-DAT website and in the documentation release notes for tracking purposes (see Introduction). The Entry Date and Last Update column have also been introduced in the EM-DAT Public Table.

Automated Procedures and Constraints

In 2023, the constraints of the EM-DAT database have been strengthened. These constraints define the domain of values that can be encoded and, if correctly set, can prevent encoding errors in the value or its format. In addition, there are automated procedure checks for anomalies that are likely to be an error, i.e., those without enough certainty to be a constraint but with sufficient likelihood to be notified to the database manager for verification.

Currently, constraints and automated routines check for the consistency of date and time fields, latitude and longitude values, and hazard magnitude values. These procedures are implemented incrementally each time an error that could be prevented is discovered. Hence, by detecting and reporting issues, users may contribute to developing these routines and improving EM-DAT data quality.

Issue Reporting

The CRED encourages any external initiative that can help us improve our data quality. Any problem with the data content (such as errors, or missing data) can be reported to CRED by email using the contact address (see Send Us an Email). For missing entries, you should be aware that the CRED only publishes disasters for which reliable figures are available and when these are corroborated by at least two sources (see Daily Encoding). The CRED reserves the right to modify the data according to subsequent notifications. For issues concerning specific disaster events, the problem should be explained and notified with a list including the related Dis No. values (see Column Description).

For less specific issues, users who have analyzed the quality of EM-DAT are encouraged to share their reports or scientific studies with the EM-DAT team to improve the database (see Contributing).

4.3 - Economic Adjustment

All economic damage in EM-DAT is expressed in thousands of US$ (see Column Description and Economic Impact Variables). For each disaster, the registered figure corresponds to value of the damage at the moment of the event. If a source reports damage in another currency, it is first converted to US$ before it is entered in EM-DAT using a converter (see this example), at the exchange rate when the damage occurred.

The adjusted economic losses in EM-DAT provide a monetary value in US$ that has been adjusted for inflation (see Column Description). The adjustment is linearly proportional to the OECD Consumer Price Index (CPI) provided by the Organization for Economic Cooperation and Development (OECD). The CPI reflects the change in prices of a basket of goods and services that are typically purchased by specific groups of households. EM-DAT relies on the total CPI, i.e., including both food and energy products in the basket, defined for the USA.

In practice, OECD provides the US CPI with 2015 as the base year (\(CPI_{2015}=100\), see https://data.oecd.org/chart/6RMa ). In EM-DAT, the CPI is rescaled to use the last year as a reference, e.g., \(CPI_{2021}=100\). The rescaling is performed when the OECD CPI value becomes available. The economic losses given for the current year are therefore not adjusted and not reported in the column Total Damages, Adjusted ('000 US$).

5 - Known Issues and Limitations

EM-DAT is the only comprehensive, free-access disaster loss database with effective global coverage¹. However, it has limitations due to the limited number of sources and limitations related to how effectively disasters are reported worldwide. This can lead to biases in the data over which CRED may have limited control, and that could be overlooked in the literature². Nevertheless, EM-DAT remains a key resource for understanding disaster events and impacts. No current impact database is completely accurate. The United Nations emphasizes the importance of global improvements in documenting disasters in global agendas such as the SENDAI Framework for Disaster Risk Reduction (SFDRR).

Understanding the limitation of a dataset such as EM-DAT is of paramount importance for those who wish to adequately use the data and mitigate its weaknesses for the following purposes: disaster risk management, emergency planning, scientific research, and raising public awareness.

5.1 - General Issues

Three types of data quality issues can be considered:

A cross-comparison of EM-DAT with a local database and/or a disaster-specific database can help identifiy Issue 1 (e.g., Koç & Thieken, 2018¹; Lin et al., 2021²). For an account of missing values for existing events, we refer to Jones et al. 2021³ and the section on Accounting Biases. Issue 3 is partially related to the data collection sources, protocols, or reporting systems generally used by different databases.

Data quality issues within EM-DAT are related to the data collection protocols from dedicated sources. EM-DAT’s completeness reflects the coverage of its sources. Since source reporting has improved over the years, EM-DAT data coverage has improved significantly over the last 30 to 40 years. Nevertheless, gaps and quality issues remain. EM-DAT protocols are meant to guide the way information is monitored and collected from sources. However, no universally applied protocol ensures that different sources report disaster impact and losses using the same guidelines to define, for instance:

• the beginning and end of disaster events.
• the geographical footprint of a disaster.
• impact variables such as deaths (in particular, when computed based on excess mortality), affected people, or economic costs.
• the disaster type selected by the sources.

Some references illustrate the issues and challenges related to collecting and maintaining a disaster database, e.g., Guha-Sapir & Misson 1992⁴, Kron et al. 2012⁵, and Wirtz et al. 2014⁶.

To some extent, EM-DAT owes its popularity to its simplicity. It reports disaster events as rows in an Excel table. However, this simplicity comes at the cost of conceptual limitations in dealing with complex and compound events and situations. In such cases, as exemplified in the box below, EM-DAT will probably report the disaster in the same way as the source which presented it. The EM-DAT database manager can only choose to select some numbers over some others (see Daily Encoding). However, no model is involved in correcting differences in reporting protocols because this task goes beyond the information monitoring conducted at the CRED by the EM-DAT team.

Such biases that result from differences in the impact reporting systems were generally referred to by Gall et al. 2009⁷ as systemic biases. Some studies point to systemic biases by highlighting that EM-DAT does not correlate well with other databases (e.g., Moriyama et al., 2018⁸; Panwar & Sen, 2020⁹). In their article, Gall et al. 2009⁷ cover four other types of biases: time, hazard-related, spatial, and accounting biases. These are illustrated in the next sections.

5.2 - Specific Biases

Time Bias

Time biases result from unequal reporting quality and coverage over time¹. The figure below shows the occurrence of disasters in EM-DAT. The figure shows a significant increase that starts in the 1960s. This increase coincides with the creation of OFDA. In 1973, OFDA started compiling disaster data, and the CRED was created². In 1988, the CRED took over the disaster database and created EM-DAT. In the meantime, communication technologies have improved, with the first personal computers and satellites appearing in the 1970s and the advent of the World Wide Web in the 1990s (see also History of EM-DAT)

Technologies and initiatives can be considered responsible for the dominant trend observed. Therefore, it is challenging to infer insight into the actual drivers of disasters such as climate change, population growth, or disaster risk management³. Accordingly, excluding pre-2000 data from trend analyses based on EM-DAT is strongly recommended. From September 2023 onward, the CRED refers to pre-2000 data as Historic data in the EM-DAT Public Table.

