It is estimated that 10,000 people die each year worldwide due to hurricanes and tropical storms. The majority of human deaths are caused by flooding. Hurricane Irma hit Florida as a Category 4 storm the morning of Sept. 10, 2017, ripping off roofs, flooding coastal cities, and knocking out power to more than people. The storm and its aftermath has killed at least 38 in the Caribbean, 34 in Florida, three in Georgia, four in South Carolina, and one in North Carolina. The occurrences of these natural disasters have been on a high which is a concern for United Nation; The World Meteorological Organization (specialized agency of UN) has been collecting data about all the individuals that are living in and around Hurricanes and Cyclones prone areas. In the aftermath of Irma, WMO wants to find a pattern or a relation between the attributes that will prove whether an individual will SURVIVE OR NOT SURVIVE any hurricane/cyclones in the near future.

DATA DICTIONARY VARIABLES DESCRIPTION DOB Date of Birth(MM/DD/YYYY) M_STATUS Marital Status (Married/Unmarried/Divorced) SALARY Annual salary ( specified in Ranges) EDU_DATA Education details ( Uneducated/High-School/Gradute / Post-Graduate) EMP_DATA Employment details ( Employed/Self-Employed/unemployed) REL_ORIEN Religious orientation ( Agnostic / Atheist / Believer) FAV_TV Favourite TV Show PREF_CAR Preferred brand of car GENDER Gender( Male/Female/Other) FAV_CUIS Favourite Cuisine FAV_MUSIC Favourite Genre of Music ENDU_LEVEL Endurance Level FAV_SPORT Favourite sport FAV_COLR Favourite color NEWS_SOURCE Source of the news DIST_FRM_COAST Distance from the coast MNTLY_TRAVEL Monthly travel GEN_MOVIES Preferred Genre of Music FAV_SUBJ Favourite subject ALCOHOL Preferred Alcohol FAV_SUPERHERO Favourite Superhero Class x(will survive) and y(Will not survive)

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.

This layer features tropical storm (hurricanes, typhoons, cyclones) tracks, positions, and observed wind swaths from the past hurricane season for the Atlantic, Pacific, and Indian Basins. These are products from the National Hurricane Center (NHC) and Joint Typhoon Warning Center (JTWC). They are part of an archive of tropical storm data maintained in the International Best Track Archive for Climate Stewardship (IBTrACS) database by the NOAA National Centers for Environmental Information.Data SourceNOAA National Hurricane Center tropical cyclone best track archive.Update FrequencyWe automatically check these products for updates every 15 minutes from the NHC GIS Data page.The NHC shapefiles are parsed using the Aggregated Live Feeds methodology to take the returned information and serve the data through ArcGIS Server as a map service.Area CoveredWorldWhat can you do with this layer?Customize the display of each attribute by using the ‘Change Style’ option for any layer.Run a filter to query the layer and display only specific types of storms or areas.Add to your map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools like ‘Enrich Data’ on the Observed Wind Swath layer to determine the impact of cyclone events on populations.Visualize data in ArcGIS Insights or Operations Dashboards.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

About this dataset

Context

The dataset tabulates the Hurricane population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Hurricane across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Hurricane was 6,806, a 0.61% decrease year-by-year from 2022. Previously, in 2022, Hurricane population was 6,848, a decline of 1.05% compared to a population of 6,921 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Hurricane increased by 719. In this period, the peak population was 6,961 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Hurricane is shown in this column.
• Year on Year Change: This column displays the change in Hurricane population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Year. You can refer the same here

Introduction

Vietnam’s geographic location and topography make it particularly susceptible to a wide range of disasters, both natural and technological.

This dataset provides data as uploaded, on the occurrence and impacts of mass disasters in Vietnam from 1953 to 2024. This includes both natural (biological, climatological, extra-terrestrial, geophysical, hydrological, meteorological), and technological (industrial accident) disasters. Data was extracted from The International Disaster Database (EM-DAT), maintained by the Centre for Research on the Epidemiology of Disasters (CRED), published by Open Development Vietnam.

