Hurricane Ian, which hit Cuba in September 2022, caused an economic loss of more than ** billion Cuban pesos, making it the costliest hurricane to hit the country in the period since 2007. Hurricane Irma, in September 2017, ranked second, with a total loss of **** billion pesos, followed by hurricane Ike (September 2008), with losses adding up to some *** billion pesos.

Hurricane Katrina destroyed over 200,000 homes and led to massive economic and physical dislocation. Using a panel of tax return data, we provide one of the first comprehensive analyses of the hurricane's long-term economic impact on its victims. Hurricane Katrina had large and persistent impacts on where people live, but small and surprisingly transitory effects on employment and income. Within just a few years, Katrina victims' incomes actually surpass that of controls from similar unaffected cities. The strong economic performance of Hurricane Katrina victims is particularly remarkable given that the hurricane struck with essentially no warning.

Code supporting Strauss et al. (2020) submitted to Nature Communications. If you use any original data from this archive, please cite the study as:

B. H. Strauss, P. Orton, K. Bittermann, M. K. Buchanan, D. M. Gilford, R. E. Kopp, S. Kulp, C. Massey, H. de Moel, S. Vinogradov, 2020: Economic Damages from Hurricane Sandy Attributable to Sea Level Rise Caused by Anthropogenic Climate Change. Nature Communications. (under review, Dec. 2020)

If you have any questions or comments, please contact Daniel Gilford at dgilford@climatecentral.org

Included are Input, Output, and Source files (compressed) used in the publication; data files are primarily in txt, csv, xlsx, and mat formats. In the absence of a MATLAB license, mat files may be read with open access software such as SciPy. Code supporting this publication may be found at https://github.com/climatecentral/cc_sandy_matlab.

Archived Data Short Descriptions:

INPUT -- Input semi-empirical model, hydrodynamic, and observational data files used to create distributions/analyses in this study.

8518750_meantrend.csv: The Battery, NY monthly mean sea levels and trends/uncertainty, accessed from https://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?id=8518750 on 29 July 2020.

cmip5.zip: CMIP5 semi-empirical model analyses for each individual model and scenarios (historical and counterfactual), and index files for reference.

hadcrut.zip: HadCRUT4 semi-empirical model analyses for each individual HadCRUT4 scenario (historical and counterfactuals)

Dangendorf2019_GMSL.txt: Monthly mean global mean sea level rise from Dangendorf et al. (2019).

Also included are datum information, block damages (/damage/ directory), hydrodynamic simulations (/simulations_july_2016/ directory), and additional auxiliary files required to run the accompanying repository analyses.

OUTPUT -- Code outputs supporting this publication

fig1_data.mat: Quick access source data file which may be used to recreate Fig. 1 in the manuscript

SEanalysis.mat: The full output semi-empirical model analyses in this study

summary_samps.mat: Summary/ensemble analyses in this study

SOURCE -- Individual source data files for each Figure (1, 2, 3a-b), Table (1-2), Supplementary Figure (S1-4), and Supplementary Table (S1-6) in this study.

Included is a readme.txt with full descriptions of source data files.

We acknowledge funding from NSF grant ICER-1663807, NASA grant 80NSSC17K0698,

The increasing losses from tropical cyclones in developing countries highlight the importance of understanding how natural habitats can be used to protect assets and economic activity against this hazard. Here, we estimate the relationship between hurricane strength and economic damages in Central America and explore how the presence of mangrove habitats mitigate these losses. We find that hurricanes lead to significant losses in economic activity in the short run, and that wide mangrove belts are capable of mitigating these losses. One important implication of these findings is that only large-scale mangrove conservation efforts are likely to provide a benefit in terms of protection.

Three of the ten costliest tropical cyclones recorded since 1900 occurred in 2017. That year, cyclones Harvey and Irma, which hit the U.S., and Maria, which hit Puerto Rico, resulted combined in roughly *** billion U.S. dollars worth of economic damage. Hurricane Katrina was the largest tropical cyclone recorded in terms of economic losses, at over *** billion dollars.