Hazard-related Biases

Hazard-related biases result from unequal reporting quality and coverage for different hazard types¹. For example, in EM-DAT, data related to biological hazards (e.g., epidemics) and extreme temperature hazards (e.g., heat waves) are less covered and the cover of lower quality. Some hazard-related biases are illustrated in the Accounting Biases and Geographic Biases sections.

Threshold Biases

Threshold biases result from unequal reporting quality and coverage for disasters of different magnitudes¹. High-impact disasters attract more attention, resulting in better media coverage and reporting. This could lead to threshold biases in EM-DAT. The EM-DAT entry criteria introduce a kind of threshold bias, as shown in the figure below regarding disaster mortality, while some studies have shown locally that small disasters may have a high cumulative impact, e.g.⁴. Regarding disasters that fit EM-DAT’s entry criteria, it is fair to assume that disasters close to the entry criteria are more likely to be missing than high-impact disasters. However, as shown in the figure below, the cumulative mortality associated with low-mortality events exceeds the cumulative impact of higher-mortality events.

Accounting Biases

Accounting biases result from unequal reporting quality and coverage for different impact variables¹. For instance, in EM-DAT, the economic losses are, on average, less frequently reported than the human impact variables, which may also depend on the hazard type (see the figure below). Furthermore, insured damages are naturally more reported than uninsured damages, which creates a geographic bias where there is a lack of insurance coverage, as in Africa. For droughts, EM-DAT fails to capture the associated mortality because it is overlooked as an indirect impact, as evidenced by the UNDRR 2021 Global Assessment Report on Droughts.

Besides, it cannot be assumed that because an impact is reported in EM-DAT, there is no accounting bias. In general, direct impacts are often reported by EM-DAT sources, while indirect impact estimates are less available. For instance, indirect deaths for a flood event correspond to the number of fatalities occurring during the event, while indirect deaths result from disease outbreaks due to deteriorated sanitary conditions. Yet, indirect mortality is sometimes more important than direct mortality, for examples.⁵

Geographic Biases

Geographic biases result from unequal reporting quality and coverage across space¹. In general, EM-DAT has a relatively worse coverage for Sub-Saharan Africa regarding the occurrence and the accounting of impact variables⁶. Any disaster type may be subject to geographic biases in EM-DAT as there may be discrepancies between reporting systems from one country to another (see General Issues).

This issue is particularly pronounced regarding heat waves⁷, as shown in the figure below. Heat waves are often overlooked in Sub-Saharan Africa⁸. About 52% of heatwave events in EM-DAT occurred in nine countries: Japan, India, Pakistan, the USA, followed by Western European countries (France, Belgium, United Kingdom, Spain, and Germany).

References

6 - Additional Resources and Tutorials

6.1 - Tutorials

6.1.1 - Python Tutorial 1: Basic Operations and Plotting

This tutorial shows basic examples on how to load, handle, and plot the EM-DAT data using the pandas Python data analysis package and the matplotlib charting library.

Note: The Jupyter Notebook version of this tutorial is available on the EM-DAT Python Tutorials GitHub Repository.

Import Modules

Let us import the necessary modules and print their versions. For this tutorial, we used pandas v.2.1.1 and matplotlib v.3.8.3. If your package versions are different, you may have to adapt this tutorial by checking the corresponding package documentation.

import pandas as pd #data analysis package
import matplotlib as mpl
import matplotlib.pyplot as plt #plotting library
for i in [pd, mpl]:
    print(i.__name__, i.__version__)

pandas 2.1.1
matplotlib 3.8.3

Load EM-DAT

To load EM-DAT:

• Download the EM-DAT data at https://public.emdat.be/ (registration is required, see the EM-DAT Documentation page on Data Accessibility);
• Use the pd.read_excel method to load and parse the data into a pd.DataFrame object;
• Check if the data has been successfully parsed with the pd.DataFrame.info method.

Notes:

1. You may need to install the openpyxl package or another engine to make it possible to read the data.
2. Another option is to export the .xlsx file into a .csv, and use the pd.read_csv method;
3. If not in the same folder as the Python code, replace the filename with the relative path or the full path, e.g., E:/MyDATa/public_emdat_2024-01-08.xlsx

#!pip install openpyxl
df = pd.read_excel('public_emdat_2024-01-08.xlsx') # <-- modify file name or path
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15560 entries, 0 to 15559
Data columns (total 46 columns):
 #   Column                                     Non-Null Count  Dtype  
---  ------                                     --------------  -----  
 0   DisNo.                                     15560 non-null  object 
 1   Historic                                   15560 non-null  object 
 2   Classification Key                         15560 non-null  object 
 3   Disaster Group                             15560 non-null  object 
 4   Disaster Subgroup                          15560 non-null  object 
 5   Disaster Type                              15560 non-null  object 
 6   Disaster Subtype                           15560 non-null  object 
 7   External IDs                               2371 non-null   object 
 8   Event Name                                 4904 non-null   object 
 9   ISO                                        15560 non-null  object 
 10  Country                                    15560 non-null  object 
 11  Subregion                                  15560 non-null  object 
 12  Region                                     15560 non-null  object 
 13  Location                                   14932 non-null  object 
 14  Origin                                     3864 non-null   object 
 15  Associated Types                           3192 non-null   object 
 16  OFDA Response                              15560 non-null  object 
 17  Appeal                                     15560 non-null  object 
 18  Declaration                                15560 non-null  object 
 19  AID Contribution ('000 US$)                490 non-null    float64
 20  Magnitude                                  3356 non-null   float64
 21  Magnitude Scale                            9723 non-null   object 
 22  Latitude                                   1809 non-null   float64
 23  Longitude                                  1809 non-null   float64
 24  River Basin                                1197 non-null   object 
 25  Start Year                                 15560 non-null  int64  
 26  Start Month                                15491 non-null  float64
 27  Start Day                                  14068 non-null  float64
 28  End Year                                   15560 non-null  int64  
 29  End Month                                  15401 non-null  float64
 30  End Day                                    14132 non-null  float64
 31  Total Deaths                               12485 non-null  float64
 32  No. Injured                                5694 non-null   float64
 33  No. Affected                               7046 non-null   float64
 34  No. Homeless                               1312 non-null   float64
 35  Total Affected                             11508 non-null  float64
 36  Reconstruction Costs ('000 US$)            33 non-null     float64
 37  Reconstruction Costs, Adjusted ('000 US$)  29 non-null     float64
 38  Insured Damage ('000 US$)                  691 non-null    float64
 39  Insured Damage, Adjusted ('000 US$)        683 non-null    float64
 40  Total Damage ('000 US$)                    3070 non-null   float64
 41  Total Damage, Adjusted ('000 US$)          3020 non-null   float64
 42  CPI                                        15056 non-null  float64
 43  Admin Units                                8336 non-null   object 
 44  Entry Date                                 15560 non-null  object 
 45  Last Update                                15560 non-null  object 
dtypes: float64(20), int64(2), object(24)
memory usage: 5.5+ MB

