The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region).

This entry does not contain data itself, it is for the website, the NASA Disasters Mapping Portal: https://maps.disasters.nasa.gov The Disasters Mapping Portal contains numerous datasets that can be streamed from the Portal into GIS software. The Disasters Applications area promotes the use of Earth observations to improve prediction of, preparation for, response to, and recovery from natural and technological disasters. Disaster applications and applied research on natural hazards support emergency mitigation approaches, such as early warning systems, and providing information and maps to disaster response and recovery teams. NOTE: Removed "2017 - Present" from "Temporal Applicability" since it's not valid NOTE: Removed "Event-Specific and Near-Real Time Products" from "Update Frequency" since it's not valid

Health Canada and the Public Health Agency of Canada are making an effort to decrease the damage and suffering man-made and natural disasters inflict on the Canadian public. Several gains have been made in order to strengthen our emergency management, readiness and response in order to come up with a comprehensive natural disaster plan.

OverviewThe multiple hazard index for the United States Counties was designed to map natural hazard relating to exposure to multiple natural disasters. The index was created to provide communities and public health officials with an overview of the risks that are prominent in their county, and to facilitate the comparison of hazard level between counties. Most existing hazard maps focus on a single disaster type. By creating a measure that aggregates the hazard from individual disasters, the increased hazard that results from exposure to multiple natural disasters can be better understood. The multiple hazard index represents the aggregate of hazard from eleven individual disasters. Layers displaying the hazard from each individual disaster are also included.

The hazard index is displayed visually as a choropleth map, with the color blue representing areas with less hazard and red representing areas with higher hazard. Users can click on each county to view its hazard index value, and the level of hazard for each individual disaster. Layers describing the relative level of hazard from each individual disaster are also available as choropleth maps with red areas representing high, orange representing medium, and yellow representing low levels of hazard.Methodology and Data CitationsMultiple Hazard Index

The multiple hazard index was created by coding the individual hazard classifications and summing the coded values for each United States County. Each individual hazard is weighted equally in the multiple hazard index. Alaska and Hawaii were excluded from analysis because one third of individual hazard datasets only describe the coterminous United States.

Avalanche Hazard

University of South Carolina Hazards and Vulnerability Research Institute. “Spatial Hazard Events and Losses Database”. United States Counties. “Avalanches United States 2001-2009”. < http://hvri.geog.sc.edu/SHELDUS/

Downloaded 06/2016.

Classification

Avalanche hazard was classified by dividing counties based upon the number of avalanches they experienced over the nine year period in the dataset. Avalanche hazard was not normalized by total county area because it caused an over-emphasis on small counties, and because avalanches are a highly local hazard.

None = 0 AvalanchesLow = 1 AvalancheMedium = 2-5 AvalanchesHigh = 6-10 Avalanches

Earthquake Hazard

United States Geological Survey. “Earthquake Hazard Maps”. 1:2,000,000. “Peak Ground Acceleration 2% in 50 Years”. < http://earthquake.usgs.gov/hazards/products/conterminous/

. Downloaded 07/2016.

Classification

Peak ground acceleration (% gravity) with a 2% likelihood in 50 years was averaged by United States County, and the earthquake hazard of counties was classified based upon this average.

Low = 0 - 14.25 % gravity peak ground accelerationMedium = 14.26 - 47.5 % gravity peak ground accelerationHigh = 47.5+ % gravity peak ground acceleration

Flood Hazard

United States Federal Emergency Management Administration. “National Flood Hazard Layer”. 1:10,000. “0.2 Percent Annual Flood Area”. < https://data.femadata.com/FIMA/Risk_MAP/NFHL/

. Downloaded 07/2016.

Classification

The National Flood Hazard Layer 0.2 Percent Annual Flood Area was spatially intersected with the United States Counties layer, splitting flood areas by county and adding county information to flood areas. Flood area was aggregated by county, expressed as a fraction of the total county land area, and flood hazard was classified based upon percentage of land that is susceptible to flooding. National Flood Hazard Layer does not cover the entire United States; coverage is focused on populated areas. Areas not included in National Flood Hazard Layer were assigned flood risk of Low in order to include these areas in further analysis.

Low = 0-.001% area susceptibleMedium = .00101 % - .005 % area susceptibleHigh = .00501+ % area susceptible

Heat Wave Hazard

United States Center for Disease Control and Prevention. “National Climate Assessment”. Contiguous United States Counties. “Extreme Heat Events: Heat Wave Days in May - September for years 1981-2010”. Downloaded 06/2016.