Hurricane Insurance Market Outlook

According to our latest research, the global hurricane insurance market size in 2024 is valued at USD 23.8 billion, reflecting the increasing severity and frequency of hurricanes worldwide. The market is experiencing a robust growth trajectory, driven by heightened awareness of climate risks and the need for comprehensive risk mitigation strategies. The market is projected to expand at a CAGR of 7.3% from 2025 to 2033, reaching an estimated USD 45.1 billion by 2033. This significant growth is fueled by the rising economic impact of hurricanes, advancements in insurance products, and expanding adoption across both residential and commercial sectors, as per our latest research.

One of the primary growth factors driving the hurricane insurance market is the escalating frequency and intensity of hurricanes, particularly in regions such as North America and the Asia Pacific. Climate change has led to more unpredictable and severe weather patterns, causing substantial property damage and economic losses. As a result, individuals, businesses, and governments are increasingly seeking robust insurance solutions to safeguard assets and ensure financial resilience. The rising number of catastrophic events has also prompted regulatory authorities to mandate insurance coverage in high-risk areas, further propelling market demand. This shift is particularly evident in coastal regions where the threat of hurricanes is most pronounced, prompting a surge in policy uptake and premium volumes.

Another key driver of market expansion is the continuous evolution of insurance products and services tailored to diverse customer needs. Insurers are leveraging advanced technologies such as artificial intelligence, big data analytics, and geospatial mapping to assess risks more accurately and design customized policies. This has enabled the introduction of innovative coverage options, including parametric insurance and comprehensive packages that address not only property damage but also business interruption and loss of use. The integration of digital platforms has streamlined the policy acquisition and claims management processes, enhancing customer experience and operational efficiency. These advancements have made hurricane insurance more accessible and attractive to a broader customer base, including small businesses and renters who were previously underserved.

Government initiatives and public-private partnerships are also playing a pivotal role in market growth. In regions with high hurricane exposure, governments are collaborating with insurance providers to develop risk-sharing mechanisms, such as catastrophe bonds and reinsurance pools. These initiatives aim to stabilize insurance markets, lower premium costs, and expand coverage to vulnerable populations. Additionally, educational campaigns and incentives for disaster preparedness are encouraging proactive adoption of hurricane insurance. The synergy between public policy and private sector innovation is fostering a resilient insurance ecosystem capable of absorbing and distributing hurricane-related risks more effectively, thereby supporting long-term market sustainability.

From a regional perspective, North America dominates the hurricane insurance market, accounting for the largest share due to its high exposure to Atlantic hurricanes and robust insurance infrastructure. The United States, in particular, has witnessed increased policy adoption in hurricane-prone states such as Florida, Texas, and Louisiana. Europe and the Asia Pacific are also emerging as significant markets, driven by rising awareness and regulatory developments. Latin America and the Middle East & Africa, while currently smaller in market size, are expected to witness accelerated growth as governments and businesses recognize the need for comprehensive disaster risk management solutions. The regional landscape is characterized by varying levels of market maturity, regulatory frameworks, and consumer awareness, shaping distinct growth trajectories across geographies.

Hurricane Risk Modeling has become an indispensable tool in the insurance industry, particularly in the realm of hurricane insurance. By utilizing sophisticated algorithms and data analytics, insurers can predict the potential impact of hurricanes with greater accuracy. This modeling not only helps in a

Hurricane Risk Modeling Market Outlook

According to our latest research, the global hurricane risk modeling market size reached USD 1.47 billion in 2024 and is projected to grow at a CAGR of 13.8% from 2025 to 2033, culminating in a forecasted market size of USD 4.47 billion by 2033. This robust expansion is primarily driven by the increasing frequency and severity of hurricanes due to climate change, compelling governments, insurers, and businesses to invest in advanced risk modeling solutions to improve preparedness, underwriting, and mitigation strategies.

