EM-DAT: the Emergency Events Database

<title>Abstract</title>         <p>The Emergency Events Database (EM-DAT) compiles global disaster data resulting from technological and natural hazards. EM-DAT details the human and economic impacts from 1900 to present, with systematic recording since 1988. Serving the humanitarian, disaster risk reduction, and academic sectors, EM-DAT's transition to open access and climate change concerns have expanded its reach and visibility. The dataset, freely available for non-commercial use, is downloadable in a tabular format. It is categorized by hazard type and standardized by individual disaster events for each country. With over 26,000 unique entries, the database is populated through the monitoring of textual documents and their manual processing. The data collection and validation processes involve systematic daily checks from predefined sources, searches for additional sources, and periodic thematic updates. The evolution of EM-DAT's data content mirrors societal and technological advancements in disaster reporting. Besides these progresses, known inconsistencies and biases in data quality have been reported. The article acknowledges these issues, highlighting their potential implications for research and decision-making.</p>

section_content: The EM-DAT Emergency Events Database (www. emdat. be) was established in 1988 by the Centre for Research on the Epidemiology of Disasters (CRED) at the Université catholique de Louvain (UCLouvain) 1. Originally a collaboration with the World Health Organization (WHO), EM-DAT was part of a broader global emergency management data integration system [1] [2] [3] [4] and was primarily intended for decision-makers and policymakers to support disaster preparedness and emergency management 1, 5. In EM-DAT, disasters are defined as situations or events that overwhelm local capacity, meeting at least one of the following criteria: 1. 10 deaths or above. 

2. 100 people affected or above. 

3. A call for international assistance or a declaration of a state of emergency. 

Globally and for each country, EM-DAT provides data on disaster occurrences and their human and economic impacts. As of July 2023, EM-DAT has recorded over 26,000 disasters from both natural and technological hazards since 1900. 

EM-DAT's data collection is a manually supervised process involving significant human oversight. The EM-DAT staff systematically reviews a list of pre-identified sources, including reports and catalogs from United Nations (UN) organizations, humanitarian agencies, non-profit organizations, research institutes, reinsurance groups, and press agencies. These sources are manually processed to input disaster records and their corresponding impact figures into EM-DAT, supplemented with information from other sources found through active internet searches. This methodology has been employed for over 35 years. Before the 1990s and the rise of the internet, however, the data was sourced from a lower amount of printed materials. 

The foundational content of the database was mainly data from the United States (US) Office of Foreign Disaster Assistance (OFDA) established in 1964, currently known as the Bureau for Humanitarian Assistance (BHA), within the US Agency for International Development (USAID) 6. 

EM-DAT's use has evolved to play a pivotal role in advancing a comprehensive understanding of disaster risks, i. e., not limited to emergency management. Internationally, EM-DAT contributes to the monitoring of UN Disaster Risk Reduction (DRR) agendas [7] [8] [9] and other UN institution disaster-risk-related activities [10] [11] [12] [13]. The current global DRR agenda under the UN is the 2015-2030 Sendai Framework for Disaster Risk Reduction (SFDRR) 9. EM-DAT's data provide input for SFDRR Target A to C, aiming to reduce (A) global disaster mortality, (B) the number of people affected globally, and (C) economic losses relative to the Gross Domestic Product (GPD). SFDRR targets and indicators also intersect with those of the UN Sustainable Development Goals (SDGs), in particular, poverty (SDG-1), sustainable cities and communities (SDG-11), and climate action (SDG-13), articulating the interconnections between disaster risks, sociodemographic factors, and climate change 14, 15. 