It documents natural and human-related disasters in Vietnam from 1953 onward, with key fields related to: - Disaster types and sub types (e.g., storm, flood, drought, epidemic). - Start and end dates. - Human impact (deaths, injuries, people affected). - Economic damage (where data is available). - Geo-location and metadata (latitude/longitude, region, event name).

How Vietnam has changed since 1953, with a focus on climate change-driven disasters:

• Storms (including tropical cyclones).
• Floods.
• Droughts.

We explore long-term trends, decadal comparisons, regional distribution, and statistical correlations to understand Vietnam’s evolving climate vulnerability. The aim is to uncover data-driven insights that inform climate adaptation, disaster risk management, and sustainable development planning.

Recommended Reading

Saline Intrusion in the Mekong Delta (2021-2022). Part 1: The devastating effects of climate change; Mekong and Bengal Deltas. link - Kaggle

Rainfall & Temperature: Vietnam from 1901 to 2020. Part 3: The devastating effects of climate change; monsoon pattern changes. link - Kaggle

Trend Overview by Decade: vietnam_climate_disaster_trend_by_decade.csv

A markdown document with the R code for all the below visualisations. link

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F2808eacb7fa88dbb013546f248fe9e27%2FScreenshot%202025-06-15%2010.55.26.png?generation=1749983744158969&alt=media" alt="">

Bar Chart: - Description: - This chart presents normalised values of four metrics; Event Count, Total Affected, Total Damage Adjusted, and Total Deaths. Aggregated by decade from the 1950's to the 2020's. The normalisation allows comparison across different scales.
- Observations: - The 2000's show the highest Event Count, indicating a peak in disaster frequency. - The 1960's had a significant Total Death component. In November 1964, the quick succession of three typhoons (Iris, Joan, and Kate), caused widespread flooding in Vietnam causing 7,000 deaths, as confirmed by dataset analysis. link - Wikipedia: November 1964 Vietnam floods - Total Affected and Total Damage Adjusted peak in the 1990's and 2000's, suggesting increased vulnerability or severity during these periods. - Insights: - This visualisation highlights temporal shifts in disaster impacts, with the 1960's notable for high mortality and the 2000's for frequency, reflecting historical disaster patterns. However, the 1964 Pacific typhoon season was the most active tropical cyclone season recorded globally, with a total of 39 tropical storms forming. It had no official bounds; it ran year-round in 1964, but most tropical cyclones tend to form in the northwestern Pacific Ocean between June and December. The unprecedented and extended tropical storm season of 1964, accounted for the large amount of deaths by disasters, during the 1960's in Vietnam.

*2020s data is partial

Summary

• Rising Disaster Frequency:
  • Climate-related disasters have increased sharply since the 1980's, particularly storms and floods.
  • From just 2 recorded events in the 195...

Tornadoes in North America

Magnitude, Fatalities, Injuries, and Crop Loss Data

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset compiles historical data on tornadoes in the United States, Puerto Rico, and the U.S. Virgin Islands – providing a critical resource to researchers and policy-makers alike. Obtained from the National Weather Service's Storm Prediction Center (SPC), it contains an intricate wealth of information that sheds light onto patterns of tornado outbreaks across time & geographical space yielding insights into factors like magnitude, fatalities/injuries caused and losses incurred by these devastating weather disasters. With attributes such as Start Longitude/Latitude, End Longitude/Latitude, Day of Origin & Time Zone – this dataset will enable a comprehensive analysis of changes over time in regards to both intensity & frequency for those interested in studying climate change and its impact on extreme weather events such as tornadoes. For disaster management personnel dealing with natural hazards like floods or hurricanes - a familiarity with this dataset can help identify areas prone to frequent storms - thereby empowering proactive measures towards their mitigation.*

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How to use the dataset

This dataset contains historical tornado tracks in the United States, Puerto Rico, and the U.S. Virgin Islands. The data was obtained from the National Weather Service's Storm Prediction Center (SPC). It includes thirty-seven columns of statistics which you can use to analyze when, where, and how frequently tornadoes occur in North America over time.