Example 1: Japan Earthquake Data

Filtering

Let us focus on the EM-DAT earthquakes in Japan from the years 2000 to 2003 and create a suitable filter utilizing the EM-DAT columns Disaster Type, ISO and Start Year.

For simplicity, let’s retain only the columns Start Year, Magnitude, and Total Deaths and display the first five entries using the pd.DataFrame.head method.

Note: For further details about the columns, we refer to the EM-DAT Documentation page EM-DAT Public Table.

eq_jpn = df[
    (df['Disaster Type'] == 'Earthquake') &
    (df['ISO'] == 'JPN') &
    (df['Start Year'] < 2024)
][['Start Year', 'Magnitude', 'Total Deaths', 'Total Affected']]
eq_jpn.head(5)

Grouping

Let us group the data to calculate the number of earthquake events by year and plot the results.

• Use the groupby method to group based on one or more columns in a DataFrame, e.g., Start Year;
• Use the size method as an aggregation method (or count).
• Plot the results using the pd.DataFrame.plot method.

Note: The count method provides the total number of non-missing values, while size gives the total number of elements (including missing values). Since the field Start Year is always defined, both methods should return the same results.

eq_jpn.groupby(['Start Year']).size().plot(kind='bar', ylabel='Count')

<Axes: xlabel='Start Year', ylabel='Count'>

Customize Chart

The pandas library relies on the matplotlib package to draw charts. To have more flexibility on the rendered chart, let us create the figure using the imported plt submodule.

# Group earthquake data by 'Start Year' and count occurrences
eq_cnt = eq_jpn.groupby(['Start Year']).size()

# Initialize plot with specified figure size
fig, ax = plt.subplots(figsize=(7, 2))

# Plot number of earthquakes per year
ax.bar(eq_cnt.index, eq_cnt)

# Set axis labels and title
ax.set_xlabel('Year')
ax.set_ylabel('N° of Earthquakes')
ax.set_yticks([0, 1, 2, 3])  # Define y-axis tick marks
ax.set_title('EM-DAT Earthquakes in Japan (2000-2023)')

Text(0.5, 1.0, 'EM-DAT Earthquake in Japan (2000-2023)')

Example 2: Comparing Regions

Let us compare earthquake death toll by continents. As before, we filter the original dataframe df according to our specific needs, including the Region column.

eq_all = df[
    (df['Disaster Type'] == 'Earthquake') &
    (df['Start Year'] < 2024)
][['Start Year', 'Magnitude', 'Region', 'Total Deaths', 'Total Affected']]
eq_all.head(5)

In this case,

• Use the groupby method to group based on the Region column;
• Use the sum method for the Total Deaths field as aggregation method;
• Plot the results easilly using the pd.DataFrame.plot method.

eq_sum = eq_all.groupby(['Region'])['Total Deaths'].sum()
eq_sum

Region
Africa        5863.0
Americas    229069.0
Asia        548766.0
Europe         783.0
Oceania        641.0
Name: Total Deaths, dtype: float64

Finally, let us make an horizontal bar chart of it using matplotlib. In particular,

• use the ax.ticklabel_format method to set the x axis label as scientific (in thousands of deaths);
• use the ax.invert_yaxis to display the regions in alphabetical order from top to bottom.

fig, ax = plt.subplots(figsize=(4,3))
ax.barh(eq_sum.index, eq_sum)
ax.set_xlabel('Total Earthquake Deaths')
ax.ticklabel_format(style='sci',scilimits=(3,3),axis='x')
ax.invert_yaxis()
ax.set_title('EM-DAT Earthquake Deaths by Regions')

Text(0.5, 1.0, 'EM-DAT Earthquake Deaths by Regions')

Example 3: Multiple Grouping

At last, let us report the earthquake time series by continents. To avoid the creation of a ['Region', 'Start Year'] multiindex for future processing, we set the argument as_index to False. As such, Region and Start Year remain columns.

eq_reg_ts = eq_all.groupby(
    ['Region', 'Start Year'], as_index=False
)['Total Deaths'].sum()
eq_reg_ts

97 rows × 3 columns

Next, we apply the pivot method to restructure the table in a way it could be plot easilly.

eq_pivot_ts = eq_reg_ts.pivot(
    index='Start Year', columns='Region', values='Total Deaths'
)
eq_pivot_ts.head()

ax = eq_pivot_ts.plot(kind='bar', width=1, figsize=(6,3))
ax.set_ylabel('Total Deaths')
ax.set_title('EM-DAT Earthquake Deaths by Regions')

Text(0.5, 1.0, 'EM-DAT Earthquake Deaths by Regions')

In order to be able to visualize the data in more details, let us make a subplot instead by setting the subplot argument to True within the plot method.

ax = eq_pivot_ts.plot(kind='bar', subplots=True, legend=False, figsize=(6,6))
plt.tight_layout() # <-- adjust plot layout

We have just covered the most common manipulations applied to a pandas DataFrame containing the EM-DAT data. To delve further into your analyses, we encourage you to continue your learning of pandas and matplotlib with the many resources available online, starting with the official documentation.

If you are interested in learning the basics of making maps based on EM-DAT data, you can also follow the second EM-DAT Python Tutorial.