Classification

Heat wave was classified by dividing counties based upon the number of heat wave days they experienced over the 30 year time period described in the dataset.

Low = 126 - 171 Heat wave DaysMedium = 172 – 187 Heat wave DaysHigh = 188 – 255 Heat wave Days

Hurricane Hazard

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Atlantic Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Pacific Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

Classification

Atlantic and Pacific datasets were merged. Tropical storm and disturbance tracks were filtered out leaving hurricane tracks. Each hurricane track was assigned the value of the category number that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as being more hazardous. Values describing each hurricane event were aggregated by United States County, normalized by total county area, and the hurricane hazard of counties was classified based upon the normalized value.

Landslide Hazard

United States Geological Survey. “Landslide Overview Map of the United States”. 1:4,000,000. “Landslide Incidence and Susceptibility in the Conterminous United States”. < https://catalog.data.gov/dataset/landslide-incidence-and-susceptibility-in-the-conterminous-united-states-direct-download

. Downloaded 07/2016.

Classification

The classifications of High, Moderate, and Low landslide susceptibility and incidence from the study were numerically coded, the average value was computed for each county, and the landslide hazard was classified based upon the average value.

Long-Term Drought Hazard

United States Drought Monitor, Drought Mitigation Center, United States Department of Agriculture, National Oceanic and Atmospheric Administration. “Drought Monitor Summary Map”. “Long-Term Drought Impact”. < http://droughtmonitor.unl.edu/MapsAndData/GISData.aspx >. Downloaded 06/2016.

Classification

Short-term drought areas were filtered from the data; leaving only long-term drought areas. United States Counties were assigned the average U.S. Drought Monitor Classification Scheme Drought Severity Classification value that characterizes the county area. County long-term drought hazard was classified based upon average Drought Severity Classification value.

Low = 1 – 1.75 average Drought Severity Classification valueMedium = 1.76 -3.0 average Drought Severity Classification valueHigh = 3.0+ average Drought Severity Classification value

Snowfall Hazard

United States National Oceanic and Atmospheric Administration. “1981-2010 U.S. Climate Normals”. 1: 2,000,000. “Annual Snow Normal”. < http://www1.ncdc.noaa.gov/pub/data/normals/1981-2010/products/precipitation/

. Downloaded 08/2016.

Classification

Average yearly snowfall was joined with point location of weather measurement stations, and stations without valid snowfall measurements were filtered out (leaving 6233 stations). Snowfall was interpolated using least squared distance interpolation to create a .05 degree raster describing an estimate of yearly snowfall for the United States. The average yearly snowfall raster was aggregated by county to yield the average yearly snowfall per United States County. The snowfall risk of counties was classified by average snowfall.

None = 0 inchesLow = .01- 10 inchesMedium = 10.01- 50 inchesHigh = 50.01+ inches

Tornado Hazard

United States National Oceanic and Atmospheric Administration Storm Prediction Center. “Severe Thunderstorm Database and Storm Data Publication”. 1: 2,000,000. “United States Tornado Touchdown Points 1950-2004”. < https://catalog.data.gov/dataset/united-states-tornado-touchdown-points-1950-2004-direct-download

. Downloaded 07/2016.

Classification

Each tornado touchdown point was assigned the value of the Fujita Scale that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as more hazardous. Values describing each tornado event were aggregated by United States County, normalized by total county area, and the tornado hazard of counties was classified based upon the normalized value.

Volcano Hazard

Smithsonian Institution National Volcanism Program. “Volcanoes of the World”. “Holocene Volcanoes”. < http://volcano.si.edu/search_volcano.cfm

. Downloaded 07/2016.

Classification

Volcano coordinate locations from spreadsheet were mapped and aggregated by United States County. Volcano count was normalized by county area, and the volcano hazard of counties was classified based upon the number of volcanoes present per unit area.

None = 0 volcanoes/100 kilometersLow = 0.000915 - 0.007611 volcanoes / 100 kilometersMedium = 0.007612 - 0.018376 volcanoes / 100 kilometersHigh = 0.018377- 0.150538 volcanoes / 100 kilometers

Wildfire Hazard

United States Department of Agriculture, Forest Service, Fire, Fuel, and Smoke Science Program. “Classified 2014 Wildfire Hazard Potential”. 270 meters. < http://www.firelab.org/document/classified-2014-whp-gis-data-and-maps

. Downloaded 06/2016.