One of the most significant growth factors for the hurricane risk modeling market is the escalating impact of climate change, which has led to more frequent and intense hurricanes across major coastal regions. As the economic and human costs of these natural disasters rise, there is an urgent demand for sophisticated hurricane risk modeling tools that can provide accurate risk assessments and actionable insights. These models are crucial for insurance and reinsurance companies to price premiums accurately, for disaster management agencies to plan effective response strategies, and for urban planners to develop resilient infrastructure. The integration of advanced technologies such as artificial intelligence, machine learning, and big data analytics has further enhanced the predictive capabilities of these models, making them indispensable in todayÂ’s risk-prone environment. As a result, organizations are increasingly adopting hurricane risk modeling solutions to safeguard assets, ensure business continuity, and comply with evolving regulatory requirements.

Another key driver propelling the hurricane risk modeling market is the growing emphasis on regulatory compliance and risk management across various industries. Governments and regulatory bodies worldwide are mandating stricter risk assessment protocols, especially for financial institutions and insurance companies operating in hurricane-prone regions. This regulatory push has heightened the need for transparent, scientifically validated, and auditable risk models that can withstand scrutiny from both regulators and stakeholders. Furthermore, the rise of parametric insurance products, which rely on precise and rapid event modeling, has created new opportunities for hurricane risk modeling vendors. The increasing adoption of cloud-based platforms has also made these solutions more accessible and scalable, enabling small and medium enterprises as well as large organizations to leverage state-of-the-art risk modeling without significant upfront investments in IT infrastructure.

Technological advancements are also playing a pivotal role in shaping the hurricane risk modeling market landscape. The integration of high-resolution satellite imagery, IoT-enabled sensors, and geospatial data has dramatically improved the granularity and accuracy of hurricane risk models. These technological innovations not only enable real-time monitoring and forecasting but also facilitate scenario analysis and loss estimation with unprecedented precision. As urbanization accelerates in coastal areas, there is a growing need for customized and localized risk models that can account for unique geographical, structural, and socio-economic factors. This trend is fostering collaboration between technology providers, academic institutions, and government agencies to develop next-generation hurricane risk modeling frameworks that are both robust and adaptable. The increasing availability of open-source data and collaborative modeling platforms is further democratizing access to advanced risk modeling tools, driving market growth across developed and emerging economies.

The role of Insurance Catastrophe Modeling AI in the hurricane risk modeling market cannot be overstated. As hurricanes become more frequent and severe, insurers are increasingly turning to AI-driven catastrophe models to enhance their risk assessment capabilities. These advanced models leverage machine learning algorithms and vast datasets to simulate potential hurricane scenarios with unprecedented accuracy. By incorporating real-time data and predictive analytics, Insurance Catastrophe Modeling AI enables insurers to anticipate losses, optimize underwriting processes, and develop more resilient insurance products. This technological advancement is not only transforming the insurance landscape but also providing criti

This data set includes the raw data for the figures of the article "Wave-like global economic ripple response to Hurricane Sandy" (https://doi.org/10.1088/1748-9326/ac39c0).

Hurricane Protection Products Sales Market Outlook

The global hurricane protection products market size was valued at approximately USD 12.5 billion in 2023. It is anticipated to reach around USD 20.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.5% during the forecast period. This growth is largely driven by the increasing frequency and intensity of hurricanes across the globe, which has heightened the demand for protective measures across residential, commercial, and industrial sectors. The need for safety and security, coupled with advancements in technology, is propelling the demand for these products. As communities and businesses are increasingly recognizing the cost benefits of proactive hurricane protection versus post-disaster rebuilding, the market is expected to see sustained growth.

A key growth factor for the hurricane protection products market is the rising awareness about climate change and its resultant extreme weather patterns. Over the past decade, there has been a notable increase in the number and severity of hurricanes, particularly in regions such as the North Atlantic and the Gulf of Mexico. This has led to a surge in demand from both individual homeowners and commercial enterprises seeking to protect their properties from potential damage. Furthermore, with the advent of sophisticated forecasting technologies, there is greater awareness and readiness, thus boosting the sales of hurricane protection products as people and businesses prepare in advance for possible disasters.

The technological progression in material science is another significant growth factor for the market. Innovations in materials such as impact-resistant glass and high-strength polycarbonate have led to the development of more effective and aesthetically pleasing hurricane protection products. These materials not only offer superior protection against strong winds and flying debris but also enhance the architectural integrity of buildings. As a result, they are becoming increasingly popular among consumers who are looking for both safety and style. Additionally, the ease of installation and improved durability of these advanced materials make them a preferred choice over traditional options, further fueling market growth.