6.1.2 - Python Tutorial 2: Making Maps

If you have followed the first EM-DAT Python Tutorial 1 or are already familiar with pandas and matplotlib, this second tutorial will show you basic examples on how to make maps with the EM-DAT data using the geopandas Python package.

Note: The Jupyter Notebook version of this tutorial is available on the EM-DAT Python Tutorials GitHub Repository.

Import Modules

Let us import the necessary modules and print their versions. For this tutorial, we used pandas v.2.1.1, geopandas v.0.14.3, and matplotlib v.3.8.3. If your package versions are different, you may have to adapt this tutorial by checking the corresponding package documentation.

import pandas as pd #data analysis package
import geopandas as gpd
import matplotlib as mpl
import matplotlib.pyplot as plt #plotting library
for i in [pd, gpd, mpl]:
    print(i.__name__, i.__version__)

pandas 2.2.1
geopandas 0.14.3
matplotlib 3.8.3

Creating a World Map

To create a world map, we need the EM-DAT data and a shapefile containing the country geometries.

EM-DAT: We download and load the EM-DAT data using pandas.

Country Shapefile: We download a country shapefile from Natural Earth Data. For a world map, we download the low resolution 1:110m Adimin 0 - Countries (last accessed: March 10, 2024) and unzip it.

Load and Filter EM-DAT

Let us load EM-DAT and filter it to make a global map of Earthquake disasters between 2000 and 2023. We calculate the number of unique identifiers (DisNo.) per country (ISO) We refer to the standard ISO column instead of the Country column of the EM-DAT Public Table to be able to make a join with the country shapefile.

df = pd.read_excel('public_emdat_2024-01-08.xlsx')
earthquake_counts = df[
    (df['Disaster Type'] == 'Earthquake') &
    (df['Start Year'] < 2024)
].groupby('ISO')["DisNo."].count().reset_index(name='EarthquakeCount')
earthquake_counts

91 rows × 2 columns

Load the Country Shapefile

We use the gpd.read_file method to load the country shapefile and parse it into a geodataframe. A geodataframe is similar to a pandas dataframe, extept that has a geometry column.

We provide the filename argument, which is either a file name if located in the same directory than the running script, or a relative or absolute path, if not. In our case the shapefile with the .shp extension is located in the ne_110m_admin_0_countries folder.

Since the geodataframe contains 169 columns, we only keep the two column that we are interrested in, i.e., ISO_A3 and geometry.

gdf = gpd.read_file ('ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp') # <-- Change path if necessary
gdf = gdf[['ISO_A3', 'geometry']]
gdf

Cannot find header.dxf (GDAL_DATA is not defined)

177 rows × 2 columns

Important Notice: Above, some geometries do not have a ISO code, such as the one at row 174. Below, you will see that some ISO in EM-DAT are not matched with a geometries. Beyond this basic tutorial, we advice to carefully evaluate these correspondance and non-correspondance between ISO codes and to read the EM-DAT Documentation about ISO codes.

Join the Two Datasets

Let us merge the two dataset with an outer join, using the merge method. We prefer an outer join to keep the geometries of countries for which EM-DAT has no records.

earthquake_counts_with_geom = gdf.merge(
    earthquake_counts, left_on='ISO_A3', right_on='ISO', how='outer')
earthquake_counts_with_geom

190 rows × 4 columns

Make the Map

To make the map, we use the geopandas built-in API, through the plot method built on the top of matplotlib. Below, we show an hybrid plotting approach and first create an empty figure fig and ax object with matplotlib before passing the ax object as an argument within the plot method. This approach gives more control to users familiar with matplotlib to further customize the chart.

fig, ax = plt.subplots(figsize=(8,3))
earthquake_counts_with_geom.plot(
    column='EarthquakeCount',
    ax=ax,
    cmap='Reds',
    vmin=1,
    legend=True,
    legend_kwds={"label": "Nb of EM-DAT Earthquake"},
    missing_kwds= dict(color = "lightgrey",)
)
_ = ax.set_xlabel('Lon.')
_ = ax.set_ylabel('Lat.')

Creating a Map at Admin Level 1

We can create a more detailed map using the Admin Units column in the EM-DAT Public Table. This column contains the identifiers of administrative units of level 1or 2 as defined by the Global Administrative Unit Layer (GAUL) for country impacted by non-biological natural hazards.

Similarly to the country map, we need to download a file containing GAUL geometries. The file corresponds to the last version of GAUL published in 2015. In this tutorial, we will focus on Japanese earthquake occurrence in EM-DAT.

Note: the file size is above 1.3Go and requires a performant computer to process in Python. Using a Geographical Information Software (GIS) for the preprocessing is another option.

Load the Admin Units Geopackage

The file is a geopackage .gpkg that contains multiple layers. Let us first describe these layers with the fiona package, which is a geopandas dependency.

import fiona
print(fiona.__name__, fiona.__version__)
for layername in fiona.listlayers('gaul2014_2015.gpkg'):
    with fiona.open('gaul2014_2015.gpkg', layer=layername) as src:
        print(layername, len(src))

fiona 1.9.5
level2 38258
level1 3422
level0 277

• The level0 layer contains the country geometries defined in GAUL.
• Here, we make a map at the level1.
• Still, we need to load the administrative level2 because the Admin Units column may refer to Admin 2 levels without mentionning the corresponding Admin 1 level.
• Given the high size the admin 2 layer, we filter the data about Japan and overwrite our geodataframe variable to save memory.

gaul_adm2 = gpd.read_file ('gaul2014_2015.gpkg', layer='level2') 
gaul_adm2 = gaul_adm2[gaul_adm2['ADM0_NAME'] == 'Japan']
gaul_adm2.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
Index: 3348 entries, 23205 to 26552
Data columns (total 13 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   ADM2_CODE   3348 non-null   int64   
 1   ADM2_NAME   3348 non-null   object  
 2   STR2_YEAR   3348 non-null   int64   
 3   EXP2_YEAR   3348 non-null   int64   
 4   ADM1_CODE   3348 non-null   int64   
 5   ADM1_NAME   3348 non-null   object  
 6   STATUS      3348 non-null   object  
 7   DISP_AREA   3348 non-null   object  
 8   ADM0_CODE   3348 non-null   int64   
 9   ADM0_NAME   3348 non-null   object  
 10  Shape_Leng  3348 non-null   float64 
 11  Shape_Area  3348 non-null   float64 
 12  geometry    3348 non-null   geometry
dtypes: float64(2), geometry(1), int64(5), object(5)
memory usage: 366.2+ KB

The Admin 2 geodataframe has 12 columns describing the 3348 level 2 administrative units in Japan.