Over time, EM-DAT users have progressively linked disaster impact data with risk components, such as those defined within the SFDRR 16 : the hazard durations and magnitudes, the subsequent exposure of populations and assets to these hazards, the environmental and sociodemographic vulnerability factors in exposed areas, and the capacity to manage risks over various time scales in these areas. While EM-DAT impact data alone do not provide an exhaustive characterization of risks, it offers valuable inputs to data hubs, which integrate complementary data on various components, such as population density, sociodemographic data, or other more specific sources of disaster impact data. Examples of such disaster risk data hubs include Risk Information eXchange (RIX) platform of the UN Office for Disaster Risk Reduction (UNDRR) 17, the Humanitarian Data eXchange (HDX) platform of the UN Office for the Coordination of Humanitarian Affairs (OCHA) 18, or the Risk Data Hub of the European Commission (EU) Disaster Risk Management Knowledge Centre (DRMKC) 19, within which EM-DAT data contributes to the computation of the Index for Risk Management (INFORM), a risk indicator facilitating the comparison of risk profiles between countries 20. 

Since its launch in 2020, the EM-DAT public data portal has been freely accessible for non-commercial use upon registration. 

As of September 26, 2023, more than 59,000 users have registered. 87% of all EM-DAT users are part of academic institutions, universities, and non-profit research institutions. These figures underscore the widespread interest in EM-DAT data within the academic and scientific community, with usage ranging from anecdotal references to disaster figures to more substantial, in-depth analyses and applications of the data. Since the 2020 open access of EM-DAT, some references have proposed solutions to enhance EM-DAT by linking it to other datasets [21] [22] [23] [24]. 

Fig. 1 provides a descriptive overview of EM-DAT's utilization within the scientific community by analyzing peer-reviewed articles citing EM-DAT in the Scopus database. These articles, termed 'EM-DAT publications,' were published between 2003 and 2022 and mentioned 'EM-DAT' in the title, abstract, keywords, or references. Over these 20 years, there has been an increase in EM-DAT publications, reaching up to 250 articles per year (Fig. 1a ). Most of these belong to the Scopus domains of Physical Sciences, followed by Social Sciences. By normalizing the number of EM-DAT publications against the annual total for each Scopus domain, changes are observed in the relative interest in EM-DAT. For instance, Fig. 1b shows a major relative increase in Social Sciences. Fig. 1c summarizes trends in specific subject areas within the domains. Within Physical Sciences, subject areas contributing mostly are Environmental Sciences (ENVI) and Earth and Planetary Sciences (EART), followed by Engineering (ENGI), Energy (ENER), and Computer Sciences (COMP) in the top ranks (Fig. 1c ). In Scopus, Social Sciences is both a domain and a specific subject area (SOCI, Fig. 1c ) which is, with Economics, Econometrics, and Finance (ECON), Business, Management, and Accounting (BUSI), and Decision Sciences (DECI), making the most significant contributions to EM-DAT Social Sciences trends (Fig. 1a and b ). 

Despite EM-DAT's origins as a public health initiative, there has been no substantial change in the absolute or relative number of EM-DAT publications in Health Sciences over the past decade. A similar observation can be made for the Life Sciences domain. However, some specific subject areas within these domains remain significant (Fig. 1c ). Medicine (MEDI) accounts for over 200 documents, and Nursing (NURS) and Multidisciplinary Health Sciences (MULT) are showing upward trends. Within the Life Sciences domain, Agricultural and Biological Sciences (AGRI) stands out among the other fields in the group. 

EM-DAT's rising prominence can be attributed to its shift to open access, a general increasing focus on climate-related disasters, and its unparalleled position in the limited landscape of disaster impact data 25. Straightforwardness, large scope, and global coverage also contribute to EM-DAT's attractiveness as a user-friendly Excel file encompassing a wide range of disaster types for each country. Beyond EM-DAT, only the UNDRR Sendai DesInventar database stands as an open-access multi-disaster, impact database tailored for global coverage. It offers a higher spatial accuracy by disaggregating impact data into administrative units within a specific country 25. However, as it relies on voluntary contributions, DesInventar spatiotemporal coverage is irregular, albeit improving under the SFDRR agenda. 