The increasing governmental regulations and building codes mandating the use of hurricane protection products in hurricane-prone areas have also contributed significantly to the market expansion. Many countries have implemented strict regulatory frameworks requiring the installation of hurricane protection solutions in newly constructed or renovated buildings. These regulations are aimed at minimizing the economic and human toll of hurricanes. Consequently, compliance with these codes has become a driving factor for product adoption across various sectors, thereby supporting market growth.

Geographically, North America holds a significant share of the hurricane protection products market, primarily due to the high incidence of hurricanes in the region, particularly affecting the United States. The stringent building codes and the proactive stance of homeowners and businesses towards disaster management are key contributors to this market dominance. However, emerging economies in the Asia Pacific region are also witnessing a growing demand for hurricane protection products. This can be attributed to increased urbanization, rising disposable incomes, and a growing awareness about the importance of disaster preparedness, which are expected to drive the regional market during the forecast period.

Product Type Analysis

Hurricane shutters are a predominant segment in the hurricane protection products market due to their cost-effectiveness and proven efficacy in protecting windows and doors from high winds and flying debris. They are available in various styles, including roll-down, accordion, Bahama, and colonial shutters, each offering unique aesthetic and functional benefits. The versatility and relatively easy installation process make hurricane shutters a popular choice among homeowners and businesses alike. Furthermore, advancements in shutter technology, such as automated systems that can be controlled remotely, are enhancing their appeal, particularly in tech-savvy markets.

Impact-resistant windows and doors are experiencing growing popularity as they offer a dual benefit of protection and aesthetic appeal. These products are designed to withstand extreme wind pressures and impacts from debris, while still allowing natural light into a building. They are often used in locations where aesthetic considerations are paramount, such

Economic losses caused by tropical cyclones in the Caribbean have considerably increased in the past decades. From 2010 to 2019, the mean economic loss and damage by this type of storms stood at approximately *** billion U.S. dollars, a more than ********* increase compared to the previous decade. The increase was mainly caused by ***** category **** hurricanes in that period.

Weather Disaster Costs and Deaths

Costs and Deaths of Billion Dollar Weather Disasters in the US

By Throwback Thursday [source]

About this dataset

The Billion Dollar Weather Disasters in the US dataset is a valuable resource containing comprehensive historical data on weather events in the United States that have caused billions of dollars in damages and resulted in loss of lives. It provides insights into various types and categories of weather disasters, such as hurricanes, tornadoes, floods, wildfires, and more.

The dataset includes essential information about each weather disaster event, starting with its name or title referred to as Disaster. A brief summary or description of each event is provided under the column Description, giving readers an understanding of its impact and extent. Furthermore, the dataset categorizes each disaster based on its type under the column Disaster Type. This classification helps researchers and analysts to identify patterns or common characteristics among similar types of weather disasters.

One crucial aspect covered by this dataset is the economic impact of these severe weather events. The total cost incurred due to each catastrophic occurrence has been meticulously recorded in millions of dollars. To ensure accuracy across different time periods, these costs are adjusted for inflation using the Consumer Price Index (CPI), providing a standardized measure that enables meaningful comparisons between different events.

A significant measure reflecting the severity of these weather disasters is the number of deaths they have caused. This dataset presents this valuable statistic under the column Deaths, allowing researchers to assess not only economic implications but also human impacts associated with each disaster event.

Obtained from NOAA National Centers for Environmental Information (NCEI) U.S., this data serves as a reliable source for understanding past weather calamities within US borders. Its wide range includes devastating storms, destructive wildfires, deadly heatwaves, crippling droughts; all contributing to one overarching objective – better preparedness for future climate-related challenges.

