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

Hurricanes and other tropical cyclones pose a high risk to people, property, and ecosystems along the coastline of the United States. The impact of these storms can cascade through the nation's economy and affect communities far from the coast. Understanding the geographic limit of and exposure to winds from tropical cyclones can help citizens, businesses, and government agencies build resilience to these pending dangers. These data portray wind exposure between 1988 and 2022 in the North Atlantic and Eastern Pacific Ocean basins and between 2001 and 2022 in the Western Pacific Ocean basin. Exposure was quantified using intersecting storm tracks, overlapping wind intensity areas, and calculated return intervals.

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!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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.

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.

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.

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.

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.

In that study, eight dust storms in the western U.S. from 2015-2021 were simulated with the WRF-Chem model at 9 km grid spacing, with one simulation directly inserting soil moisture ... retrievals from the Soil Moisture Active Passive (SMAP) satellite into WRF-Chem ("Insert SMAP"), and the other leaving the WRF-Chem soil moisture field uncorrected ("No SMAP"). Model simulations were validated against ASOS METAR observations of 10 m wind speed, U.S. Climate Reference Network (USCRN) observations of soil moisture content, Aerosol Robotic Network (AERONET) retrievals of aerosol optical depth (AOD), and Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of AOD. For all eight cases, this dataset includes 1-hourly and 15-minute WRF-Chem model output for both Insert SMAP and No SMAP experiments for a limited set of relevant variables, USCRN station observations, AERONET station retrievals, and matched-pairs of valid MODIS AOD retrievals with WRF-Chem simulated AOD. All data files are provided in NetCDF.

Context

The geography of India is extremely diverse, from snowy mountains in the north to Coastal Plains in the south, it contains dense rain forests and the Thar Desert. Along with all this India is the second most populated country in the world ( 1.3 billion people). Such diversity brings a lot of different natural disasters from floods, earthquakes to hurricanes and cyclones. Not to mentation, different diseases spread that happen quickly due to the dance population.

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REF

This dataset contains all the disasters that happened in India from 1990 to 2021 with their information.

Content

The dataset has been acquired from Wikipedia. The text is extracted from the Wikipedia articles and then the text is cleaned, processed, and sorted according to the date.

The dataset contains the following columns: * Title: Title of the disaster * Duration: includes day and month as we as intervals for some disasters that lasted more than a day * Year: year of the disaster * Disaster_Info: Information about the disaster ( contains the long and short text describing the disaster) * Date: Date in the specific format ( for some disasters that lasted more than a day we have added the first day of disaster)

More specific information can be extracted from the text using natural language processing techniques.

Acknowledgements

This dataset has been created using Wikipedia articles: Ref

Inspiration

Study and understand the disasters in India using Natural language processing (NLP) and Natural language understanding (NLU) techniques

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).

1) Natural disaster events include avalanches,earthquake, flooding, heavy rainfall & snowfall, and landslides & mudflows as recorded by OCHA field offices and IOM Afghanistan Humanitarian Assistance Database (HADB). 2) A natural disaster incident is defined as an event that has affected (i.e. impacted) people, who may or may not require humanitarian assistance. 3) HADB information is used as a main reference and supplemented by OCHA Field Office reports for those incidents where information is not available from the HADB. OCHA information includes assessment figures from OCHA, ANDMA, Red Crescent Societies, national NGOs, international NGOs, and ERM. 4) The number of affected people and houses damaged or destroyed are based on the reports received. These figures may change as updates are received.