Research Ideas

• Creating a tornado watch and warning system using Geographic Information Systems (GIS) technology to track and predict the path of dangerous storms.
• Developing an insurance system that gives detailed information on historical data related to natural disasters including tornadoes, hurricanes, floods, etc., in order to better assess risk levels for insuring homes and businesses in vulnerable areas.
• Developing an app that provides real-time notifications for potential tornadoes by utilizing the dataset's coordinates and forecasting data from the National Weather Service (NWS). The app could even provide shelter locations near users based on their current location ensuring that people are aware of potential active threats nearby them quickly increasing safety levels as much as possible when these hazardous events occur

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Historical_Tornado_Tracks.csv | Column name | Description | |:--------------|:-------------------------------------| | OM | Origin Mode (Point or Line) (String) | | YR | Year (Integer) | | MO | Month (Integer) | | DY | Day (Integer) | | DATE | Date (String) | | TIME | Time (String) | | TZ | Time Zone (String) | | ST | State (String) | | STF | FIPS State Code (String) | | STN | State Name (String) | | MAG | Magnitude (Integer) | | INJ | Injuries (Integer) | | FAT | Fatalities (Integer) | | LOSS | Loss (Integer) | | CLOSS | Crop Loss (Integer) | | SLAT | Starting Latitude (Float) | | SLON | Starting Longitude (Float) | | ELAT | Ending Latitude (Float) | | ELON | Ending Longitude (Float) | | LEN | Length of Track (Float) ...

A new exhibit at the museum of South Texas History is aiming to teach about the dangers of hurricanes through past experiences. The museum is hoping to give some insight of what happened during these hurricanes, the aftermath, and some other historical hurricanes in the Rio Grande Valley and the recovery from it all. "You don't live in the Valley or even Texas and not deal with hurricanes, it's just part of living here," Exhibits and Collections Coordinator Melissa Pena said. Most Valley residents have lived through one or two hurricanes, and each one brought a lesson with it. "We want people to come through and see it maybe it will spark a memory, and they can give us more information about what they went through, we're taking in oral histories again and this is one of them," Pena said. It's those memories Pena hopes will keep Valley residents prepared through their exhibit, Hurricanes: Overcoming catastrophe. "It is a partnership with Planet Texas which is out of UT Austin and their job is to mitigate. So they do the science portion, and we communicate and so this is our section of it," Pena said. Dolly, Hannah, Allen, and Buleah are just some of the storms on display, but there are also items and mementos from those storms that remind us it only takes one. "We have cans that were filled with water, from when Dolly hit. They stopped producing beer and started filling cans with water," Pena said. Those cans of water were distributed to a colonia in the Valley after bottles of water were hard to find in stores. There is also a hurricane tracking map and pictures of the aftermath from each storm. The museum is hoping these stories remind everyone, it can happen to us. Pena says they're having a community day August 5th from 10a.m. to 3 p.m. so the community can share their stories and help their partners at UT Austin do more modeling.

The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of past hurricanes is constantly growing. Machine learning methods, that can capture non-linearities and complex relations, have only been scarcely tested for this application. We propose a neural network model fusing past trajectory data and reanalysis atmospheric images (wind and pressure 3D fields). We use a moving frame of reference that follows the storm center for the 24 h tracking forecast. The network is trained to estimate the longitude and latitude displacement of tropical cyclones and depressions from a large database from both hemispheres (more than 3,000 storms since 1979, sampled at a 6 h frequency). The advantage of the fused network is demonstrated and a comparison with current forecast models shows that deep learning methods could provide a valuable and complementary prediction. Moreover, our method can give a forecast for a new storm in a few seconds, which is an important asset for real-time forecasts compared to traditional forecasts.

Structured facts about Belize’s Atlantic hurricane season: seasonal phases, peak months, advisory types, historical storm entries, and traveler preparedness guidance.

While the Fujita and Saffir-Simpson Scales characterize tornadoes and hurricanes respectively, there is no widely used scale to classify snowstorms. The Northeast Snowfall Impact Scale (NESIS) developed by Paul Kocin of The Weather Channel and Louis Uccellini of the National Weather Service characterizes and ranks high-impact Northeast snowstorms. These storms have large areas of 10 inch snowfall accumulations and greater. NESIS has five categories: Extreme, Crippling, Major, Significant, and Notable. The index differs from other meteorological indices in that it uses population information in addition to meteorological measurements. Thus NESIS gives an indication of a storm's societal impacts. NESIS scores are a function of the area affected by the snowstorm, the amount of snow, and the number of people living in the path of the storm. The aerial distribution of snowfall and population information are combined in an equation that calculates a NESIS score which varies from around one for smaller storms to over ten for extreme storms. The raw score is then converted into one of the five NESIS categories. The largest NESIS values result from storms producing heavy snowfall over large areas that include major metropolitan centers. For details on how NESIS scores are calculated at the National Climatic Data Center, see Squires and Lawrimore (2006).