Filter EM-DAT Data

df_jpn = df[
    (df['ISO'] == 'JPN') &
    (df['Disaster Type'] == 'Earthquake') &
    (df['Start Year'] < 2024)
][['DisNo.', 'Admin Units']]
df_jpn

Note: The last two events were not geolocated at a higher administrative levels.

Convert Admin 2 units to Admin 1 units

We create a python function, json_to_amdmin1, to extract the administrative level 1 codes from the Admin Units column of EM-DAT, based on the ADM1_CODE and ADM2_CODE of the Japan geodataframe.

import json

def json_to_admin1(json_str, gdf):
    """
    Convert a JSON string to a set of administrative level 1 codes.

    Parameters
    ----------
    json_str
        A JSON string representing administrative areas, or None.
    gdf
        A GeoDataFrame containing administrative codes and their corresponding 
        levels.

    Returns
    -------
    A set of administrative level 1 (ADM1) codes extracted from the input JSON.

    Raises
    ------
    ValueError
        If the administrative code is missing from the input data or ADM2_CODE 
        not found in the provided GeoDataFrame.

    """
    adm_list = json.loads(json_str) if isinstance(json_str, str) else None
    adm1_list = []
    if adm_list is not None:
        for entry in adm_list:
            if 'adm1_code' in entry.keys():
                adm1_code = entry['adm1_code']
            elif 'adm2_code' in entry.keys():
                gdf_sel = gdf[gdf['ADM2_CODE'] == entry['adm2_code']]
                if not gdf_sel.empty:
                    adm1_code = gdf_sel.iloc[0]['ADM1_CODE']
                else:
                    raise ValueError(
                        'ADM2_CODE not found in the provided GeoDataFrame.'
                    )
            else:
                raise ValueError(
                    'Administrative code is missing from the provided data.'
                )
            adm1_list.append(adm1_code)
    return set(adm1_list)

We apply the function to all elements of the Admin Units column.

df_jpn.loc[:, 'Admin_1'] = df_jpn['Admin Units'].apply(
    lambda x: json_to_admin1(x, gaul_adm2))

df_jpn[['Admin Units', 'Admin_1']] 

Count Earthquakes per Admin 1 Units

The can be done applying the explode method on the new Admin_1 column. The method will add rows based on the number of Admin 1 we have in each set inside the Admin_1 column. Then the counting can be performed using the former groupby approach.

count_per_adm1 = df_jpn.explode('Admin_1').groupby(
    'Admin_1')['DisNo.'].count().rename('EQ Count')
count_per_adm1.head()

Admin_1
1651    1
1652    4
1653    4
1654    1
1655    1
Name: EQ Count, dtype: int64

Recreate the Admin 1 Layer

Since the Japan geodataframe contains the admin2 geometries, we could load the Admin 1 layer or simply dissolve the geometries based on the ADM1_CODE column. The geopandas package is equipped with the dissolve method.

gdf_jpn_adm1 = gaul_adm2.dissolve(by='ADM1_CODE') 
gdf_jpn_adm1.plot()

<Axes: >

Join the Two Datasets

Again, we can use the merge method to join the datasets together, here, based on their index.

gdf_jpn_adm1_merged = gdf_jpn_adm1.merge(count_per_adm1, 
                                         left_index=True, 
                                         right_index=True, 
                                         how='outer')

Make the Map

fig, ax = plt.subplots()

gdf_jpn_adm1_merged.plot(
    column='EQ Count', cmap='YlOrRd',
    linewidth=0.8, ax=ax,edgecolor='0.8',
    legend=True, 
    legend_kwds={'label': "Earthquake Count in EM-DAT"}
)
_ = ax.set_xlabel('Longitude')
_ = ax.set_ylabel('Latitude')

We have just covered the basics on how to join the EM-DAT pandas DataFrame with a geopandas GeoDataFrame to make maps. To delve further into your analyses, we encourage you to continue your learning of geopandas, matplotlib, or, in particular, cartopy for more advanced map customization, with the many resources available online.

6.2 - External Resources

Note that these resources were not developed or are not maintained by the EM-DAT team. Please, contact the corresponding authors if you have any questions.

Vizualisation Tools

• EM-VIEW: A Streamlit Dashboard to explore and analyze the EM-DAT Public table. Also available as a GitHub repo for customization. Learn more from its initial announcement.

Augmented EM-DAT Data

• Geocoded Disasters (GDIS): an augmented dataset with geocoding, based on the GADM data, for 9,924 disasters between 1960-2018.
• Flood Disasters (FLODIS): an augmented dataset for flood disasters (2000-2018), based on Tellman et al. (2021) and IDMC data, that links EM-DAT to hazard footprints, exposed and displaced population data.

Other Useful Resources

• A note on missing values and imputations summarizing insights from Jones et al. (2022) and Jones et al. (2023).

6.3 - FAQ

1. What is EM-DAT and its purpose? (see Overview of EM-DAT)
2. What are the EM-DAT disaster inclusion criteria? (see Entry Criteria)
3. What kind of information is included in EM-DAT? (see Data Structure and Content Description)
4. What is the value of the economic damage entered into EM-DAT? (see Economic Impact Variables)
5. How are the data compiled? (see Encoding, Quality Control, and Validation Procedure)
6. What is the spatial resolution of the EM-DAT? (see Spatial Information and Geocoding)
7. What is the updating interval for EM-DAT figures? (see Daily Encoding)
8. How can I download/access the EM-DAT data? (see How to Download the EM-DAT Public Data)
9. What are the conditions of use? (see Use Of EM-DAT Database Data And Derived Products)
10. How can I be kept informed about EM-DAT news and publications? (Join the Newsletter)
11. How can I contact the EM-DAT team? (see Contact)

7 - About the EM-DAT Project

7.1 - EM-DAT and the CRED

The Centre for Research on the Epidemiology of Disasters (CRED)

The Centre for Research on the Epidemiology of Disasters (CRED) is currently a research center that is part of the Institute Health and Society (IRSS) based at the Université catholique de Louvain (UCLouvain) in Brussels, Belgium. The CRED was established in 1973 and has since become one of the world’s leading research centers working on the epidemiology of disasters.