Classification

The classifications of Very High, High, Moderate, Low, Very Low, and Non-Burnable/Water wildfire hazard from the study were numerically coded, the average value was computed for each county, and the wildfire hazard was classified based upon the average value.

The testing dataset used at TRECVID for the DSDI task in 2020-2022.The dataset includes public videos, ground truth and features of the DSDI task. As the task is continuing, the dataset will be continually updated.There are 32 features across 5 main categories (Environment, Vehicles, Water, Infrastructure, Damage). All videos are airborne low altitude from natural disaster events.

Source: https://data.nasa.gov/dataset/global-landslide-catalog-export-f07b6 This dataset, sourced from NASA’s Global Landslide Catalog (GLC), contains reported rainfall-triggered landslide events worldwide from 2007 to March 7, 2016. Compiled by the NASA Goddard Space Flight Center, it includes over a decade of geolocated landslide records gathered from media reports, scientific publications, and disaster databases. Each entry provides details such as date, location, cause, fatalities, and source, making it a valuable resource for studying the spatial and temporal patterns of landslide hazards. This is a one-time export of the GLC and is ideal for research, risk modeling, and natural disaster analysis.

The U. S. Geological Survey (USGS) makes long-term seismic hazard forecasts that are used in building codes. The hazard models usually consider only natural seismicity; non-tectonic (man-made) earthquakes are excluded because they are transitory or too small. In the past decade, however, thousands of earthquakes related to underground fluid injection have occurred in the central and eastern U.S. (CEUS), and some have caused damage. In response, the USGS is now also making short-term forecasts that account for the hazard from these induced earthquakes. A uniform earthquake catalog is assembled by combining and winnowing pre-existing source catalogs. Seismicity statistics are analyzed to develop recurrence models, accounting for catalog completeness. In the USGS hazard modeling methodology, earthquakes are counted on a map grid, recurrence models are applied to estimate the rates of future earthquakes in each grid cell, and these rates are combined with maximum-magnitude models and ...

Hi all,

I imported this dataset from NASA for the purpose of doing data science in understanding land slides.

The data description in the official website reads

The Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. This is a unique data set with the ID tag “GLC” in the landslide editor. This dataset on data.nasa.gov was a one-time export from the Global Landslide Catalog maintained separately. It is current as of March 7, 2016. The original catalog is available here: http://ojo-streamer.herokuapp.com/ To export GLC data, you must agree to the “Terms and Conditions”.

We request that anyone using the GLC cite the two sources of this database: Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561–575. doi:10.1007/s11069-009-9401-4. [1] Kirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016. [2];

In general, this csv file contains details such as:

• Landslide date
• Landslide trigger reason: rain, downpour, cyclone etc
• Landslide size: Large, medium, small
• Fatality
• Injured
• Event country
• Latitude
• Longitude

I hope this data will be fun to perform data clustering, visualization and EDA.

Underserved communities, especially those in coastal areas in Puerto Rico, face significant threats from natural hazards such as hurricanes and rising sea levels. Limited funding hinders the investment in costly mitigation measures, increasing exposure to natural disasters. Providing coastal resources and data products through effective communication mechanisms is fundamental to improving the well-being of these underserved coastal communities. The overall objectives of the pilot effort to engage and connect with underrepresented coastal communities in Puerto Rico were the following: (1) compile a comprehensive database of the projects and resources relevant to natural hazards in Puerto Rico; (2) foster connections with Puerto Rican interested parties to better understand their priorities regarding coastal hazards and provide them with pertinent U.S. Geological Survey (USGS) resources; and (3) identify knowledge gaps to guide future USGS projects in Puerto Rico. As a result of thi ...