Tropical cyclones (TCs) pose a major risk to societies worldwide. While data on observed cyclones tracks (location of the center) and wind speeds is publicly available these data sets do not contain information on the spatial extent of the storm and people or assets exposed. Here, we provide a collection of tropical cyclone exposure data (TCE-DAT) derived with the help of spatially-explicit data on population densities and Gross Domestic Product (GDP), also available at http://doi.org/10.5880/pik.2017.007. Up to now, this collection contains: 1) A global data set of tropical cyclone exposure accumulated to the country/event level http://doi.org/10.5880/pik.2017.0052) A global data set of spatially-explicit tropical cyclone exposure available for all TC events since 1950 http://doi.org/10.5880/pik.2017.008 TCE-DAT is considered key information to 1) assess the contribution of climatological versus socioeconomic drivers of changes in exposure to tropical cyclones, 2) estimate changes in vulnerability from the difference in exposure and reported damages and calibrate associated damage functions, and 3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP, employment, or migration. We expect that the free availability of the underlying model and TCE-DAT will make research on tropical cyclone risks more accessible to non-experts and stakeholders.

In Puerto Rico, tens of thousands of landslides, slumps, debris flows, rock falls, and other slope failures were triggered by Hurricane María, which made landfall on 20 September 2017. “Landslide” is used here and below to represent all types of slope failures. This dataset is a point shapefile of landslide headscarps identified across Puerto Rico using georeferenced aerial and satellite imagery recorded following the hurricane. The imagery used includes publicly available aerial imagery obtained by the Federal Emergency Management Agency (FEMA; Quantum Spatial, Inc., 2017), aerial imagery obtained by the National Oceanic and Atmospheric Administration (NOAA; NOAA, 2017), and several WorldView satellite imagery datasets available from DigitalGlobe, Inc. The FEMA imagery was recorded by Sanborn and Quantum Spatial, Inc. between 25 September and 27 October 2017, has a pixel resolution of approximately 15 cm, and includes over 6,000 image tiles that cover approximately 97% of the large island and 100% of Vieques. The NOAA imagery was recorded 22-26 September 2017, also has a resolution of approximately 15 cm, and covers about 10% of the large island, 60% of Vieques, and 100% of Culebra. The DigitalGlobe imagery used in this project was recorded during September-November 2017, has a pixel resolution of approximately 50 cm, and covers approximately 99% of the large island and 35% of Vieques. DigitalGlobe images were acquired via the DigitalGlobe Open Data Program, the DigitalGlobe Foundation imagery grant, and via partnership with the U.S. Geological Survey. No imagery was examined for Desecheo, Mona, Monito, Caja de Muertos, or other smaller islands. The FEMA imagery was usually used first for landslide mapping due to its high resolution and more accurate georeferencing. For almost every location, there were multiple images available due to overlap in each dataset and overlap between different datasets. This overlap was helpful when clouds or shadows obscured the view of the ground surface