The CRED’s main focus is on studying the impact of disasters on populations and on ways to improve the effectiveness of disaster response and risk reduction. The CRED’s research includes collecting, analyzing, and disseminating data on disasters caused by natural and technological hazards, within the EM-DAT project, as well as data on conflicts and other humanitarian emergencies.

The CRED’s research findings are used to inform policy and practice in disaster management in Belgium and internationally. In addition to its research activities, the CRED provides training, consultancy, and technical support to governments, humanitarian organizations, and other stakeholders involved in disaster risk reduction and emergency response.

The Joint History of CRED and the EM-DAT International Disaster Database

In the 1970s, the approach to disaster management was predominantly reactive, emphasizing immediate rescue and relief operations. Recognizing the imperative for a more proactive, informed, and systematic approach to disaster management, Professor Michel Lechat (UCLouvain) established the Centre for Research on the Epidemiology of Disasters (CRED) in 1973. One of the primary challenges faced by the emerging field of disaster epidemiology was the absence of comprehensive global data. A holistic perspective on disasters was essential not only to solidify the foundations of this nascent scientific domain but also to ensure evidence-based disaster management strategies.

In 1984, Debarati Guha-Sapir joined the UCLouvain CRED team and collaborated with Prof. Lechat to initiate the development of the EM-DAT database. This endeavor built upon the foundational efforts of a prior database project spearheaded by the United States Agency for International Development/Office of Foreign Disaster Assistance (USAID/OFDA). The CRED then embarked on the meticulous task of data aggregation from diverse sources, including UN agencies, non-governmental organizations, and media outlets. The CRED has been managing the EM-DAT database since 1988. After Lechat, Prof. Guha-Sapir assumed the directorship of CRED in 1992, a role she held until 2021 when Prof. Niko Speybroeck took on the role of director.

Thanks to the support of USAID, EM-DAT has evolved into a public database and became an valuable unique resource for disaster research and management.

7.2 - EM-DAT Partners

USAID Bureau for Humanitarian Assistance (USAID/BHA)

The Bureau for Humanitarian Assistance (BHA) is part of the US Agency for International Development (USAID). BHA is responsible for coordinating and providing humanitarian assistance to people affected by disasters and crises around the world. BHA was formerly known as the Office of US Foreign Disaster Assistance (OFDA) until it was rebranded in 2020.

BHA works in collaboration with other US government agencies, non-governmental organizations, and international partners to deliver aid to those in need. The agency responds to a range of emergencies, including disasters, conflicts, and disease outbreaks, and provides various form of assistance, such as food, shelter, medical care, and water and sanitation services. BHA also works to build the capacity of communities and local organizations to better prepare for and respond to emergencies in the future. Since 1999, USAID/BHA has supported the EM-DAT project.

Scientific & Technological Advisory Group (STAG)

Inaugurated in March 2023, the first Scientific & Technological Advisory Group (STAG) meeting took place in Brussels. The STAG takes over from other previously organized meetings called Technical Advisory Group (TAG) meetings. Technical Advisory Group (TAG) Meetings have been implemented every two years on average as part of the evaluation of the USAID-BHA/CRED initiative related to the EM-DAT international disaster database. The objective of the TAG meetings was to discuss and share with the TAG members different aspects related to the EM-DAT initiative and development. Those meetings produce a series of recommendations for the continuous improvement of the EM-DAT database and its related proposed services.

In 2023, the first STAG meeting also included experts from the scientific community. The STAG included participants from several national, international, and UN public agencies, humanitarian agencies, research centers, and universities. The group was composed in such a way as to bring together expertise on various types of disasters and profiles with different skills and interests, from research to field actors to public institutions.

For more details and documents on the TAG and STAG meeting, please visit our dedicated web page.

7.3 - Ways to Contribute

Contributing

CRED regularly collaborates with researchers and institutions to improve the quality and use of disaster impact data. You can help us in various ways:

• By reporting errors in the database (see Issue Reporting).
• By sending us the results of your work if it could improve the quality and completeness of the EM-DAT database.
• By answering calls for projects together for research or technological development.
• By doing an internship, a thesis, or a PhD with or in partnership with the CRED.

If you are considering a project that could directly benefit EM-DAT by improving global reporting of disaster-related impacts, we recommended that you contact the EM-DAT team to consider possible improvements of the database and thus add value to your results. For projects directly involving the CRED, however, we cannot accept all the opportunities for collaboration offered to us. We prioritize projects directly related to disaster epidemiology or that allow us to improve the coverage and quality of EM-DAT data substantially. If this applies to you, do not hesitate to contact us at the CRED.

7.4 - Contact

Address

The CRED is part of the Institute of Public Health (IRSS) of the University of Louvain (UCLouvain). You can reach us by mail at:

Send Us an Email

contact@cred.be

Follow Us on …

• Twitter
• Facebook
• LinkedIn

Newsletter Subscription

You can subscribe to our CRED Crunch newsletter to receive quarterly news about the EM-DAT project and theme-specific analyses of the database contents.

8 - Legal Texts

8.1 - Use Of EM-DAT Database Data and Derived Products

UCLouvain 2023

If you are an academic organization, a university, a non-profit research institution or an international public organization (UN agencies, multi-lateral banks, other multi-lateral institutions, and national governments) or part of a media agency (journalist, or press agency) with the intention to use the EM-DAT database (hereafter EM-DAT) for research, teaching or information purposes, you shall, conditional upon the acceptance of the present conditions of use, be granted free access to EM-DAT (see also Authorized Use).

Users of EM-DAT declare that they will not (see also Unauthorized Use)

• Reproduce, copy, communicate, lend, or otherwise distribute EM-DAT or a substantial part of EM-DAT.

• Reverse assemble, decompile, decompose, or disassemble EM-DAT.

• Create substitute or derivative databases of EM-DAT.

• Attempt to unlock or bypass any initialization or security systems used by EM-DAT.

• Share, use or transmit any portion of EM-DAT via the Internet to unauthorized users.

