United States Virgin Islands GIS data

geoBoundaries ADM0 boundaries for United States Virgin Islands: 1 units, representing 2021. Source: Sentinel-2 10-Meter Land Use/LandCover.

geoBoundaries ADM3 boundaries for United States Virgin Islands: 20 units, representing 2021. Source: United States Census Bureau, MAF/TIGER.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in US Virgin Islands: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name United States Virgin Islands **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0 million km (nan%) - Unpaved roads: 0.0 million km (nan%) About 0.0 million km (nan%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (nan%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Sea ports and ferry terminals from OSM, tagged via `amenity`, `building`, or `port`. Useful for maritime logistics, supply-chain analysis, and coastal access planning. OpenStreetMap is a free, collaborative world map built by volunteers. Completeness varies by region: urban areas are usually well-mapped, remote areas may be incomplete. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap - OSM tags: Key:amenity, Key:building, Key:port - Part of many exports grid on HDX by the HOT

Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT

Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT

Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity - Part of the country exports on HDX by HOT

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: waterway, water, natural - Part of the country exports on HDX by HOT

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. US Virgin Islands District Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.  The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from U.S. Census Bureau. These were published in September 2023. Other administrative boundaries for this country are also available:Country   Subdistrict

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. US Virgin Islands Subdistrict Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.  The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from U.S. Census Bureau. These were published in September 2023. Other administrative boundaries for this country are also available:Country   District

The Composite Risk Index Plus Sea Level Rise is generated by multiplying the SACS Composite Exposure Index by the SACS Combined Hazard Plus Sea Level Rise.   The Composite Exposure Index is created by summing three separate exposure indices which are weighted on a percentage basis:  Population and Infrastructure Index 60%, Environmental Cultural and Habitat 30%, and Social Vulnerability 10% (For the US Virgin Islands, 65% Population and Infrastructure data and 35% Environmental, Cultural and Habitat due to a lack of CDC Social Vulnerability data for the USVI). For additional information on the input datasets and methodology for the exposure indices, please reference the NACCS report, Appendix C, page 103: https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. The SACS Combined Hazard Index Plus Sea Level Rise depicts the percentage annual chance of a specific flood hazard, with an additional 3 ft. of sea level rise added to the flood surface elevation. The three flooding hazards depicted are the 10% annual chance flooding event, the 1% annual chance flooding event, and Category 5 Hurricane Maximum of Maximums. The 10% annual chance flooding is derived via a statistical analysis of tide gauges within the SACS study area, utilizing methodology developed by the USACE Engineering and Research Development Center (https://hdl.handle.net/11681/7353 or https://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-15-00031.1). The 10% annual chance flooding event raster index grid cells are assigned a value of 0.1. The 1% annual chance flooding are aggregated from FEMA’s National Flood Hazard Layer (https://www.fema.gov/national-flood-hazard-layer-nfhl) and raster index grid cells are assigned a value of 0.01. The Category 5 Maximum of Maximums hazard is pulled from NOAA’s storm surge SLOSH inundation data. These data are assigned a value of 0.001 to reflect the low probability of a Category 5 event. Three feet of sea level rise were added to the 10% and 1% annual chance flooding events for this hazard grid. The Composite Risk Index Plus Sea Level Rise grid resolution is 30 meters. This Tier 1 dataset is available for download here: Tier 1 Risk Assessment Download

The Combined Hazard Plus Sea Level Rise grid is created by building the union of the 10% annual chance storm flood surface, the 1% annual chance storm flood surface and the Category 5 Maximum of Maximums (MOM) Hurricane flood surface into a single raster surface. Raster elements are assigned a numeric value (REV) by applying a qualitative “orness” measure for any annual chance flood event that yields the maximum of the given data each raster element location.  The 10% annual chance storm flood elevation is derived through statistical analyses of tide gauges within the SACS study area, utilizing a methodology developed by the USACE Engineering and Research Development Center (https://hdl.handle.net/11681/7353 or https://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-15-00031.1). The 10% annual chance storm flood surface hazard is derived by slicing the topographic DEM sourced from the NOAA Sea Level Rise Viewer across the SACS Study Area: https://coast.noaa.gov/slrdata/ with the 10% annual chance storm flood elevation.  Each raster element of the 10% annual chance storm flood surface hazard is assigned a value of 0.1. The 1% annual chance storm flood surface hazard is aggregated from FEMA’s National Flood Hazard Layer (https://www.fema.gov/national-flood-hazard-layer-nfhl) and assigned a value of 0.01. The Category 5 Maximum of Maximums Hurricane flood hazard are sourced from NOAA’s storm surge SLOSH inundation data (https://www.nhc.noaa.gov/nationalsurge/#data). These data were assigned a value of 0.001 to reflect the low probability of a Category 5 hurricane event. Three feet of sea level rise were added in a “bathtub approach” to the 10% and 1% chance events. Three feet of sea level rise corresponds to the USACE medium curve for a 100 year projection, or the USACE high curve for a 50 year projection. For more information on USACE tools and guidance for sea-level change please refer to the following: https://www.usace.army.mil/corpsclimate/Public_Tools_Dev_by_USACE/sea_level_change/ The SACS Combined Hazard Plus Sea Level Rise grid resolution is 30 meters. This Tier 1 dataset is available for download here: Tier 1 Risk Assessment Download