in one or more images for a given location. Additional oblique and un-georeferenced aerial imagery recorded by the Civil Air Patrol (ArcGIS, 2017) was consulted, if needed. Comparing the post-event imagery with pre-event imagery available through the ESRI ArcGIS basemap layer and/or Google Earth was useful to accurately identify sites that failed during September 2017; such comparisons were made for landslides that appeared potentially older. Some landslides in our inventory may have occurred prior to Hurricane María—potentially triggered by Hurricane Irma which passed northeast of Puerto Rico two weeks earlier—or between the time of the hurricane and when photographs were taken. UTM Zone 19N projection with WGS 84 datum was used throughout the mapping process. The inventory process began with creation of a first draft by a team of 15 people. This draft was subsequently checked for quality and revised by the three leaders of the mapping effort. Each identified landslide is represented by a point located at the center of its headscarp. The horizontal position of headscarp points was carefully selected using multiple overlapping images (usually available) and other geospatial datasets including lidar acquired during 2015 and available from the U.S. Geological Survey 3DEP program, the U.S. Census Bureau TIGER road shapefile, and the National Hydrology Dataset flowline shapefile. Mapping was generally performed at 1:1000 scale. Given errors in georeferencing and landslides poorly resolved in imagery, we conclude that headscarp point locations are generally accurate within 3 m. Municipality (municipio) and barrio names in which each landslide occurred are included in the attribute table of the shapefile, as are the geographic coordinates of each point in decimal degrees (WGS 84 datum). Landslides were identified in 72 of the 78 municipalities of Puerto Rico. No landslides were documented on the island municipalities of Culebra or Vieques. On the main island of Puerto Rico, 64% of land experienced 0-3 landslides per square kilometer, 26% experienced 3-25 landslides per square kilometer, and 10% experienced more than 25 landslides per square kilometer. Concentrated zones of more than 100 landslides per square kilometer are in the municipalities of Maricao, Utuado, Jayuya, and Corozal. Of the ten barrios where more than 100 landslides per square kilometer were catalogued, eight are in Utuado. The drainage basins with the highest density of landslides are the Rio Grande de Arecibo and Rio Grande de Añasco watersheds, each with over 30 landslides per square kilometer. Six out of the seven sub-basins with more than 50 landslides per square kilometer are in the Rio Grande de Arecibo basin. We identified and mapped 71,431 landslides in total. The College of Arts and Sciences at the University of Puerto Rico in Mayagüez is thanked for providing release time to K.S. Hughes to permit partial development of this dataset. References ArcGIS, 2017, CAP Imagery – Hurricane Maria: https://www.arcgis.com/home/webmap/viewer.html?webmap=3218d1cb022d4534be0c7d6833c0adf1. Last accessed 18 June 2019. NOAA, 2017, Hurricane MARIA Imagery: https://storms.ngs.noaa.gov/storms/maria/index.html. Last accessed 18 June 2019. Quantum Spatial, Inc., 2017, FEMA PR Imagery: https://s3.amazonaws.com/fema-cap-imagery/Others/Maria. Last accessed 18 June 2019.