This map shows currently active hurricanes in relation to their recent path, predicted path, and the population that falls underneath them. Click on a diocese for population figures and count of parishes affected.Hurricane tracks and positions provide information on where the storm has been, where it is currently located, and where it is predicted to go. Each storm location is depicted by the sustained wind speed, according to the Saffir-Simpson Scale. It should be noted that the Saffir-Simpson Scale only applies to hurricanes in the Atlantic and Eastern Pacific basins, however all storms are still symbolized using that classification for consistency.Data SourceThis data is provided by NOAA National Hurricane Center (NHC) for the East Pacific and Atlantic, and the Joint Typhoon Warning Center (www.metoc.navy.mil/jtwc/jtwc.html) for the West Pacific and Indian basins. For more disaster-related live feeds visit the Disaster Web Maps & Feeds ArcGIS Online Group.Update FrequencyThe Aggregated Live Feeds methodology checks the Source for updates every 15 minutes. Tropical cyclones are normally issued every six hours at 5:00 AM EDT, 11:00 AM EDT, 5:00 PM EDT, and 11:00 PM EDT (or 4:00 AM EST, 10:00 AM EST, 4:00 PM EST, and 10:00 PM EST).Public advisories for Eastern Pacific tropical cyclones are normally issued every six hours at 2:00 AM PDT, 8:00 AM PDT, 2:00 PM PDT, and 8:00 PM PDT (or 1:00 AM PST, 7:00 AM PST, 1:00 PM PST, and 7:00 PM PST).Intermediate public advisories may be issued every 3 hours when coastal watches or warnings are in effect, and every 2 hours when coastal watches or warnings are in effect and land-based radars have identified a reliable storm center. Additionally, special public advisories may be issued at any time due to significant changes in warnings or in a cyclone. For the NHC data source you can subscribe to RSS Feeds.North Pacific and North Indian Ocean tropical cyclone warnings are updated every 6 hours, and South Indian and South Pacific Ocean tropical cyclone warnings are routinely updated every 12 hours. Times are set to Zulu/UTC.Scale/ResolutionThe horizontal accuracy of these datasets is not stated but it is important to remember that tropical cyclone track forecasts are subject to error, and that the effects of a tropical cyclone can span many hundreds of miles from the center.[Map, App, or Layer Name]Burhans, Molly A., Cheney, David M., Grayons, J . “[Name]”. Scale not given. Version 1.2. CA and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Derived from:Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.www.Catholic-Hierarchy.orgAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/

Layers

This feature service depicts the National Weather Service (NWS) watches, warnings, and advisories within the United States. Watches and warnings are classified into 43 categories. Those 43 categories have been filtered to just coastal watches, warnings, and advisories: coastal flooding, hurricanes, tropical storms. A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or property. People in the path of the storm need to take protective action.A watch is used when the risk of a hazardous weather or hydrologic event has increased significantly, but its occurrence, location or timing is still uncertain. It is intended to provide enough lead time so those who need to set their plans in motion can do so. A watch means that hazardous weather is possible. People should have a plan of action in case a storm threatens, and they should listen for later information and possible warnings especially when planning travel or outdoor activities.An advisory is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. Advisories are for less serious conditions than warnings, that cause significant inconvenience and if caution is not exercised, could lead to situations that may threaten life or property.SourceNational Weather Service RSS-CAP Warnings and Advisories: Public AlertsNational Weather Service Boundary Overlays: AWIPS Shapefile DatabaseSample DataSee Sample Layer Item for sample data during Weather inactivity!Update FrequencyThe services is updated every 5 minutes using the Aggregated Live Feeds methodology.The overlay data is checked and updated daily from the official AWIPS Shapefile Database.Area CoveredUnited States and TerritoriesWhat can you do with this layer?Customize the display of each attribute by using the Change Style option for any layer.Query the layer to display only specific types of weather watches and warnings.Add to a map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools, such as Enrich Data, to determine the potential impact of weather events on populations.

This StoryMap highlights Hurricane Hugo's impact on South Carolina 30 years later. The impact of Hugo is estimated to exceed $7 Billion in South Carolina. Photos and video from the days after the event are included, as well as new interviews with people who witnessed the storm and its aftermath. There are maps of the locations of impacts and the storms track, as well as archived radar and satellite images. Finally, there is information about how the State prepares for and responds to hurricanes. This StoryMap was developed by the South Carolina Department of Natural Resources.

This map is created based on an aggregated version of Data for Good at Meta Network Coverage Maps. The data is aggregated to Census Designated Places of affected areas by Hurricane Idalia (2023).Below is the description of major steps used to create this dataset by Meta. For more information, please refer to the detailed information from Data for Good at Meta.Step 1: Identify crisis areaWe only generate network coverage data for a defined crisis area. We then sample connectivity data within the crisis area from cellular towers that usually hold multiple antennas, each of which has a unique identifier (site).Step 2: Draw estimated coverage areaWe draw an estimated coverage area describing the locations of the devices that are accessing the site to obtain cellular connectivity.Step 3: Calculate coverage areas with uncertain coverageWe highlight areas in which we have observed no network traffic compared to the baseline.Step 4: Calculate likelihood of network outagesFor areas in which we have not observed network traffic, we estimate how likely it is that the cell site is nonoperational given the expected network traffic and the number of people estimated to be within its coverage area.