The Mainland China Composite Damaging Earthquake Catalog (MCCDE-CAT) was developed by Li et al. (2021). It contains three databases: Earthquake damage database, Intensity map database, Population exposure database, which for 493 damaging earthquakes that occurred in Mainland China during 1950-2019. Citation: "Y. Li, Z. Zhang, D. Xin, A Composite Catalog of Damaging Earthquakes for Mainland China, Seismol. Res. Lett. 92(6) (2021) 3767-3777. https://doi.org/10.1785/0220210090"

The Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. This is a unique data set with the ID tag “GLC” in the landslide editor. This dataset on data.nasa.gov was a one-time export from the Global Landslide Catalog maintained separately. It is current as of March 7, 2016. The original catalog is available here: http://www.arcgis.com/home/webmap/viewer.html?url=https%3A%2F%2Fmaps.nccs.nasa.gov%2Fserver%2Frest%2Fservices%2Fglobal_landslide_catalog%2Fglc_viewer_service%2FFeatureServer&source=sd To export GLC data, you must agree to the “Terms and Conditions”. We request that anyone using the GLC cite the two sources of this database: Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561–575. doi:10.1007/s11069-009-9401-4. [1] Kirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016. [2]

Photographs and other visual media provide valuable pre- and post-event data for natural hazards. Research, mitigation, and forecasting rely on visual data for post-analysis, inundation mapping and historic records. Instrumental data only reveal a portion of the whole story; photographs explicitly illustrate the physical and societal impacts from an event. This resource provides high-resolution geologic and damage photographs from natural hazards events, including earthquakes, tsunamis, slides, volcanic eruptions and geologic movement (faults, creep, subsidence and flows). The earliest images date back to 1867. Each event also links to NCEI's Global Historical hazards databases, which provide details for these events.

The Global Landslide Catalog (GLC) was developed with the goal of identifying landslide events around the world, regardless of size, impacts, or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. This dataset is a one-time export from the GLC and is current as of March 7, 2016.

Dataset Overview Number of Records: 11,000+ File Format: CSV File Size: ~8 MB

Columns: source_name: News entity source_link: News source event_id: Unique event ID event_date: Date of the landslide event event_time: Time of the landslide event event_title: Title of the news story event_description: Description of the landslide event location_description: Detailed location of the landslide location_accuracy: Accuracy of the location within a certain range landslide_category: Type of landslide landslide_trigger: Cause of the landslide landslide_size: Size of the landslide landslide_setting: Environment where the landslide occurred fatality_count: Number of fatalities injury_count: Number of injuries storm_name: Name of the storm (if applicable) photo_link: Link to a photo of the landslide notes: Additional notes event_import_source: Source of the event import event_import_id: ID of the event import country_name: Country where the landslide occurred country_code: Country code admin_division_name: State or region where the landslide occurred admin_division_population: Population of the administrative division gazeteer_closest_point: Closest point in the gazeteer gazeteer_distance: Distance to the closest point in the gazeteer submitted_date: Date the record was submitted created_date: Date the record was created last_edited_date: Date the record was last edited longitude: Longitude of the landslide location latitude: Latitude of the landslide location

Licensing and Attribution License: The license for this dataset is unspecified. Source Link: https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog-Export/dd9e-wu2v/about_data References: Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561–575. doi:10.1007/s11069-009-9401-4. Kirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016.

The U. S. Geological Survey (USGS) makes long-term seismic hazard forecasts that are used in building codes. The hazard models usually consider only natural seismicity; non-tectonic (man-made) earthquakes are excluded because they are transitory or too small. In the past decade, however, thousands of earthquakes related to underground fluid injection have occurred in the central and eastern U.S. (CEUS), and some have caused damage. In response, the USGS is now also making short-term forecasts that account for the hazard from these induced earthquakes. A uniform earthquake catalog is assembled by combining and winnowing pre-existing source catalogs. Seismicity statistics are analyzed to develop recurrence models, accounting for catalog completeness. In the USGS hazard modeling methodology, earthquakes are counted on a map grid, recurrence models are applied to estimate the rates of future earthquakes in each grid cell, and these rates are combined with maximum-magnitude models and ground-motion models to compute the hazard. The USGS published a forecast for the years 2016 and 2017. This data set is the master earthquake catalog composed of several pre-existing source catalogs.

These seismic data were created by a consortium formed of Risk Engineering and Design (RED) and Evaluación de Riesgos Naturales (ERN), as part of a multi-hazard national risk assessment and risk profile development, conducted by GFDRR Innovation Labs. This contributes to GFDRR’s implementation of the Africa Disaster Risk Assessment and Financing Program, in turn part of the ACP-EU funded programme “Building Disaster Resilience to Natural Hazards in Sub-Saharan African Regions, Countries and Communities”. The probabilistic seismic hazard analysis is based on the ISC-GEM global instrumental catalog, NEIC (U.S. Geological Survey) catalog, and GCMT earthquake catalog. Ground Motion Prediction Equations appropriate to the extensional tectonic regime and stable continental areas within in the region are used, with VS30 soil amplification data. The PSHA is computed using the CRISIS2015 model. These data are created as part of a set of three countries (Ethiopia, Uganda, and Uganda).

