Sao Tome and Principe GIS data

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.

**UNOSAT code: FL20220119STP** This map illustrates satellite-detected landslides/mudflow in Neves town, Lembá district, São Tomé province, São Tomé and Príncipe as observed in Pleiades satellite imagery acquired on 23 January 2022 at 10:12 local time. Within this analyzed area, about 10 structures appear to be affected, and 63 structures are potentially affected. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT)

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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 - name:pt This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

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

**UNOSAT code: FL20220119STP** This map illustrates satellite-detected landslides/mudflow in western part of Lembá district, São Tomé province, São Tomé and Príncipe as observed in Pleiades satellite imagery acquired on 23 January 2022 at 10:12 local time. Within the analyzed area, about 5 structures appear to be affected, and 23 structures are potentially affected. 2 road obstacles, 2 affected bridges, and a potentially affected bridge were also identified. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

This vector dataset represents suitable areas for offshore mariculture of yellowfin tuna (thunnus albacares) in São Tomé and Príncipe, produced in 2022 under the scope of the Hand-in-Hand initiative, using publicly accessible data. 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 yellowfin tuna (thunnus albacares). (*) 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 thunnus albacares (yellowfin tuna 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 Yellowfin Tuna - São Tomé and Príncipe