While EM-DAT aims to provide continuous spatiotemporal coverage, it is not free from biases arising from the complexities of monitoring disaster impact data 4, [26] [27] [28] [29] [30] [31] [32]. Even though comprehensive knowledge exists on the meteorological state variables at the land surface, e. g., spanning globally from 1940 to the present 33, challenges remain in accurately capturing the mortality or broader impacts of weather-related disasters 34, 35, as for other types of disasters. The database contains significant missing data regarding human and economic losses, with proportions varying across time and disaster types 36. Scientific studies seem to regularly overlook issues related to missing data and biases in disaster impact data 37. In particular, this publication aims to supersede the outdated 1992 paper reference on EM-DAT 4 to reflect its actual and current content, structure, and outreach.
section_name: Background & Summary

section_content: EM-DAT collects disaster data from various sources following several documentation routines. Among all the collected figures found in the sources, the validation routines control what information is being published to the end user.
section_name: Methods

section_name: 2/16

section_content: The EM-DAT team processes textual documents, referred to as sources, to extract data about the disaster type, the timing and location of the event, and the impact figures per country. In complement to this section, we refer to Table 2 for a column-based description of the dataset served to the registered users, following the EM-DAT documentation 38. 

Disasters are hierarchically classified in a four-level classification tree. For disasters related to natural hazards, the classes are based on the Integrated Research on Disaster Risk (IRDR) Peril Classification and Hazard Glossary 39, 40. Event start and end dates and locations are directly extracted from sources, with countries identified using a three-letter standard country code (ISO-3166) 41, 42. Standardization for region, subregion, and country names follows the UN M-49 standard 41, 43. For non-biological natural hazards occurring after 2000, additional spatial information is provided up to the second administrative level of the 2015 Global Administrative Unit Layers (GAUL) by the Food and Agriculture Organization (FAO) 41. However, while EM-DAT references GAUL units identified from locations mentioned in the sources, the impact figures are not distributed among them. 

Human impact data include the total number of deaths (including long-term missing individuals) and the count of total affected individuals, which combines numbers of injured, homeless, and otherwise affected individuals, where the concept of otherwise affected people is less explicit than that of injured and homeless individuals. Whenever possible, EM-DAT numbers are reported as specified in the sources. However, based on regional demographic statistics updated by the United Nations Development Programme (UNDP) 44, the EM-DAT team estimates the number of people impacted when it is expressed in terms of impacted housing units or families. Likewise, when a source refers to an order of magnitude, such as 'thousands affected,' they are entered in the database as the minimal suggested amount. For example, in such cases, 2,000 people affected would be encoded. 

Economic impact figures in EM-DAT include the total damage estimated by the sources, converted in US$ at the time of the disaster. Less frequently, EM-DAT includes figures for insured damage and reconstruction costs, also in US$. To account for the effect of inflation on economic loss figures, EM-DAT also provides values adjusted using the Consumer Price Index (CPI) provided by the Organisation for Economic Cooperation and Development (OECD) 45. EM-DAT's data processing and structuring methods have evolved since EM-DAT's inception in 1988, particularly concerning disaster classification systems and definitions due to interactive discussions with stakeholders and researchers 4, 39, 40, 46, 47. For updates on future changes, readers are directed to the official EM-DAT documentation 48.
section_name: Collected Data

section_content: The sources utilized in EM-DAT are not displayed in the public records for various reasons, including legal considerations, preserving the integrity of services EM-DAT depends on, and upholding neutrality, especially when dealing with potentially politically sensitive data. Nevertheless, Table 1 and Fig. 2 provide a summarized overview of the sources collected following the process described in the Documentation Routines section for the 2000-2022 period. Built with the user in mind, the table features the top-20 sources that reference at least one figure contributing to the three primary impact variables in the EM-DAT public table: Total Deaths, Total Affected, and Total Damage (Table 2 ). The top-20 covers 96. 9% of the total number of entries, as defined in Table 1 for the 2000-2022 period. 

