Natural Disasters Deaths

People killed in natural disasters by country by year

About this dataset

How much do natural disasters cost us? In lives, in dollars, in infrastructure? This dataset attempts to answer those questions, tracking the death toll and damage cost of major natural disasters since 1985. Disasters included are storms ( hurricanes, typhoons, and cyclones ), floods, earthquakes, droughts, wildfires, and extreme temperatures

How to use the dataset

This dataset contains information on natural disasters that have occurred around the world from 1900 to 2017. The data includes the date of the disaster, the location, the type of disaster, the number of people killed, and the estimated cost in US dollars

Research Ideas

• An all-in-one disaster map displaying all recorded natural disasters dating back to 1900.
• Natural disaster hotspots - where do natural disasters most commonly occur and kill the most people?
• A live map tracking current natural disasters around the world

Acknowledgements

License

See the dataset description for more information.

In 2023, there was a total of *** natural disasters events recorded worldwide, down from *** recorded a year earlier. The Europe, Middle East and Africa region experienced the highest number of natural disasters that year. Deaths and costs of natural disasters Natural disasters affect almost every part of the world. In February 2023, Turkey and Syria were hit by earthquakes that resulted in the highest number of deaths due to natural disaster events that year. In terms of economic damage, Hurricane Katrina remains one of the most expensive natural disasters in the world, topped only by the earthquake/tsunami which hit Japan in 2011. Climate change and natural disasters Climate change has influenced the prevalence of natural disasters. Global warming can increase the risk of extreme weather, resulting in higher risk of droughts and stronger storms, such as tropical cyclones. For instance, higher levels of water vapor in the atmosphere give storms the power to emerge. Furthermore, the heat in the atmosphere and high ocean surface temperatures lead to increased wind speeds, which characterize tropical storms. Areas that are usually unaffected by the sea are becoming more vulnerable due to rising sea levels as waves and currents become stronger.

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.

Database Disaster Recovery Orchestration Market Outlook

According to our latest research, the global Database Disaster Recovery Orchestration market size in 2024 stands at USD 2.4 billion, reflecting a robust demand for advanced disaster recovery solutions across industries. The market is projected to grow at a CAGR of 12.8% from 2025 to 2033, reaching a forecasted value of USD 7.1 billion by 2033. This impressive growth is primarily driven by the increasing frequency of cyberattacks, the growing reliance on data-driven operations, and the stringent regulatory requirements for data protection and business continuity. As organizations continue to digitize their operations and adopt hybrid IT environments, the need for efficient and automated disaster recovery orchestration has become paramount, fueling the rapid expansion of this market.

One of the most significant growth drivers for the Database Disaster Recovery Orchestration market is the escalating complexity and volume of enterprise data. As businesses generate and store massive amounts of critical data, the risk of data loss due to system failures, cyber threats, or natural disasters has increased exponentially. This has compelled organizations to invest in sophisticated disaster recovery orchestration solutions that can automate and streamline recovery processes, minimize downtime, and ensure data integrity. The adoption of cloud-based platforms and hybrid IT environments has further amplified the need for robust disaster recovery strategies, as organizations seek to balance agility with resilience. Additionally, the proliferation of remote work and digital transformation initiatives has underscored the importance of uninterrupted data access and business continuity, driving sustained demand for advanced orchestration tools.

Another key factor propelling the growth of the Database Disaster Recovery Orchestration market is the evolving regulatory landscape. Governments and industry bodies worldwide are imposing stricter data protection and business continuity mandates, particularly in highly regulated sectors such as banking, healthcare, and government. Compliance with standards such as GDPR, HIPAA, and PCI DSS necessitates the implementation of comprehensive disaster recovery plans and automated orchestration solutions that can ensure rapid recovery and auditability. Organizations are increasingly recognizing the reputational and financial risks associated with data breaches and prolonged downtime, prompting them to prioritize investments in disaster recovery orchestration technologies. This regulatory impetus is fostering a culture of proactive risk management and resilience, further accelerating market growth.

Technological advancements and innovation in disaster recovery orchestration are also contributing to the market's expansion. Vendors are increasingly leveraging artificial intelligence, machine learning, and automation to enhance the efficiency, accuracy, and scalability of disaster recovery processes. Modern orchestration platforms offer features such as predictive analytics, automated failover, real-time monitoring, and policy-driven recovery workflows, enabling organizations to achieve faster recovery times and reduce operational overhead. The integration of orchestration solutions with broader IT management and cybersecurity frameworks is also gaining traction, providing organizations with holistic visibility and control over their data protection strategies. These technological trends are making disaster recovery orchestration more accessible, cost-effective, and adaptable to diverse business needs, further driving adoption.