• Divulge the password to unauthorized users.

• Remove, alter or obscure any proprietary legend, copyright, trademark, or other intellectual property right notice, logo, and image in or on EM-DAT.

• Perform acts that conflict with the normal exploitation of EM-DAT or unreasonably prejudice the interest of the Licensor.

• Make Commercial Use of EM-DAT, except in the event such users enter into a separate Database License Agreement, described hereafter.

If you are an entity (which may include a corporation, partnership, limited liability company, law firm, or other business organization) that is not an academic organization, university, non-profit research institution, or an international public organization (UN agencies, multi-lateral banks, other multi-lateral institutions, and national governments), the use of EM-DAT shall be considered to be Commercial Use. More precisely, this means the use of EM-DAT for a commercial purpose rather than for a public, non-profit, or educational purpose. A commercial purpose is considered when: the use of EM-DAT for, among others, sale, lease or license or the use of EM-DAT to perform contract research, to produce or to manufacture products for sale purposes, or to conduct research activities that result in any sale, lease, license, or transfer of EM-DAT to any organization. Except if agreed upon in a separate Database License Agreement, EM-DAT (including its data and derivate products) cannot be used for any commercial purpose.

In cases of Commercial Use, the user shall send a prior written request to Ms. Regina BELOW, EM-DAT data manager – regina.below@uclouvain.be (hereinafter “the Database Manager”) describing the aim of such Commercial Use and the number of users needing access to such data. Access shall be granted to EM-DAT upon proof of payment of the corresponding annual fee, as agreed in a separate Database License Agreement.

General Terms and Conditions

• Access to EM-DAT shall be granted as of the moment the user agrees to it by clicking on the “I agree” button.

• All published papers and technical reports from the EM-DAT Database owner related to EM-DAT may be used free of charge by the user, provided the user uses Proper Citation. Proper Citation of both EM-DAT and data obtained through EM-DAT is the responsibility of the user alone. “Proper Citation” of EM-DAT means:

“EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be”

• Certain personal data may be collected and processed via our site in order to provide access to EM-DAT, for example, through the registration form. The collected personal data shall be used exclusively for the purpose indicated above, in accordance with the Belgian Law of July 30, 2018, on the protection of privacy in relation to the processing of personal data (as amended and the General Data Protection Regulation, Regulation (EU) 2016/679 of April 27, 2016, of the European Parliament and the Council Concerning the protection of individuals with regard to the processing of personal data and the free movement of such data and repealing Directive 95/46 / EC (General Data Protection Regulation)).

• The CRED makes every effort to ensure, but cannot and does not guarantee, and makes no warranties as to, the accuracy, accessibility, integrity, and timeliness of this information. The CRED assumes no liability or responsibility for any errors or omissions in the content of this site and further disclaims any liability of any nature for any loss, howsoever caused, in connection with using EM-DAT. The CRED may make changes to EM-DAT at any time without notice. If you are undertaking an in-depth analysis, we recommend that you consult our staff to obtain a good understanding of the weaknesses, limitations, and peculiarities of our data;

• Any use that causes unjustified damage to the legitimate interests of CRED – UCLouvain is strictly forbidden. The same applies to the use with the aim of creating a tool in competition with EM-DAT;

• CRED reserves the right to restrict access by any user who contravenes these conditions of use.

8.2 - Database License Agreement (Commercial Use)

UCLouvain 2023

This Database License Agreement (the Agreement) is made between yourself (the Licensee) and Université catholique de Louvain (UCLouvain), a Belgian University with its registered office located at Place de l’Université, 1, B-1348 Louvain-la-Neuve, Belgium, acting through its Research Group “Center for Research on the Epidemiology of Disasters” or CRED (the Licensor).

WHEREAS the Licensor has developed the EM-DAT database (hereinafter the Database) made available on the internet subject to its conditions of use;

WHEREAS the Database aims at providing an objective basis for impact and vulnerability assessment and rational decision-making in disaster situations by collecting, organizing, and giving access to validated data on the human impact of disasters (such as the number of people killed, injured, or affected), and the disaster-related economic damage estimates;

The Licensor wishes to lay down the conditions enabling the Licensee to use the Database for Commercial Purposes.

Article 1: Ownership of the Database

The Licensor guarantees to be the owner of all intellectual property rights related to the Database, including all copyrights, and to determine the content of the Database. The Licensor shall, at all times, have the right amongst other decisions, without any prior notice or motivation, to (i) modify the data disclosed, (ii) disclose other data, (iii) suspend the availability of the Database for maintenance or any other purposes, (iv) decide to cease making available such Database. The Licensor shall notify the Licensee of any amendment that might have a consequence on the access rights of the Licensee by email at the Licensee’s email address.

Article 2: License Conditions of the Database

2.1. Subject to the terms set forth in this Agreement and conditional upon the payment of a license fee, the Licensor agrees to make available to the Licensee the data contained in the Database for Use as detailed in article 2.2 of this Agreement. For the sake of clarity, the Licensee shall be an entity (which may include a corporation, partnership, limited liability company, law firm, or other business organization) that is not an academic organization, a university, a non-profit research institution, or an international public organization (UN agencies, multi-lateral banks, other multi-lateral institution, or national governments).

2.2. Use means that the Licensor grants to the Licensee a limited, non-exclusive, non-transferable, and revocable license to use the Database for internal purposes by the Licensee’s direct employees. The Licensee shall not have the right to sell, assign, transfer, rent, lease, sublicense, lend, give, or make available to others or otherwise transfer or dispose of the Database in its present form or as converted or modified by Licensee or Licensor, or make the Database available in any manner for use by any subsidiary establishment of Licensee or by any other person, or firm, or customer.

Except if explicitly agreed upon by the Licensor, the Licensee shall not reverse, decompile, disassemble or otherwise reverse engineer the whole or part of the Database nor modify, adapt or translate the Database in any way nor merge the whole or part of the Database with any other Database.

2.3. The Licensee acknowledges that, except for the use rights granted in this article 2, no intellectual or any other proprietary right are granted, transferred, or assigned through this Agreement. All intellectual property rights, including but not limited to copyrights, trademark rights, and database rights, used or embodied in or in connection with the Database are and remain entirely with the Licensor.

