This dataset contains information about the administrative "Arrondissements" of Cameroon. The dataset is produced by the Institut National de Cartographie (INC) and the descriptions are made on the basis of topographic maps (INC background) at a scale of 1:200 000. This layer represents a global view of the regions and does not correspond to official boundaries. There is no legal validation process for this data. The dataset was edited by FAO-CSI for topology/geometry validation, schema and coding system conformance to SALB-UN specifications. International borders were updated/validated against the official/recognized borders from the United Nations Geospatial Information Section.

This dataset represents the administrative regions of Cameroon. Boundaries of territorial areas gathering several administrative divisions, placed under the authority of Governors. The dataset was originally produced by the Institut National de Cartographie (INC) and the descriptions are made on the basis of topographic maps (INC background) at a scale of 1:200 000. This layer represents a global view of the regions and does not correspond to official boundaries. There is no legal validation process for this data. The dataset was edited by FAO-CSI for topology/geometry validation, schema and coding system conformance to SALB-UN specifications. International borders were updated/validated against the official/recognized borders from the United Nations Geospatial Information Section.

This represents the "départments" administrative divisions of Cameroon. The dataset is originally produced by the Institut national de cartographie (INC) and the descriptions are made on the basis of topographic maps (INC background) at a scale of 1:200 000. This layer represents a global view of the regions and does not correspond to official boundaries. There is no legal validation process for this data. The dataset was edited by FAO-CSI for topology/geometry validation, schema and coding system conformance to SALB-UN specifications. International borders were updated/validated against the official/recognized borders from the United Nations Geospatial Information Section.

Vector map representing potential/suitability score state average, for non-intensive and integrated, small-scale, African Catfish and Nile Tilapia, freshwater fish farming systems using ponds and small water bodies (SWB) in Nigeria. The dataset is produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. Non-intensive aquaculture systems are considered based on natural food supply from SWB or ponds, from integrated systems (crop/livestock byproducts or waste), or with complementary feeding resourcing to on-farm or locally produced feed. The score results from combining sub-model outputs that characterize natural geographical and economical factors: 1. farm-gate sales - based on population density classification 2. Water balance - precipitation/evapotranspiration 3. Soil/slope suitability. 4. Inputs - Crop and livestock byproducts Considered constraints or exclusive criteria are: 1. Urban areas 2. Protected areas It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("WaterBalance" X 0.5) + ("Soil/Slope " X 0.25) + (“Byproducts” X 0.125) + (”FarmgateSales” X 0.125)

This vector dataset contains FAO processed administrative boundaries from multiple sources, produced in 2022 for the Hand-in-Hand Initiative Geospatial Platform publishing. The data was sourced and processed from the United Nations second administrative level boundaries (UN-SALB) programme, complemented with Hand-in-Hand Initiative and geospatial platform data from official geospatial data producers. Country boundaries are processed against UN official recognized borders (UN-map 2018), administrative subdivision checked for geometry a topology, validated, and corrected. Attributes are standardized to the UN-SALB programme schema and coding system. Processed by UN-FAO-CSI AgroInformatics geospatial analysis team, the data is used for thematic mapping, geospatially enabled statistics location-based integration, and Hand-in-Hand geospatial analysis (GIS-MCDA, suitability/location analysis, agricultural typologies, zonal statistics extraction).