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. In the case of tropical cyclones, annual frequency is calculated using the category one of the Saffir-Simpson scale. It corresponds to the largest wind buffer of each past event footprint.

Sources: The dataset includes an estimate of tropical cyclone frequency of Saffir-Simpson category 1. It is based on two sources: 1) IBTrACS v02r01 (1969 - 2008, http://www.ncdc.noaa.gov/oa/ibtracs/), year 2009 completed by online data from JMA, JTWC, UNISYS, Meteo France and data sent by Alan Sharp from the Australian Bureau of Meteorology. 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. Unit is expected average number of event per 100 years multiplied by 100. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: Raw data: IBTrACS, compilation and GIS processing UNEP/GRID-Europe.

Comprehensive demographic dataset for Hurricane, UT, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Context

Disasters include all geophysical, meteorological and climate events including earthquakes, volcanic activity, landslides, drought, wildfires, storms, and flooding. Decadal figures are measured as the annual average over the subsequent ten-year period.

Content

Thanks to Our World in Data, you can explore death from natural disasters by country and by date.

Acknowledgements

https://www.acacamps.org/sites/default/files/resource_library_images/naturaldisaster4.jpg" alt="Natural Disasters">

Inspiration

List of variables for inspiration: Number of deaths from drought Number of people injured from drought Number of people affected from drought Number of people left homeless from drought Number of total people affected by drought Reconstruction costs from drought Insured damages against drought Total economic damages from drought Death rates from drought Injury rates from drought Number of people affected by drought per 100,000 Homelessness rate from drought Total number of people affected by drought per 100,000 Number of deaths from earthquakes Number of people injured from earthquakes Number of people affected by earthquakes Number of people left homeless from earthquakes Number of total people affected by earthquakes Reconstruction costs from earthquakes Insured damages against earthquakes Total economic damages from earthquakes Death rates from earthquakes Injury rates from earthquakes Number of people affected by earthquakes per 100,000 Homelessness rate from earthquakes Total number of people affected by earthquakes per 100,000 Number of deaths from disasters Number of people injured from disasters Number of people affected by disasters Number of people left homeless from disasters Number of total people affected by disasters Reconstruction costs from disasters Insured damages against disasters Total economic damages from disasters Death rates from disasters Injury rates from disasters Number of people affected by disasters per 100,000 Homelessness rate from disasters Total number of people affected by disasters per 100,000 Number of deaths from volcanic activity Number of people injured from volcanic activity Number of people affected by volcanic activity Number of people left homeless from volcanic activity Number of total people affected by volcanic activity Reconstruction costs from volcanic activity Insured damages against volcanic activity Total economic damages from volcanic activity Death rates from volcanic activity Injury rates from volcanic activity Number of people affected by volcanic activity per 100,000 Homelessness rate from volcanic activity Total number of people affected by volcanic activity per 100,000 Number of deaths from floods Number of people injured from floods Number of people affected by floods Number of people left homeless from floods Number of total people affected by floods Reconstruction costs from floods Insured damages against floods Total economic damages from floods Death rates from floods Injury rates from floods Number of people affected by floods per 100,000 Homelessness rate from floods Total number of people affected by floods per 100,000 Number of deaths from mass movements Number of people injured from mass movements Number of people affected by mass movements Number of people left homeless from mass movements Number of total people affected by mass movements Reconstruction costs from mass movements Insured damages against mass movements Total economic damages from mass movements Death rates from mass movements Injury rates from mass movements Number of people affected by mass movements per 100,000 Homelessness rate from mass movements Total number of people affected by mass movements per 100,000 Number of deaths from storms Number of people injured from storms Number of people affected by storms Number of people left homeless from storms Number of total people affected by storms Reconstruction costs from storms Insured damages against storms Total economic damages from storms Death rates from storms Injury rates from storms Number of people affected by storms per 100,000 Homelessness rate from storms Total number of people affected by storms per 100,000 Number of deaths from landslides Number of people injured from landslides Number of people affected by landslides Number of people left homeless from landslides Number of total people affected by landslides Reconstruction costs from landslides Insured damages against landslides Total economic damages from landslides Death rates from landslides Injury rates from landslides Number of people affected by landslides per 100,000 Homelessness rate from landslides Total number of people affected by landslides per 100,000 Number of deaths from fog Number of people injured from fog Number of people affected by fog Number of people left homel...

Global Climate Events and Economic Impact Dataset (2020-2025)

🌍 Overview

This comprehensive dataset tracks major climate events worldwide and their economic impact from 2020 to September 2025. With over 3,000 documented events across 51 countries, this dataset provides crucial insights into the increasing frequency and severity of climate-related disasters and their economic consequences.

📊 Dataset Description

Key Features:

• 3,000+ climate events documented with detailed impact metrics
• 51 countries covered across all continents
• 12 types of climate events including floods, droughts, hurricanes, wildfires, and more
• Economic impact data in millions of USD
• Human impact metrics including affected population, deaths, and injuries
• Infrastructure damage scores (0-100 scale)
• International aid data for major disasters
• Geospatial coordinates for mapping and spatial analysis

📈 Quick Statistics

• Total Events: 3,000
• Countries Covered: 51
• Total Economic Impact: $5,833 million
• Total Lives Lost: 13,845
• Total People Affected: 2,605,651,486
• Average Event Severity: 3.8/10
• Most Affected Country: United States
• Most Common Event Type: Earthquake

🎯 Potential Use Cases

1. Climate Risk Assessment - Identify high-risk regions and predict future patterns
2. Economic Impact Analysis - Calculate losses and analyze insurance requirements
3. Machine Learning - Build predictive models for severity and impact forecasting
4. Policy Planning - Inform climate adaptation strategies and resource allocation

📜 License

This dataset is released under CC BY-SA 4.0 license.