From a regional perspective, North America currently dominates the Database Disaster Recovery Orchestration market, accounting for the largest share of global revenues in 2024. The region's leadership can be attributed to the high concentration of technology-driven enterprises, widespread adoption of cloud computing, and strong regulatory frameworks. However, Asia Pacific is emerging as the fastest-growing market, fueled by rapid digitalization, increasing cyber threats, and rising investments in IT infrastructure across countries such as China, India, and Japan. Europe also represents a significant market, driven by stringent data protection regulations and the growing focus on business continuity among enterprises. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by improving digital infrastructure and increasing awarenes

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.

Please note that the following information along with the dataset is taken from http://www.fao.org/faostat/en/#data/QC From the Food and Agriculture Organization of the United Nations (FAO)

Context

Crop statistics are recorded for 173 products, covering the following categories: Crops Primary, Fibre Crops Primary, Cereals, Coarse Grain, Citrus Fruit, Fruit, Jute Jute-like Fibres, Oilcakes Equivalent, Oil crops Primary, Pulses, Roots and Tubers, Tree nuts and Vegetables and Melons. Data are expressed in terms of area harvested, production quantity, and yield. The objective is to comprehensively cover the production of all primary crops for all countries and regions in the world. Cereals: Area and production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage or used for grazing are therefore excluded. Area data relate to harvested area. Some countries report sown or cultivated area only

If FAO is to carry out its work successfully it will need to know where and why hunger and malnutrition exist, what forms they take, and how widespread they are. Such data will serve as a basis for making plans, determining the efficacy of measures used, and measuring progress from time to time.

Content

Statistical concepts and definitions - Areas refer to the area under cultivation. Area under cultivation means the area that corresponds to the total sown area, but after the harvest it excludes ruined areas (e.g. due to natural disasters). If the same land parcel is used twice in the same year, the area of this parcel can be counted twice. For tree crops, some countries provide data in terms of number of trees instead of in area. This number is then converted to an area estimate using typical planting density conversions. Production means the harvested production. Harvested production means production including on-holding losses and wastage, quantities consumed directly on the farm and marketed quantities, indicated in units of basic product weight. Harvest year means the calendar year in which the harvest begins. Yield means the harvested production per ha for the area under cultivation. Seed quantity comprises all amounts of the commodity in question used during the reference period for reproductive purposes, such as seed or seedlings. Whenever official data are not available, seed figures can be estimated either as a percentage of production or by multiplying a seed rate (the average amount of seed needed per hectare planted) with the planted area of the particular crop of the subsequent year. Usually, the average seed rate in any given country does not vary greatly from year to year.

Statistical unit: Agriculture holdings cultivated for the production of crops.

Statistical population: All areas cultivated with crops in a country.

Reference area: All countries of the world and geographical aggregates according to the United Nations M-49 list.

Time coverage: 1961-2018 (up to 2017 for all elements computed from FBS framework, e.g. seed, derived/processed commodities)

Periodicity: Annual

Pelase note that the information on flags and units used can be found along with the dataset.

Acknowledgements

Food and Agriculture Organization of the United Nations (FAO), Statistics Division (ESS), Environment Statistics team, Mr. Salar Tayyib. Source - http://www.fao.org/faostat/en/#data/QC/metadata

Inspiration

Initially found this dataset when I was working on a school project regarding the crops most popular in spain. Found this extremely useful in determining the most popular crops and visualizing the same.

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.

The GPM Ground Validation Global Flood Monitoring System (GFMS) Flood Maps IFloodS dataset contains global flood estimates on a 0.25 degree spatial resolution every 3 hours, from March 26, 2013 through June 30, 2013. These data are provided in support of the Iowa Flood Studies (IFloodS) experiment conducted in eastern Iowa. The goals of the IFloodS campaign were to collect detailed measurements of precipitation at the Earth’s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. The data are available in netCDF-4 and ASCII formats. Flood map and rain graph files are available in KMZ, JPG, and GIF formats.

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.

This dataset presents results of current and future riverine flood impact data for NUTS3 regions in Europe. The dataset has been developed following the methodology presented in Tiggeloven et al. (2020) and Mortensen et al. (In Review).

This dataset can be used to as direct input for the DIFI model as described in Tesselaar et al. (2023).