By analyzing this comprehensive dataset, researchers can gain insights into trends over time while identifying regions most vulnerable to specific types of extreme weather events. These findings allow policymakers and emergency response planners to make informed decisions regarding resource allocation, risk mitigation strategies, and community resilience-building initiatives

How to use the dataset

1. Understanding the Columns

The dataset contains several columns that provide important information about each weather disaster event. Let's understand what each column represents:

• Disaster: The name or title of the weather disaster event.
• Disaster Type: The type or category of the weather disaster event.
• Total CPI-Adjusted Cost (Millions of Dollars): The total cost of the weather disaster event in millions of dollars, adjusted for inflation using the Consumer Price Index (CPI).
• Deaths: The number of deaths caused by the weather disaster event.
• Description: A brief description or summary of the weather disaster event.

2. Exploring Total Cost and Deaths

One key aspect to explore is how much damage was caused by each weather disaster event, as well as its human impact in terms of fatalities. By analyzing these factors, you can gain insights into which types of disasters are more costly and have a higher mortality rate.

You can start by visualizing the Total CPI-Adjusted Cost (Millions of Dollars) column to identify which disasters have been more financially devastating over time. Additionally, you can analyze the Deaths column to gauge which types of disasters have had a greater impact on human lives.

3. Comparing Disasters

Another interesting analysis would involve comparing different disasters based on their characteristics such as type, cost, and fatalities. You can group similar types together and compare their costs or death tolls across different time periods.

For example, you could examine whether hurricanes tend to cause higher financial losses compared to floods or wildfires. Or, you could analyze if certain types of disasters have been more deadly than others.

4. Analyzing Descriptions

The Description column provides a brief summary of each weather disaster event. Analyzing the descriptions can give you valuable insights into the specific circumstances surrounding each event. By understanding the context and conditions, you can get a better understanding of why some events resulted i...

Hurricane Risk Modeling Market Outlook

According to our latest research, the global hurricane risk modeling market size reached USD 1.54 billion in 2024, reflecting the rapid adoption of advanced analytics and predictive technologies across multiple industries. The market is set to expand at a robust CAGR of 9.1% from 2025 to 2033, reaching a forecasted market size of USD 3.36 billion by 2033. This remarkable growth trajectory is driven by increased climate volatility, rising economic losses from hurricanes, and the growing need for accurate risk assessment and mitigation strategies among insurers, governments, and urban planners globally.

One of the primary growth factors fueling the hurricane risk modeling market is the escalating frequency and intensity of hurricanes worldwide, attributed to climate change and global warming. With coastal populations and infrastructure expanding, the potential for catastrophic losses has surged, compelling insurance companies, government agencies, and urban planners to invest heavily in hurricane risk modeling solutions. These solutions leverage sophisticated algorithms, historical data, and real-time meteorological inputs to forecast hurricane paths, assess potential damages, and optimize disaster preparedness. The heightened demand for data-driven decision-making in risk-prone regions has further propelled the adoption of advanced hurricane risk modeling platforms, making them indispensable tools for both public and private sector stakeholders.

Another significant driver is the technological advancement in modeling tools, particularly the integration of artificial intelligence, machine learning, and simulation technologies. These innovations have dramatically improved the accuracy, speed, and scalability of hurricane risk models, enabling users to generate more granular and actionable insights. The proliferation of cloud-based platforms has also democratized access to high-performance modeling capabilities, allowing organizations of all sizes to leverage cutting-edge analytics without substantial upfront investments in hardware or software infrastructure. As a result, the market for hurricane risk modeling is witnessing a shift from legacy deterministic models to more sophisticated probabilistic and simulation-based approaches, further expanding the market’s addressable base.

Regulatory pressures and the evolving landscape of disaster risk management are also catalyzing market growth. Governments and international agencies are increasingly mandating rigorous risk assessment and reporting standards, particularly in the insurance and reinsurance sectors. This has led to a surge in the adoption of hurricane risk modeling tools to ensure compliance, improve resilience planning, and optimize capital allocation. Moreover, the growing emphasis on public safety, infrastructure resilience, and sustainable urban planning is encouraging municipalities and real estate developers to integrate hurricane risk analytics into their decision-making processes. These trends collectively underscore the critical role of hurricane risk modeling in safeguarding communities, assets, and economies against the escalating threats posed by extreme weather events.