The Composite Risk Index is generated by multiplying the SACS Composite Exposure Index by the SACS Combined Hazard.  The USACE South Atlantic Coastal Study’s (SACS) initial study product is the Tier 1 Risk Assessment. The main output of the Tier 1 Risk Assessment is the Composite Risk Index. In the SACS, risk is defined as the probability of a hazard, multiplied against the exposure of a specific element. The Composite Exposure Index is generated following the methodology for the Tier 1 Risk Assessment cited in the USACE North Atlantic Coastal Comprehensive Study (NACCS). The Tier 1 Composite Risk Index is derived from multiplying the SACS Composite Exposure Index by the SACS Combined Hazards Present index. The Composite Risk Index is depicted as a classified grid using the Jenks or Natural Breaks classification. The four classes are Low Potential Risk, Medium Potential Risk, Medium/High Potential Risk, and High Potential Risk. The resolution of the grid is 30 meters. The Composite Exposure Index is created by summing three separate exposure indices which are weighted on a percentage basis:  Population and Infrastructure Index 60%, Environmental Cultural and Habitat 30%, and Social Vulnerability 10% (For the US Virgin Islands, 65% Population and Infrastructure data and 35% Environmental, Cultural and Habitat due to a lack of CDC Social Vulnerability data for the USVI). For additional information on the input datasets and methodology for the exposure indices, please reference the NACCS report, Appendix C, page 103: https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. The SACS Combined Hazard Index depicts the percentage annual chance of a specific flood hazard. The three flooding hazards depicted are the 10% annual chance flooding event, the 1% annual chance flooding event, and Category 5 Hurricane Maximum of Maximums. The 10% annual chance flooding is derived via a statistical analysis of tide gauges within the SACS study area, utilizing methodology developed by the USACE Engineering and Research Development Center (https://hdl.handle.net/11681/7353 or https://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-15-00031.1). The 10% annual chance flooding event raster index grid cells are assigned a value of 0.1. The 1% annual chance flooding are aggregated from FEMA’s National Flood Hazard Layer (https://www.fema.gov/national-flood-hazard-layer-nfhl) and raster index grid cells are assigned a value of 0.01. The Category 5 Maximum of Maximums hazard was pulled from NOAA’s storm surge SLOSH inundation data. These data are assigned a value of 0.001 to reflect the low probability of a Category 5 event. The Composite Risk Index grid resolution is 30 meters. This Tier 1 dataset is available for download here: Tier 1 Risk Assessment Download

The Combined Hazard Present grid is created by building the union of the 10% annual chance storm flood surface, the 1% annual chance storm flood surface and the Category 5 Maximum of Maximums (MOM) Hurricane flood surface into a single raster surface. Raster elements are assigned a numeric value (REV)  by applying a qualitative “orness” measure for any annual chance flood event. that yields the maximum of the given data each raster element location.  The 10% annual chance storm flood elevation is derived through statistical analyses of tide gauges within the SACS study area, utilizing a methodology developed by the USACE Engineering and Research Development Center (https://hdl.handle.net/11681/7353 or https://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-15-00031.1). The 10% annual chance storm flood surface hazard is derived by slicing the topographic DEM sourced from the NOAA Sea Level Rise Viewer across the SACS Study Area: https://coast.noaa.gov/slrdata/ with the 10% annual chance storm flood elevation.  Each raster element of the 10% annual chance storm flood surface hazard is assigned a value of 0.1. The 1% annual chance storm flood surface hazard  is aggregated from FEMA’s National Flood Hazard Layer (https://www.fema.gov/national-flood-hazard-layer-nfhl) and assigned a value of 0.01. The Category 5 Maximum of Maximums Hurricane flood hazard are sourced from NOAA’s storm surge SLOSH inundation data (https://www.nhc.noaa.gov/nationalsurge/#data). These data were assigned a value of 0.001 to reflect the low probability of a Category 5 hurricane event. The SACS Combined Hazard Present grid resolution is 30 meters. This Tier 1 dataset is available for download here: Tier 1 Risk Assessment Download

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

United States Virgin Islands administrative level 0-2 boundaries (COD-AB) dataset. The date that these administrative boundaries were established is unknown. This COD-AB was most recently reviewed for accuracy and necessary changes in November 2024. The COD-AB does not require any update. Sourced from GADM Vetting by Information Technology Outreach Services (ITOS) with funding from USAID. This COD-AB is suitable for database or GIS linkage to the United States Virgin Islands COD-PS. As this is an island country, no edge-matched (COD-EM) version of this COD-AB is required. Please see the COD Portal. Administrative level 1 contains 3 feature(s). The normal administrative level 1 feature type is ""District"". Administrative level 2 contains 20 feature(s). The normal administrative level 2 feature type is ""Sub-district"". Recommended cartographic projection: North America Albers Equal Area Conic This metadata was last updated on January 13, 2025.

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

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OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IN ('kindergarten', 'school', 'college', 'university','language_school') OR office='educational_institution' Features may have these attributes: name, amenity, isced:level, capacity Geometry types: point, polygon This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, building - Part of the country exports on HDX by HOT

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. US Virgin Islands Country Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.  The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from U.S. Census Bureau. These were published in September 2023. Other administrative boundaries for this country are also available:District   Subdistrict

Gridded raster of population distribution for the United States Virgin Islands, with cell values measuring population. Published by WorldPop (University of Southampton), the data cover the year 2030.

A gridded raster of population distribution for the United States Virgin Islands, with cell values representing population counts. The data cover the year 2030 and are published by WorldPop (University of Southampton).