EM-DAT's paid subscription to Agence France Presse (AFP) provides extensive coverage of disasters. In particular, AFP is important in EM-DAT to track disasters of lower impact and, therefore, of higher frequency. Within the Press group, AFP is the primary source, selected around 50% of the time when documenting a disaster, except for total economic damage (Fig. 2d ). 

Data from the International Federation of Red Cross and Red Crescent Societies (IFRC), with its emphasis on humanitarian aspects, is invaluable for documenting the health impacts of disasters (Fig. 2b and 2c ). In 90% of cases, IFRC sources provide figures relative to the total number of affected people (Fig. 2c ), and, in 65% of cases, IFRC sources indeed contributed to the calculation of the Total Affected variable (Table 2 ) in the published records. A similar pattern is observed for OCHA sources or the less frequent 'Government' category, a grouped category that consolidates official reports from various governmental bodies and institutions. Reinsurance reports provide vital insights into economic damage (Fig. 2d ), though they play a lesser role in detailing affected populations (Fig. 2c ). Contributions from UN or US/EU sources display no consistent pattern. However, OFDA/BHA, part of the US/EU group, is a key source for historical data 6, primarily before 2000. ECHO transitioned from a complementary source to a systematic one in 2019, resulting in a noticeable increase in contributions from the US/EU group that year (Fig. 2a ). It is important to note that the source system for EM-DAT is dynamic; certain systematic sources have either ceased to be used or have become obsolete for practical reasons (Table 1 ). This non-stationarity introduces uncertainties and potential biases in data quality, as further discussed in the Technical Validation section.
section_name: EM-DAT Sources

section_content: Three internal documentation routines (A1 to A3, Fig. 3 ) support the completion of the EM-DAT database, complemented by potential external contributions from user notifications. The systematic routine (A1) is the day-to-day documentation process. A set of systematic sources (Table 2 ) are reviewed based on their update or release frequency, which can vary from daily to yearly. Fig. 3 refers to entries as disaster impacts per country, as in the downloadable EM-DAT data to align with the user perspective 49. 

If sources further document an existing entry, the sources and their impact figures are attached to that entry. Attaching a figure to an entry makes it potentially available for selection and display in the public EM-DAT data (see Validation Routines). 

Most top-20 sources (Table 2 ) are associated with the systematic day-to-day documentation routine (A1, Fig. 3 ). Nonetheless, the top-20 list also includes complementary sources such as 'Wikipedia' and 'Government,' resulting from the complementary routine (A2, Fig. 3 ). This routine involves active internet searches to fill in missing impact variables or to update figures with more recent or dependable data. The last documentation routine (A3) relies on occasional thematic reviews. These reviews are designed to enhance data with a specific focus, such as a particular type of impact or hazard, or by leveraging a new data source. 

For example, a thematic review might concentrate on refining heatwave data through a comprehensive review of scientific literature.
section_name: Documentation Routines

section_content: Validation routines systematically begin with documentation routines to ensure their completion and the latest status of the collected data. Subsequently, the validation process continues by selecting figures for display in each entry of the public dataset and confirming that the entries meet EM-DAT criteria for publication. We identify three manually supervised validation routines following different time frames: monthly (B1, Fig. 3 ) and annual validations (B2), as well as thematic reviews (B3). 

Every month, as part of the monthly validation (B1), a complementary documentation routine (A2) completes the data added continuously during the month with systematic routines (A1). Any disaster still ongoing during the previous validation is also reviewed. Then, the EM-DAT team proceeds with the selection of the figures for public display. This selection and the subsequent prioritization of some sources over another is influenced by various factors: the figure itself, the area and period it covers, the chronology of the sources, and their reliability, with a preference for institutional or scientific sources over press sources. This attribution task is both complex and case-specific, and the defined guiding rules are voluntarily kept flexible, leaving the final decision to the discretion of the EM-DAT team. We refer to Fig. 2b-d to highlight what sources tend to be selected relative to what impact. Finally, if an entry meets the EM-DAT entry criteria, i. e., at least ten deaths, 100 affected persons, or a national or international emergency situation, its status is updated to 'public,' provided it is not already public. EM-DAT further imposes that at least two distinct sources are attached to the entry to be set public. A newly added or updated entry will be accessible to users by the following week. 