Regionally, North America continues to dominate the hurricane risk modeling market, accounting for the largest revenue share in 2024. This is primarily due to the region’s high exposure to Atlantic hurricanes, significant investments in disaster resilience, and the presence of leading technology providers. However, the Asia Pacific region is poised to witness the fastest CAGR over the forecast period, driven by rising urbanization, increasing awareness of climate risks, and government initiatives aimed at strengthening disaster management capabilities. Europe, meanwhile, is experiencing steady growth, supported by regulatory mandates and the expanding application of risk modeling in infrastructure and urban planning. Latin America and the Middle East & Africa are gradually emerging as promising markets, as governments and private entities recognize the value of proactive hurricane risk assessment in mitigating losses and enhancing preparedness.

Component Analysis

The hurricane risk modeling market is segmented by component into software and services, each playing a distinct yet complementary role in the ecosystem. Software solutions form the backbone of the market, encompassing advanced modeling platforms, simulation engines, and analytical tools d

Cartographic display of cross-scale phenomena and user-centered design are considered through a discussion of the development of an interactive web map depicting local-to-national economic impacts of hurricane storm surge events in Galveston Bay, Texas. Map development and design (as informed by stakeholder focus groups) is described, including approaches to presenting complex, cross-scale impacts of surge events across multiple years and scenarios. Particular consideration is given to how designs may communicate complexity without overly taxing users’ mental and perceptual resources (measured via NASA task-load index) or outstripping their mapping/domain expertise. The map produced uses linked map views to communicate multiple, cross-scale storm surge impacts. The production process and associated user testing highlighted the importance of matching tool complexity to users’ needs and levels of expertise, including through the use of tiered interface design. Optimizing the design of such maps to meet users’ needs is essential to fostering public hazard literacy.

This dataset supports the analysis conducted in the study "Did Official Flood Maps Work in Hurricane Helene? Systematic Evaluation of Official Flood Maps with Ground-truth Observations." It includes: (1) camera-based ground-truth flood extent data from Hurricane Helene in Pinellas County, Florida; (2) official flood maps from FEMA, FDEM, and Fathom; (3) population exposure and flood map performance metrics at the census block group level; (4) auxiliary datasets such as land cover and high-resolution population grids; and (5) Python scripts for calculating the Social Vulnerability Index (SoVI). The data enable spatial validation of flood risk models and investigation of socio-spatial disparities in flood map accuracy.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Hurricane: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 261(3.83%) households where the householder is under 25 years old, 2,063(30.26%) households with a householder aged between 25 and 44 years, 2,191(32.14%) households with a householder aged between 45 and 64 years, and 2,302(33.77%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the city of Hurricane, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hurricane median household income by age. You can refer the same here

This is a component of the main Hurricane Tracker Story Map. The hurricane track layers are provided by National Hurricane Center and the satellite imagery layers are provided by NESDIS using GOES data. About NHC The National Hurricane Center (NHC) is a component of the National Centers for Environmental Prediction (NCEP) located at Florida International University in Miami, Florida. The NHC mission is to save lives, mitigate property loss, and improve economic efficiency by issuing the best watches, warnings, forecasts, and analyses of hazardous tropical weather and by increasing understanding of these hazards. The NHC vision is to be America's calm, clear, and trusted voice in the eye of the storm and, with its partners, enable communities to be safe from tropical weather threats. About NESDIS National Environmental Satellite, Data, and Information Service (NESDIS) provides secure and timely access to global environmental data and information from satellites and other sources to promote and protect the Nation's security, environment, economy, and quality of life.About NESDIS National Environmental Satellite, Data, and Information Service (NESDIS) provides secure and timely access to global environmental data and information from satellites and other sources to promote and protect the Nation's security, environment, economy, and quality of life.GOES-R Series Satellites NOAA’s most sophisticated Geostationary Operational Environmental Satellites (GOES), known as the GOES-R Series, provide advanced imagery and atmospheric measurements of Earth’s Western Hemisphere, real-time mapping of lightning activity, and improved monitoring of solar activity and space weather.GOES satellites orbit 22,236 miles above Earth’s equator, at speeds equal to the Earth's rotation. This allows them to maintain their positions over specific geographic regions so they can provide continuous coverage of that area over time.The first satellite in the series, GOES-R, now known as GOES-16, was launched in 2016 and is currently operational as NOAA’s GOES East satellite. GOES-S, now known as GOES-17, was launched in 2018 and now serves as an on-orbit backup. GOES-T, now GOES-18, launched in 2022 and now serves as NOAA’s operational GOES West satellite. GOES satellites are designated with a letter prior to launch. Once a GOES satellite has successfully reached geostationary orbit, it is renamed with a number. GOES-U, the final satellite in the series, is scheduled to launch in 2024.Together, GOES East and GOES West watch over more than half the globe — from the west coast of Africa to New Zealand and from near the Arctic Circle to the Antarctic Circle. The GOES-R Program is a collaborative effort between NOAA and NASA. NASA builds and launches the satellites for NOAA, which operates them and distributes their data to users worldwide.