2.4. Access to the Database. Upon completion of the registration information on www.emdat.be, acceptance of the present Database License Agreement, and payment of the license fee (article 4 of this Agreement), the Licensor shall provide the Licensee with a password for internet access to the Database. Data transmission and computer link to the Database through the internet shall be the sole and exclusive responsibility of the Licensee.

Article 3: Personal Data

The Licensee agrees that certain personal data may be collected and processed via our site, for example, through the registration form to be completed, in order to provide access to the Database. The collected personal data shall be used exclusively for the purpose indicated above. Licensor undertakes to process such data in accordance with the Belgian Law of July 30, 2018, on the protection of privacy in relation to the processing of personal data, as amended and the General Data Protection Regulation, Regulation (EU) 2016/679 of April 27, 2016, of the European Parliament and the Council Concerning the protection of individuals with regard to the processing of personal data and the free movement of such data and repealing Directive 95/46 / EC (General Data Protection Regulation) The legal texts can be consulted on the website of the Commission for the Protection of Privacy (statbel.fgov.be/en/about-statbel/privacy/privacy-gdpr).

The data collected is neither transferred nor transmitted to any other organization. However, the Licensor reserves the right to disclose information and personal data at the request of a legal authority in accordance with the laws and regulations in force.

Article 4: Fees

In exchange for the Commercial Use of the Database, the Licensee shall pay an annual fee equal to the amount of 6.000,00 EUR (TVA not included).

The Database shall be available upon proof of payment of the corresponding fee.

Article 5: Warranty and Liability

5.1. Warranty. The data contained in the Database are given for information purposes only and do not constitute any form of advice, recommendation, representation, or endorsement. The Licensor disclaims all warranties and/or conditions of any kind, expressed or implied, in respect of the functions, performances, completeness, or accuracy of the Database. Licensor does not guarantee that the functions or performances of the Database will meet the Licensee’s requirements, that the operation of the Database will be uninterrupted or error-free, or that any defects in the Database will be corrected. The Licensor shall have no obligation to repair or replace the Database under any circumstances.

The Licensor makes every effort to ensure, but cannot and does not guarantee, and makes no warranties as to, the accuracy, accessibility, integrity, and timeliness of this information. The Licensor assumes no liability or responsibility for any errors or omissions in the content of this site and further disclaims any liability of any nature for any loss, howsoever caused, in connection with using the Database. The Licensor may make changes to the Database at any time without notice. If you are undertaking in-depth analyses, we recommend that you consult our staff to obtain a good understanding of the weaknesses, limitations, and peculiarities of our data;

5.2. Liability. The Licensor shall in no event be liable for any business decision taken by the Licensee based on the data made available through the Database. The entire risk arising out of the use of the Database remains with the Licensee. Under no circumstances, including claims of negligence, shall the Licensor be liable for any damages whatsoever (including, without limitation, damages for loss of business profits, interruption of business activity, loss of business information, or other monetary loss) arising out of the use or inability to use the Database. More in particular, since the use of and access to the Database depends, in part, on third parties (e.g., telecommunications carriers) whose performance is outside Licensor’s control, the Licensor disclaims all liability for damages arising from the failure of the transmission or receipt of data.

The entire risk arising out of the possible reproduction and distribution of the Database remains with the Licensee.

Article 6: Citation

The Licensee agrees to use Proper Citation of the Database in all public use of the Database or its data. All published papers and technical reports from the Licensor related to the Database may be used free of charge by the Licensee, provided the Licensee uses Proper Citation. Proper Citation of both the Database and data obtained through the Database is the responsibility of the Licensee alone. “Proper Citation” of the Database means:

“EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be”

Article 7: Duration of the Agreement

7.1. Term. This Agreement shall become effective as of the date upon which payment of the license fee has been received by the Licensor (the Effective Date) for a definite term of one (1) year counting as from such Effective Date.

7.2. Termination. The Agreement shall automatically terminate at its termination date.

The Agreement shall automatically terminate, without any compensation due, in case the Licensor no longer provides access to the Database or ceases its activities. The Licensor shall notify the Licensee of such termination with no undue delay at the Licensee’s email address.

Without prejudice to any other rights and remedies.

Under this Agreement or at law, the Licensor may have to claim compensation for damages incurred; the Licensor shall have the right to terminate the Agreement immediately without any prior notification in case of breach of use by the Licensee. In such case, the Licensee shall no longer have access to the Database and shall be notified hereof at the moment of the first login after termination.

7.3. Consequences of termination. Upon termination of this Agreement, the Licensee will cease and desist from all use of the Database and will uninstall, remove and destroy all copies of the Database in the Licensee’s possession or control, including any modified or merged portions thereof, in any form, and execute and deliver evidence of such actions to the Licensor. The Licensee shall remain bound by those provisions of the Agreement which, by their terms, extend beyond the date of termination.

Article 8: Miscellaneous

8.1. Assignment. Neither this Agreement nor any of the Licensee’s rights hereunder shall be assigned, sublicensed, or transferred (in insolvency proceedings, by mergers, acquisitions, or otherwise) by the Licensee without the previous written consent of the Licensor. Any assignment or other transfer which is inconsistent with the foregoing shall be null and void ab initio.

8.2. Entire Agreement. This Agreement constitutes the entire agreement between the parties with respect to the subject matter of this Agreement and supersedes all previous agreements, arrangements, or undertakings between Licensor and Licensee relating to its subject matter and any representations or warranties previously given or made to it, if any.

8.3. Failure or neglect of the Licensor to enforce any provision of this Agreement at any time shall not be construed or deemed to be a waiver of its rights and shall not in any way affect the validity of this Agreement or any of its provisions nor prejudice the Licensor’s right to take subsequent action.

8.4. In the event that any provision of this Agreement is deemed by any competent authority having jurisdiction to be invalid, unlawful, or unenforceable to any extent, that provision shall, to that extent, only be severed from the remaining provisions, which shall continue to be valid.

8.5. Jurisdiction venue. In the event of any dispute arising out of or in connection with the subject matter of this Agreement, the Parties shall first endeavor to resolve such dispute amicably within thirty (30) days after the date of the notification by one Party of such dispute to the other Party. Should the Parties fail to do so, then such dispute shall be subject to Belgian law except its conflicts of law rules and the competent courts of Brussels.