The annual validation (B2) is conducted during the January-February period, scoping the preceding year's data before publishing the annual report. This routine involves both systematic and complementary documentation processes (A1, A2) for all events from the previous year, as well as those that were ongoing the prior year. In contrast, thematic reviews (B3) are conducted once or more per year and are associated with occasional documentation routines (A3) that cover a diverse range of topics. Consequently, reviews focusing on the same topic are spread over multiple years. 

As of September 2023, updates to the database model and data entry interface have introduced enhanced systems to automatically prevent the manual encoding of forbidden or incorrect values or formats (B4). Occasionally, errors in the EM-DAT database may not be related to impossible values or formats, such as duplicated events or typographical errors in text or numbers 32. To address these issues, automated validation routines also generate warnings to highlight anomalies and potential errors. These automated routines check for potential duplicated events based on their location and timing and ensure the consistency of date and time fields, latitude and longitude values, and hazard magnitude values. These procedures are implemented progressively as errors are identified or reported by users.
section_name: Validation Routines

section_content: The EM-DAT public table is freely accessible for non-commercial use upon registration via the EM-DAT Public Portal (https://public. emdat. be/). Users can download an Excel file containing the data, following the Office Open XML standard. 

The online form allows for spatiotemporal queries and hazard-type filtering. Each row in the EM-DAT Excel file represents a collection of disaster impacts within a specific country 49, along with additional attributes (Table 2 ). 

As of September 2023, EM-DAT contains more than 26,000 public entries from 1900 to the present. Fig. 4 provides an overview of the EM-DAT content distribution by hazard types, following the third level of the EM-DAT classification tree. 64% of EM-DAT disasters belong to the 'Natural' group, most of which refer to floods (22%) and storms (18%). Within the remaining 36% of 'Technological' hazards, transport accidents (road, water, air, and rail) are the most frequent.
section_name: Data Records

section_content: EM-DAT is known to have several limitations. In the existing literature, these limitations often pertain to the fundamental design of EM-DAT. For example, criteria for data entry in EM-DAT and the granularity of the data are sometimes seen as too coarse for specific purposes. In most cases, they are not sufficiently disaggregated by administrative units or vulnerable population groups (e. g., age, income, or sex categories). While EM-DAT does not provide services beyond its scope, its prominence in the field inevitably brings into focus its limitations for scientific research or the monitoring of local and global disaster risk management agendas such as the SFDRR and the SDGs. This article, along with the EM-DAT documentation released in September 2023, supports the transparent outline of these design-related limits. 

Other limitations are related to data quality issues, which have been regularly overlooked in previous research relying on EM-DAT 37. Primarily, three types of issues arise: (i) omitted disaster events, as revealed in local case studies 50, 51, (ii) events with missing values, especially in impact variables 36, 37, and (iii) events with inconsistent or inaccurate attributes compared to other sources 31, 32. 

Variations in data collection sources, protocols, and explicit or implicit disaster reporting systems contribute to discrepancies between disaster loss databases. Biases arising from the reporting system are termed systemic biases 27. Quantifying these biases is a complex task, encompassing the broader societal context of disaster reporting, as well as the specific ways in which disasters are captured, defined, and structured within reporting services and disaster loss databases. Despite these challenges, some understanding of systemic biases is possible through a better understanding of documentation protocols used by disaster loss databases and by gaining insight into the operational challenges faced by managers of these databases 4, 26, 28, 29. 