References:

Mortensen, E., Tiggeloven, T., Haer, T., van Bemmel, B., Bouwman, A., Ligtvoet, W., & Ward, P.J.: The potential for various riverine flood DRR measures at the global scale. Journal of Coastal and Riverine Flood Risk, In Review.

Tesselaar, M., Botzen, W.J.W., Aerts, J.C.J.H., Tiggeloven, T. (2023). Flood insurance is a driver of population growth in European floodplains. Nature Communications (provisionally accepted)

Tiggeloven, T., De Moel, H., Winsemius, H. C., Eilander, D., Erkens, G., Gebremedhin, E., ... & Ward, P. J. (2020). Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures. Natural Hazards and Earth System Sciences, 20(4), 1025-1044.

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.

⚠️ Data layers are restricted and require permission from the data owner.
Please contact us or the dataset's contact point to request access.

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.

⚠️ Data layers are restricted and require permission from the data owner.
Please contact us or the dataset's contact point to request access.

Total-Other-Finance-Cost Time Series for American International Group Inc. American International Group, Inc. offers insurance products for commercial, institutional, and individual customers in North America and internationally. It operates through three segments: North America Commercial; International Commercial; and Global Personal. The company provides commercial and industrial property insurance, including business interruption and package insurance that cover exposure to made and natural disasters; general liability, environmental, commercial automobile liability, workers' compensation, excess casualty, and crisis management insurance products; and professional liability insurance. It also offers marine, energy-related property insurance, aviation, political risk, trade credit, trade finance, and portfolio solutions; voluntary and sponsor-paid personal accident, and supplemental health products; and personal auto and homeowners, extended warranty, device protection insurance, home warranty and related services, and insurance for high net-worth individuals. Further, the company provides mortgage and other loans receivable includes commercial mortgages, life insurance policy loans, and commercial loans, The company was founded in 1919 and is headquartered in New York, New York.

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.

Climate-Adjusted Replacement Cost Platforms Market Outlook

According to our latest research, the global Climate-Adjusted Replacement Cost Platforms market size reached USD 2.4 billion in 2024, reflecting the rapid adoption of advanced risk assessment solutions across industries. The market is experiencing significant momentum, with a robust CAGR of 16.7% projected during the forecast period from 2025 to 2033. By 2033, the market is anticipated to achieve a valuation of USD 10.7 billion, driven by increasing climate risk awareness, regulatory pressures, and the critical need for accurate asset valuation in the face of environmental volatility. These factors are propelling organizations to invest in platforms that integrate climate data into replacement cost calculations, ensuring resilience and financial prudence.

The primary growth driver for the Climate-Adjusted Replacement Cost Platforms market is the escalating frequency and severity of climate-related disasters globally. As natural catastrophes such as floods, hurricanes, wildfires, and extreme weather events become more prevalent, organizations are compelled to reassess their risk management strategies. Traditional replacement cost models, which often overlook the dynamic impact of climate change, are proving inadequate. In response, businesses and governments are turning to climate-adjusted platforms that utilize advanced analytics, geospatial data, and machine learning to deliver precise, real-time asset valuations. This shift is not only enhancing operational resilience but also ensuring compliance with evolving regulatory standards that demand comprehensive climate risk disclosures.

Another significant growth factor is the increasing adoption of digital transformation initiatives across insurance, real estate, construction, and government sectors. As these industries digitize their operations, the demand for integrated, automated, and scalable platforms capable of processing vast datasets is rising sharply. Climate-adjusted replacement cost platforms provide the technological backbone for such transformation, offering seamless integration with enterprise resource planning (ERP) systems, claims management software, and property databases. The ability to continuously update replacement values based on the latest climate projections and local risk factors is becoming indispensable for insurers in underwriting, for property managers in asset planning, and for governments in infrastructure resilience planning.

Furthermore, the growing emphasis on sustainability and environmental, social, and governance (ESG) criteria is accelerating the deployment of climate-adjusted replacement cost platforms. Investors and stakeholders are increasingly scrutinizing organizations’ climate risk exposure and mitigation strategies. Platforms that can quantify the financial impact of climate change on physical assets are emerging as critical tools for ESG reporting and sustainable investment decision-making. This trend is particularly pronounced among financial institutions and large corporates, which are leveraging these platforms to align their asset management and investment portfolios with global climate goals and risk tolerance thresholds.