Storms Historical Reports

Analyzing the Impacts of Severe Weather Events

By US Open Data Portal, data.gov [source]

About this dataset

This intriguing dataset contains historical storm prediction reports from the US National Weather Service Storm Prediction Center, giving you a glimpse into some of mother nature's most jaw-dropping weather events! It includes detailed summaries of today's latest information about tornado and severe thunderstorm watches, mesoscale discussions, convective day 1-3 outlooks, and fire weather outlooks. Valuable for both weather professionals and students alike, this one-of-a-kind dataset can be used to gain insights into how extreme storms form and the potential dangers they present. Stay informed with these reliable reports -- last updated at 2019-12-05!

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Featured Notebooks

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Research Ideas

• Creating a geographical map of storm prediction areas, to help people prepare in advance for storms and floods.
• Using the data to develop predictive analytics models for forecasting tornado, thunderstorm or fire activity in specific areas ahead of time.
• Analyzing trends and patterns in severe weather occurrences over time; this could be useful for understanding how extreme weather events are becoming more frequent as climate change progresses

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: web-page-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit US Open Data Portal, data.gov.

Houston Hurricane Harvey Shelter Availability and Needs Data

Aid and Resources for Displaced Residents

By Selene Arrazolo [source]

About this dataset

This dataset contains information on the shelter availability in Houston during Hurricane Harvey, when devastating floods and destruction left thousands homeless. Join the slack group to stay up-to-date on current emergency needs and view maps pinpointing available shelters in your immediate vicinity. With the help of charitable organizations, volunteers, and disaster responders across the globe—we’re helping those affected start their path to rebuilding a safe and stable home. Knowledge is power, so help us spread awareness to ensure that no one goes unaided in this time of need. View meaningful data points from this dataset including accommodation status, check-in times, address locations, contact info for established shelters – without bounds or borders! Let’s use our strengths together to make sure that every recovered household can continue life stronger than before

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How to use the dataset

How to Use This Dataset: Houston Hurricane Harvey Shelter Availability & Needs Info

This Dataset contains information on the availability of shelters in Houston during Hurricane Harvey that are actively being monitored by Sketch City. It includes data on shelter locations, availability, and needs for volunteers and donated items. Here's a quick guide on how to make the most of this dataset.

• Download and view the raw data from Houston-shelter-availability - LAST UPDATED September 2nd 2017.csv or view it directly at this Google doc. This contains detailed information on each shelter including its name, capacity, location, contact info, list of items needed (including food & water), and any other special notes or requests that have been provided by that particular shelter coordinator at the time of going live with this doc (Sept 2nd 2017)

2a) View an interactive map visualizing where all Houston shelters are located along with their current status (i.e., Open vs Closed): Visit this link. For example, if you would like to identify open shelters within a 5 mile radius from Downtown Houston you can utilize this map showing markers for them accordingly along with links to a full page profile containing more detailed information about each shelter plus needs list per location:

2b) Alternatively view an interactive map summarizing volunteer needs across all affected areas:visit this link This is ideal if you would like to summarize summary volunteer opportunities as well as donated materials needed across multiple locations at once — thus allowing visitors better understand situation holistically before viewing individual profiles per location specified in #1 above

• Last but not least; If you see something missing from any type of profile mentioned above or have any questions do not hesitate to get in touch with those who are running & curating efforts around these docs either via Hurricane Harvey Slack team #or Facebook page https:/facebook/​SketchCityHouston

Research Ideas

• Creating a map to help people in need to quickly find the nearest shelter with available space.
• Building an app that keeps people up-to-date about changing availability of shelters as well as needs for supplies and services at each location.
• Developing a chatbot that answers questions from potential volunteers or support staff about Harvey shelters and resources available in Houston during Hurricane Harvey

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

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Mobile Home Park Locations

Locations and details of mobile home parks in the United States

By Homeland Infrastructure Foundation [source]

About this dataset

The Mobile Home Parks Inventory dataset provides a comprehensive list of mobile home parks across the United States. This dataset is crucial for emergency preparedness and evacuation planning, as mobile home parks are inhabited by a vulnerable population that is particularly susceptible to natural disasters such as hurricanes, tornadoes, and flooding.

The dataset includes detailed information about each mobile home park, including its location coordinates (longitude and latitude), address details (street address, city, state, ZIP code), and additional address information if available. It also provides contact details such as telephone numbers and websites for further information about each park.