All biases are related to reporting systems, hence to systemic biases, but some are more specific and thus more easily visualized. These biases may be called time, geographic, hazard-related, threshold, or accounting biases 25, 27. In other words, data quality depends on disaster attributes such as the date it occurred, its location, the type of hazard, its magnitude, and the type of impact monitored (Fig. 5 ). Time biases (Fig. 5a ), rooted in uneven reporting quality over time, are exacerbated by technological and institutional advancements affecting data availability 1, 26. Especially before 2000, the content of disaster loss databases was influenced by mass media, the internet as other information technology, and disaster management and monitoring initiatives, including the EM-DAT establishment in 1988. The time bias makes it difficult to infer insights about the evolution of disaster risks from long-term trend analysis of EM-DAT data alone. 

Hazard-related biases highlight disparities in data coverage for different hazard types. Specifically, EM-DAT shows less consistent monitoring for epidemics and temperature extremes. Pandemics have always been excluded from EM-DAT. 

Regarding epidemics, the EM-DAT inclusion criteria, e. g., at least ten deaths or 100 affected, are often too low and impractical for monitoring. Therefore, the epidemics are currently, in most cases, included based on the third EM-DAT inclusion criterion, i. e., an emergency situation. Extreme temperature events, such as heat waves, also exhibit biases in EM-DAT 52, 53 (Fig. 5b ). 

The impact of heat waves is often expressed in terms of excess mortality, a challenging estimation in countries without civil registrations of deaths 34 or where less research is conducted 54. Given the unavailability of information, EM-DAT heatwave records may not fully reflect the actual situation. 

Threshold biases refer to low-impact disasters being more likely to go unnoticed. However, their cumulative impact, if recorded, can be significant 55. Threshold biases are inevitably reinforced through EM-DAT entry criteria. In Fig. 5c, the cumulative mortality impact of disasters with less than ten deaths is not null because some disasters are included based on other criteria. Yet, this cumulative impact overlooks the actual situation, as evidenced by the sharp difference in the cumulative mortality impact of disasters with 10 to 19 victims. Geographic biases result from spatial inconsistencies in data coverage, with areas like Sub-Saharan Africa often being underrepresented 56, as is the case with heatwave events (Fig. 5b ). Discrepancies in reporting systems between countries further exacerbate these biases. Countries investing in disaster loss monitoring systems or with substantial international media coverage will likely be better represented in EM-DAT. In addition, EM-DAT may also exhibit distance or language biases since such biases have been studied and reported for other databases 57. 

Finally, accounting biases refer to inconsistent reporting across different impacts. Fig. 5d illustrates the better coverage of mortality in EM-DAT compared to other losses, in particular, the economic damage, which is only specified for about 20% of EM-DAT entries. Generally, less tangible and accountable losses tend to be underestimated. For instance, indirect and long-term impacts, such as disease outbreaks or excess mortality linked to post-disaster sanitary conditions, can be overlooked in EM-DAT since it is the case in the literature 58. Similarly, insured damage is better covered than non-insured or indirect economic losses. 

Beyond specific examples (e. g., Fig. 5 ), a comprehensive quality assessment across all periods, regions, hazards, or impacts is still lacking, given the general absence of independent, ground truth datasets within the EM-DAT monitoring domain. ] [29]. In particular, variations in data availability and quality should not be mistaken for actual disaster risk patterns. Similar to EM-DAT content, disaster data collection has progressed and will keep progressing in the near future, e. g., through higher integration of spatial data, the advent of monitoring systems supported by artificial intelligence, or, generally, increased societal commitment to disaster monitoring. (Table 2 ). Each source group, denoted by a unique symbol and color, corresponds to the categories used in Table 1. The bottom sub-panels detail the frequency at which sources from the top-20 list (Table 1 ) document (blank bar) or are being selected (filled bar) to contribute to EM-DAT published impact variables: (b) Total Deaths, (c) Total Affected, and (d) Total Damage. The percentage (x-axis) of documentation or selection is calculated relative to the 'N°of Entries' column in Table 1. The color used for the filled bar maps to the source group colors used in the top-panel panel a.
section_name: Technical Validation

section_name: Figures & Tables

section_name: Acknowledgements

section_content: The EM-DAT project, within the Université catholique de Louvain (UCLouvain), receives funding from USAID/BHA [ref no. 72OFDA20CA00072 ].
section_name: Acknowledgements

section_name: Code availability

section_content: The EM-DAT data is encoded and supervised manually (see Methods) within a web-based private data entry interface developed for that purpose. The Scopus and EM-DAT data for this publication were read and processed using Python programming libraries, including SciPy 59, pybibliometrics 60, pandas and geopandas [61] [62] [63], and matplotlib 64. EM-DAT users who want to track changes in EM-DAT automatically can also replicate or develop their custom automated validation protocol using pandera 65. 