From a regional perspective, North America currently dominates the Climate-Adjusted Replacement Cost Platforms market, accounting for approximately 38% of the global revenue in 2024. This leadership is attributed to advanced regulatory frameworks, high insurance penetration, and the presence of leading technology vendors. Europe follows closely, driven by stringent climate disclosure mandates and robust public-private partnerships in climate resilience. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, increasing climate vulnerability, and proactive government initiatives to modernize disaster risk management. Latin America and the Middle East & Africa, though at earlier stages of adoption, are expected to demonstrate accelerated growth as climate risks intensify and digital infrastructure improves.

EO Synthetic Data for Model Training Market Outlook

According to our latest research, the global EO Synthetic Data for Model Training market size is valued at USD 1.26 billion in 2024, with a robust compound annual growth rate (CAGR) of 34.7% projected from 2025 to 2033. By the end of 2033, the market is expected to reach USD 16.38 billion, driven by advancements in artificial intelligence, the increasing need for high-quality training datasets, and growing adoption across industries such as defense, agriculture, and urban planning. The market’s exponential growth is underpinned by the demand for scalable, diverse, and privacy-compliant data solutions that enhance the performance and reliability of machine learning models, especially in geospatial and remote sensing domains.

One of the primary growth factors fueling the EO Synthetic Data for Model Training market is the rapid proliferation of artificial intelligence and machine learning applications in sectors requiring geospatial analysis. As organizations increasingly rely on AI-driven insights for decision-making, the need for vast, accurately labeled, and diverse datasets has surged. Traditional data collection methods, particularly for earth observation (EO) imagery, are often hampered by high costs, privacy concerns, and limited accessibility, especially in sensitive or hard-to-reach regions. Synthetic data generation addresses these challenges by producing high-fidelity, customizable datasets that can be tailored to specific use cases, substantially accelerating model development cycles while reducing dependency on real-world data acquisition. This paradigm shift is particularly evident in industries such as defense & intelligence, agriculture, and environmental monitoring, where timely and accurate data is crucial for operational success.

Another significant growth driver is the advancement in simulation technologies and generative models, such as Generative Adversarial Networks (GANs) and physics-based rendering engines. These technologies have matured to a point where synthetic EO data can closely mimic real-world conditions, including variations in lighting, weather, terrain, and sensor characteristics. This realism is critical for training robust machine learning models capable of generalizing to real-world scenarios. Furthermore, the integration of multispectral and hyperspectral data in synthetic datasets enables more comprehensive analysis, supporting applications ranging from crop health assessment to disaster response. The ability to generate rare or underrepresented scenarios, such as natural disasters or military activities, further enhances the value proposition of synthetic data, making it an indispensable tool for risk assessment, planning, and simulation.

The increasing regulatory emphasis on data privacy and security is also propelling the adoption of EO synthetic data for model training. With stringent data protection laws such as GDPR and CCPA, organizations face mounting challenges in utilizing real-world EO data that may contain sensitive information. Synthetic data offers a privacy-preserving alternative, as it is artificially generated and inherently anonymized, thereby mitigating compliance risks. This aspect is especially relevant for government agencies and commercial enterprises operating in regions with strict data governance frameworks. Moreover, synthetic data enables the democratization of AI by providing smaller organizations and research institutions with access to high-quality datasets, leveling the playing field and fostering innovation across the ecosystem.

From a regional perspective, North America currently leads the EO Synthetic Data for Model Training market, accounting for approximately 41% of the global revenue in 2024, followed by Europe and Asia Pacific. This dominance is attributed to the presence of major technology companies, robust research infrastructure, and significant investments in defense and intelligence applications. Europe is witnessing rapid adoption driven by environmental monitoring and smart city initiatives, while the Asia Pacific region is emerging as a high-growth market due to expanding satellite programs and increased focus on agricultural modernization. Latin America and the Middle East & Africa, though currently smaller in market share, are expected to register above-average growth rates as awareness and technological capabilities improve.

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.

The expected economic impact due to natural hazards is illustrated through an average annual loss (AAL) map, which indicates the estimated economic losses averaged over the 10,000 realizations of next-year activity. Economic loss is defined as the total direct ground-up losses, i.e., the cost needed to repair or replace damaged assets. Three types of assets were considered: (1) buildings (e.g., residential, commercial, industrial, and public buildings) - (2) major infrastructure (airports, ports, power plants, bridges, dams, etc.) - and (3) valuable crops (sugarcane, taro, rice, banana, etc.). Two types of natural events were explicitly considered in this risk analysis: earthquakes (inducing both ground shaking and tsunami waves) and tropical cyclones (inducing wind, precipitation/flood, and coastal flooding due to surge of the sea level). The resolution is taken at a specified administration boundary for each country. Compiled by AIR Worldwide.