Sao Tome and Principe GIS data

geoBoundaries ADM0 boundaries for Sao Tome and Principe: 1 units, representing 2017. Source: OpenStreetMap, Wambacher.

aircraft imagery by Francisco, acquired 2025-10-11 to 2025-10-11, 1.7 cm per pixel, published on OpenAerialMap.

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.

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Sao Tome and Principe. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Sao Tome and Principe. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Sao Tome and Principe: (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 Sao Tome and Principe **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0017 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0005 million km (27.9066%) - Unpaved roads: 0.0003 million km (19.6774%) About 0.0009 million km (52.416%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0001 million km (8.2498%) 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

Sao Tome and Principe administrative level 0-1 boundaries (COD-AB) dataset version 01. This dataset is structured into 1 level: - Admin 1: 7 Province Note: São Tomé and Príncipe comprises two provinces (São Tomé and Príncipe) at Admin 1, subdivided into 7 districts at Admin 2. Six districts are located on São Tomé island (Água Grande, Cantagalo, Caué, Lembá, Lobata, Mé-Zóchi) and one (Pagué) covers Príncipe island. The island of Príncipe has held autonomous region status since 1995. Dates associated with this dataset: - 01 June 2026: dataset reviewed for accuracy and completeness - 01 June 2026: valid for use by the humanitarian community - 01 June 2026: last edit to the dataset before publishing - 17 August 2020: boundaries created by the source Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

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

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

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

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

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

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access 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: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - 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

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

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

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

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

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).

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

Accessibility to major cities dataset is modeled as raster-based travel time/cost analysis, computed for the largest cities (>50k habitants) in the country. This 500m resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location). **Data publication:** 2022-06-28 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Produced using 1. OpenStreetMap data for roads, railways, and rivers; 2. UN Map country border. 3. The HydroSHEDS 15' resolution GRID for the DEM. 4. GHSL - Global Human Settlement Layer. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Accessibility: Travel time-cost to major cities (São Tomé and Príncipe - ~ 500 m)

The vector dataset represents suitable areas for offshore mariculture of Cobia (rachycentron canadum) in São Tomé and Príncipe, produced in 2022 under the scope of the Hand-in-Hand initiative, using publicly accessible data. Cobia is distributed in warm marine waters worldwide displaying a very large potential suitable area. It is selected as a representative species for tropical/subtropical waters offshore mariculture. The geospatial analysis adopts Kapetsky and Aguilar-Manjarrez study (Kapetsky et al., 2013(*)) methodology, where suitability is estimated based on the assumptions: 1. Activity develops within the exclusive economic zones (EEZs). 2. Uses cages for fish. 3. Employs species with proven mariculture technologies and established markets or identified as having good prospects for future development. Analytical criteria and thresholds are defined based on technical limits on cages, cost-effective farming, and species habitat: 1. Depths (25–100 m). 2. Current speeds (10–100 cm/s). 3. Cost limits on distance from shore to offshore installations (25 nm (46.3 km)). 4. Reliable access to a port. 5. Habitat Cobia (rachycentron canadum). (*) Kapetsky, J. M., Aguilar-Majarrez, J., & Jenness, J. (2013). A global assessment of offshore mariculture potential from a spatial perspective. In FAO Fisheries and Aquaculture Technical Paper (Issue No. 549, pp. 0–181). Food and Agriculture Organization of the United Nations. **Data creation:** 2022-08-05 **Contact points:** Metadata Contact: [FAO-Data](mailto:FAO-data@fao.org) **Data lineage:** lineage source statement The assessment analysis is developed using publicly accessible data. FAO Map Catalogue data 1. FAO aquatic species distribution map of Cobia (rachycentron canadum 2. Regions with HYCOM current speeds at 30m depth between 1 - 10, 10 - 100 and > 100 cm/s for all months in the year - Based on HYCOM NCODA (HYbrid Coordinate Ocean Model (HYCOM) and Navy Coupled Ocean Data Assimilation (NCODA)) 8.9 km 2004-2008 3. Sea Ports 4. EEZ - VLIZ Flanders Maritime Institute - Maritime boundaries geodatabase version 5 (2009 5. Bathymetry – GEBCO – General Bathymetric Chart of the oceans - Gridded bathymetric data sets are global terrain models for ocean and land 2021 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:** Zipped raster TIF file for Offshore Mariculture Suitability for Cobia - São Tomé and Príncipe

Point locations of health facilities in Sao Tome and Principe including hospitals, health centres, clinics, dispensaries and health posts. Each feature records the facility name, type and ownership. Use this layer to map healthcare coverage, identify service gaps, plan facility distribution, and support health sector analysis.

A raster of service areas for motorised transport in Sao Tome and Principe, published by Orion Terrametrics. Each cell value indicates whether that location falls within a motorised service area.

Water resources and water infrastructure in Sao Tome and Principe including rivers, lakes, boreholes, wells and water points. Use this layer for water access analysis, WASH programme planning, drought monitoring, flood risk assessment, and water resource management.

Point locations of towns, cities, villages and populated places in Sao Tome and Principe. Each feature includes the place name, type and population where available. Use this layer for location referencing, travel planning, service delivery mapping, and spatial analysis at the settlement level.

Point locations of educational facilities across Sao Tome and Principe, including primary schools, secondary schools and other learning institutions. Each feature records the facility name, type and administrative location. Use this layer to map education access, identify underserved areas, plan school placement, and analyse catchment areas and travel distances to schools.

Point locations of named places in Sao Tome and Principe, including beaches, points, and mountains. Each feature records a name, coordinates, feature class and code, administrative units, population, elevation, and timezone. Published by Orion Terrametrics.

Spatial distribution of the population of Sao Tome and Principe, showing where people live and in what densities across the country. Use this layer for understanding settlement patterns, sizing infrastructure, planning service delivery, and conducting demographic analysis.

Road and transport network of Sao Tome and Principe showing the classification and extent of road infrastructure. Includes primary roads, secondary roads, tracks and footpaths where available. Use this layer for route planning, accessibility analysis, transport modelling, and logistics planning.

Raster of population counts for Sao Tome and Principe at 1 km resolution, where each cell value represents the number of people in that cell. Published by Orion Terrametrics.

Point locations of named places in Sao Tome and Principe, including beaches, points, mountains and other features. Each point records a name, coordinates, feature class and code, country, administrative unit, population and timezone. Published by Orion Terrametrics.

Locations of seaports, harbours and maritime infrastructure in Sao Tome and Principe. Includes commercial ports, fishing harbours and ferry terminals. Use this layer for maritime transport planning, trade logistics analysis, coastal zone management, and shipping route mapping.

Point locations of airports, airstrips and airfields in Sao Tome and Principe. Each feature includes the facility name, type, elevation and runway information where available. Use this layer for aviation planning, accessibility analysis, emergency response logistics, and transport network mapping.

Travel time in minutes to the nearest health facility in Sao Tome and Principe, showing healthcare accessibility across the country by motorised transport or walking. Use this layer for healthcare access analysis, facility gap identification, health equity studies, and humanitarian planning.