The Python code used in this paper is available upon request.
section_name: Code availability

section_name: Usage Notes

section_content: For up-to-date usage notes and additional assistance on the use of the data, we refer the reader to the EM-DAT Documentation website 48.
section_name: Usage Notes

section_content: All authors have made substantial contributions either to the writing of the data descriptor or to prior work associated with the revision of the database and the publication of its documentation website. DD penned the initial drafts of the EM-DAT data descriptor and documentation website, configured and adapted the front-end side of the documentation website, and created the figures and tables for the paper. VW oversees the continuous management of the EM-DAT IT infrastructure, developed the latest version of the EM-DAT back-end and database model, the EM-DAT public data portal, and reviewed the documentation website and the data descriptor. RB has been managing the EM-DAT data content since 1992, contributed to writing the documentation website, and reviewed the data descriptor. MT and CLS reviewed the documentation website, tested it along with the new EM-DAT public portal for feedback and debugging, and reviewed the data descriptor. JVL and NS reviewed and proofread the data descriptor, and NS supervised the EM-DAT updating process and data descriptor initiative.
section_name: Author contributions statement

section_content: The authors declare no competing interests. List of secondary disaster types cascading from or co-occurring aside from the main type, e. g., a landslide following a flood or an explosion after an earthquake.
section_name: Competing interests

section_content: Binary field specifying whether or not the Office of US Foreign Disaster Assistance (OFDA) responded to the disaster. Appeal Binary field specifying whether or not there was a request for international assistance from the affected country.
section_name: OFDA Response

section_content: Binary field specifying whether a state of emergency was declared in the country.
section_name: Declaration

section_content: The total amount (in thousands of US$ at the time of the report) of contributions for immediate relief activities to the country in response to the disaster, sourced from the Financial Tracking System of OCHA (1992 to 2015). Not maintained after 2015.
section_name: AID Contribution

section_content: A value related to the intensity of a hazard, depending on the disaster type.
section_name: Magnitude

section_content: The associated unit for the Magnitude column. Latitude North-South coordinates mainly for earthquakes and volcanic activity. Sometimes reported for floods, landslides, and storms (mostly when associated with floods). Longitude East-West coordinates mainly for earthquakes and volcanic activity. Sometimes reported for floods, landslides, and storms (mostly when associated with floods 3 Costs for replacement of lost assets in thousands of US dollars ('000 US$). Insured Damage ('000 US$) 3 Economic damage covered by insurance companies, in thousands of US dollars ('000 US$). Total Damage ('000 US$) 3 Value of all economic losses directly or indirectly due to the disaster, in thousands of US dollars ('000 US$). CPI Consumer Price Index from OECD used to adjust US$ values for inflation relative to Start Year. Admin Units Collection of impacted Administrative Units from the FAO GAUL 2015 referential. Individual objects correspond to Level-1 or Level-2 Administrative Units. Geocoding is maintained for non-biological natural hazards from 2000 onwards.
section_name: Magnitude Scale

section_content: The day on which the event record was created in EM-DAT.
section_name: Entry Date

section_content: The last date of modification of the event or one of its associated records in EM-DAT. 1 See the EM-DAT Documentation for an up-to-date and more detailed description 38. 2 Multiple columns in EM-DAT, grouped here for legibility. 3 EM-DAT also include CPI-adjusted monetary values, ignored here for legibility.
section_name: Last Update