Furthermore, the dataset contains essential attributes related to the characteristics of mobile home parks. These attributes include the number of units (mobile homes) within each park, allowing authorities to assess capacity during emergency situations. Additionally, it categorizes the type of each park (e.g., recreational vehicle parks), its status (e.g., operational or closed), and its size classification.

To ensure data accuracy and reliability, various validation methods have been implemented. The validation process includes verifying the data sources from where this information was obtained along with dates when data was sourced or validated.

Moreover, this comprehensive inventory incorporates geographical references with FIPS codes for counties in which these mobile home parks are located. Furthermore,the NAICS code provides an additional industry classification system describing these facilities in greater detail.

Lastly,this Mobile Home Parks Inventory recognizes that reverse geocoding has been employed for gathering precise spatial coordinates.Because vulnerability differs across regions,state boundaries have also been included to facilitate analysis at a higher level.Alongside state boundaries,this dataset acknowledges country-level variations which could be valuable while comparing international mobile homes inventories .

By utilizing this extensive collection of accurate and up-to-date information on mobile home parks in the United States policymakers,government agencies,and emergency responders can effectively plan evacuation strategies,mobile resource allocation,and disaster response efforts for ensuring public safety during natural calamities.This valuable knowledge will ultimately enhance disaster mitigation and the overall resilience of these vulnerable communities

How to use the dataset

• Understanding the Columns:

  • X and Y: These columns represent the longitude and latitude coordinates of each mobile home park. They can be used for geographical analysis and mapping purposes.
  • NAME: This column provides the name of each mobile home park. It is useful for identifying specific parks.
  • ADDRESS: The street address where each mobile home park is located.
  • ADDRESS2: Additional address information (if available) for each mobile home park.
  • CITY: The city where each mobile home park is situated.
  • STATE: The state where each mobile home park is located.
  • ZIP and ZIP4: These columns contain the ZIP code information for each mobile home park, including additional ZIP code details if available.
  • TELEPHONE: The contact telephone number for each mobile home park, which can be useful for making inquiries or gathering more information directly from them.
  • TYPE: This column indicates the type of the mobile home park (e.g., permanent residential, seasonal).
  • STATUS: The status of a particular mobile home park (e.g., open, closed).
  • COUNTY and COUNTYFIPS:The county where each mobile h0me_1park is situated along with its associated FIPS code.

• Analyzing Park Characteristics: UNITS & SIZE columns provide insights into various aspects: UNITS represents the number of individual dwelling units within a given Mobile Home Park SIZE describes its physical size.

• Demographic Analysis: By referring to NAICS_CODE & NAICS_DESC columns ,you'll get an idea about the associated industries and business activities in the vicinity of each park.

• Geographical Analysis: The LATITUDE and LONGITUDE coordinates allow you to map out mobile home parks on various GIS (Geographic Information System) platforms. You can analyze the distribution of mobile home parks across different states, cities, or counties.

• Emergency Preparedness: ...

This layer is a subset of Global Recent Hurricanes, Cyclones and Typhoons. You can access the global coverage from here. This layer features tropical storm (hurricanes, typhoons, cyclones) tracks, positions, and observed wind swaths from the past hurricane season for the Atlantic, Pacific, and Indian Basins. These are products from the National Hurricane Center (NHC) and Joint Typhoon Warning Center (JTWC). They are part of an archive of tropical storm data maintained in the International Best Track Archive for Climate Stewardship (IBTrACS) database by the NOAA National Centers for Environmental Information.Data SourceNOAA National Hurricane Center tropical cyclone best track archive.Update FrequencyWe automatically check these products for updates every 15 minutes from the NHC GIS Data page.The NHC shapefiles are parsed using the Aggregated Live Feeds methodology to take the returned information and serve the data through ArcGIS Server as a map service.Area CoveredPacific RegionWhat can you do with this layer?Customize the display of each attribute by using the ‘Change Style’ option for any layer.Run a filter to query the layer and display only specific types of storms or areas.Add to your map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools like ‘Enrich Data’ on the Observed Wind Swath layer to determine the impact of cyclone events on populations.Visualize data in ArcGIS Insights or Operations Dashboards.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!