These seismic data were created by a consortium formed of Risk Engineering and Design (RED) and Evaluación de Riesgos Naturales (ERN), as part of a multi-hazard national risk assessment and risk profile development, conducted by GFDRR Innovation Labs. This contributes to GFDRR’s implementation of the Africa Disaster Risk Assessment and Financing Program, in turn part of the ACP-EU funded programme “Building Disaster Resilience to Natural Hazards in Sub-Saharan African Regions, Countries and Communities”. The probabilistic seismic hazard analysis is based on the ISC-GEM global instrumental catalog, NEIC (U.S. Geological Survey) catalog, and GCMT earthquake catalog. Ground Motion Prediction Equations appropriate to the extensional tectonic regime and stable continental areas within in the region are used, with VS30 soil amplification data. The PSHA is computed using the CRISIS2015 model. These data are created as part of a set of three countries (Ethiopia, Uganda, and Uganda).

Countermeasures against Natural Disasters Act.

The U.S. National Seismic Hazard Model (NSHM) was updated in 2023 for all 50 states using the best available science related to earthquake seismicity, fault ruptures, ground motions, and hazard estimation techniques to produce a standard of practice for public policy applications. Best available or applicable science is defined here as well-vetted and published hazard input component models and information that are accepted through a comprehensive review process, consistent with open and timely science principles, and encompass a scientifically reasonable range of earthquake characteristics and ground motion effects that improve the basis of the 2023 NSHM. This time-independent probabilistic seismic hazard model benefited from several dozens of co-authors, more than 50 reviewers, and hundreds of end-users, and hazard scientists that attended the public workshops and provided technical inputs and reviews of the inputs and their integration in the hazard assessment. The hazard asses ...

The Canadian Disaster Database is a publicly accessible web-based repository of historical information about natural and man-made disasters that have taken place since 1900 in Canada or abroad that have directly affected Canadians. The database contains information on over 1000 events and can be used to support research, academic activities and decision-making across a breadth of fields including earth sciences, agriculture, climate change, biology and epidemiology, land use planning, insurance, investment, and the anthropological and sociological aspects of community resilience, among many others. Canada endeavours to provide the best information possible; however, the information contained in the Canadian Disaster Database (CDD) is based on information that is sourced from outside parties and may not be accurate. Canada makes no representations, warranties, or guarantees, express or implied, that the data contained in the CDD may be relied upon for any use whatsoever. Canada accepts no responsibility or liability for inaccuracies, errors or omissions in the data and any loss, damage or costs incurred as a result of using or relying on the data in any way. The CDD may contain material that is subject to licensing requirements or copyright restrictions and may not be reproduced, published, distributed or transferred in whole or in part without the consent of the author. The CDD shares information on events that have fully concluded to ensure that the data reflects the event appropriately (i.e., insurance and disaster recovery payment information is available). For this reason, events for which the costs and/or other impacts have not fully recorded contributes to a delay in making them available through the CDD. If you have technical questions about accessing or using the data in the CDD, please write to us at ps.cdd-bdc.sp@ps-sp.gc.ca.

The U. S. Geological Survey (USGS) makes long-term seismic hazard forecasts that are used in building codes. The hazard models usually consider only natural seismicity; non-tectonic (man-made) earthquakes are excluded because they are transitory or too small. In the past decade, however, thousands of earthquakes related to underground fluid injection have occurred in the central and eastern U.S. (CEUS), and some have caused damage. In response, the USGS is now also making short-term forecasts that account for the hazard from these induced earthquakes. A uniform earthquake catalog is assembled by combining and winnowing pre-existing source catalogs. Seismicity statistics are analyzed to develop recurrence models, accounting for catalog completeness. In the USGS hazard modeling methodology, earthquakes are counted on a map grid, recurrence models are applied to estimate the rates of future earthquakes in each grid cell, and these rates are combined with maximum-magnitude models and ground-motion models to compute the hazard. The USGS published a forecast for the years 2016 and 2017. This data set is the catalog of natural earthquakes without duplicates. Duplicate events have been removed based on a hierarchy of the source catalogs. Explosions and mining related events have been deleted.