Only secondary data was used for this study on the impact of Hurricane Katrina on Southern Louisiana The data sets include: land-cover data for Louisiana, social and economic variables for New Orleans and avian species abundance data gathered from the NOAA Coastal Change Analysis Program, US Census and USGS North American Breeding Bird Survey, respectively. This dataset is associated with the following publication: Chuang, W., T. Eason, A. Garmestani, and C. Roberts. Impact of Hurricane Katrina on the Coastal Systems of Southern Louisiana. Frontiers in Environmental Science. Frontiers, Lausanne, SWITZERLAND, 7(68): 01-15, (2019).

Context

This dataset was created to house Hurricane data in order to create an LSTM Neural Network to predict the economic impact of hurricanes across nine geographical landing points. It should be noted that the definition of Hurricane here was any storm awarded that status from https://www.wunderground.com/hurricane, while the geographic data was from NOAA.

Content

The data included here is: - Hurricane Name - Landfall Date - Storm End Date - Max wind velocity in knots - Max wind velocity in mph - Hurricane Intensity Index which is a score for how intense and volatile the winds are (used to account for deficiencies in the Saffir Simpson Scale - Storm Category - State -County -City -Latitude -Longitude -NDVI which is a measure of the vegetation in an area (taken from Google Earth Engine) -NDWI which is a measure of the water in an area (taken from Google Earth Engine) -Land Value (collected from U.S. Energy Information Administration 2012 CBECS Summary Table B1, Housing and Urban Development Construction Cost Indices Table 4.1Section 202 Construction Costs Actual Costs Per Square Foot by R.S. Means Regions, and United States Department of Agriculture’s National Agriculture Statistical Service Land Value Summary 2019 – Farm Real Estate Average Value per Acre – Regions, States, and United States 2015-2019). These were approximations that can be reevaluated to account for land parcel prices as well as materials

Acknowledgements

This dataset was collected with support from the Urban Coastal Institute

Inspiration

If you all would love to discuss this dataset or offer ways that it can be refined and improved, please reach out to me!

India Imports: Volume: HS: 94055010: Hurricane Lanterns data was reported at 2.300 Unit th in 2018. This records a decrease from the previous number of 26.320 Unit th for 2017. India Imports: Volume: HS: 94055010: Hurricane Lanterns data is updated yearly, averaging 4.010 Unit th from Mar 2004 (Median) to 2018, with 15 observations. The data reached an all-time high of 46.940 Unit th in 2012 and a record low of 0.130 Unit th in 2005. India Imports: Volume: HS: 94055010: Hurricane Lanterns data remains active status in CEIC and is reported by Ministry of Commerce and Industry. The data is categorized under India Premium Database’s Foreign Trade – Table IN.JDR006: Foreign Trade: Harmonized System 8 Digits: By Commodity: HS94: Furniture, Bedding, Cushions, Lamps etc: Imports: Volume.

About this dataset

Context

The dataset presents the median household income across different racial categories in Hurricane. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Hurricane population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 87.43% of the total residents in Hurricane. Notably, the median household income for White households is $70,048. Interestingly, despite the White population being the most populous, it is worth noting that Two or More Races households actually reports the highest median household income, with a median income of $131,250. This reveals that, while Whites may be the most numerous in Hurricane, Two or More Races households experience greater economic prosperity in terms of median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Hurricane.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hurricane median household income by race. You can refer the same here