Togo GIS data
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The raster dataset consists of a 500 m score grid for dairy processing industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on dairy production potential using proximity to cities and dairy density. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Livestock distribution. 2. Infrastructure - Transportation network (accessibility) 3. Demand - Atlas AI Asset Wealth Index. It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Dairy Density” * 0.4) + (“Major Cities Accessibility” * 0.4) + (”Asset Wealth” *0.2). **Data publication:** 2022-04-27 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 2. OpenStreetMap. 3. Altas AI - Asset Wealth Index 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Dairy Processing Location Score (Togo - ~ 500 m)
The raster dataset consists of a 500 m score grid for slaughterhouse industry facilities, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on livestock production potential using proximity to cities, crop production, and aggregated density of cattle, goats, sheep and pig. Aggregated density layer produced using weighting by the average live weight: Goat 30kg (X0.09); Sheep 30kg (X0.09); Cattle 210kg (X0.63), Pig 63kg (X0.19). The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed and livestock distribution. 2. Infrastructure - Transportation network (accessibility) 3. Demand - Atlas AI Asset Wealth Index. It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Livestock Density” * 0.4) + ("Asset Wealth" * 0.3) + (“Major Cities Accessibility” * 0.2) + ("Crop Production" * 0.1). **Data publication:** 2022-12-23 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Metadata Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Slaughterhouse Location Score (Togo - ~ 500 m)
Third-level administrative divisions of Togo such as sub-districts or wards. Use this layer for fine-grained spatial analysis, community health planning, and detailed humanitarian response.
Spatial distribution of the population of Togo, 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.
Travel time in minutes to the nearest health facility in Togo, 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.
Second-level administrative divisions of Togo such as districts or departments. Use this layer for local government analysis, service delivery planning, and community-level spatial work.
The national boundary of Togo, defining the outermost extent of the country's territory. Use this layer as a base for national-scale maps, spatial analysis, and geographic context in any GIS project covering Togo.
A 1-kilometre resolution raster of population for Togo for 2020. Each cell value represents the estimated number of people in that cell. Published by Orion Terrametrics.
A 100-metre resolution raster of population for Togo for 2015. Each cell value represents the estimated number of people in that cell. Published by Orion Terrametrics.
Line features representing road segments in Togo, published by Orion Terrametrics. Each row is a road or path with attributes such as highway type, surface, smoothness, width, lanes and OpenStreetMap identifiers. The dataset contains 248,605 features.
Retirement Notice: This item is in mature support as of October 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. Togo Region Boundaries provides a 2023 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 2023 boundaries are provided by Michael Bauer Research GmbH. These were published in October 2023. Other administrative boundaries for this country are also available: Country
First-level administrative divisions of Togo such as regions, provinces or states. Use this layer for regional analysis, planning, resource allocation, and thematic mapping at the provincial scale.
Spatial distribution of estimated births across Togo at 1km resolution. Each grid cell shows the estimated number of live births. Use this layer for maternal health planning, antenatal care facility siting, health resource allocation, and reproductive health programme design.
Point locations of airports, airstrips and airfields in Togo. 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.
Administrative boundary lines of Togo as linear features. Use this layer for cartographic production and boundary demarcation visualization.
Point locations of health facilities in Togo 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 school-age population for Togo, with twelve bands. Each band represents a different school-age group, and cell values give the estimated number of children in that group. Published by Orion Terrametrics.
Togo administrative level 0-3 boundaries (COD-AB) dataset version 02. This dataset is structured into 3 levels: - Admin 1: 5 Region - Admin 2: 40 Prefecture - Admin 3: 373 nan Dates associated with this dataset: - 30 October 2025: dataset reviewed for accuracy and completeness - 07 January 2021: valid for use by the humanitarian community - 12 January 2020: last edit to the dataset before publishing - 12 January 2020: boundaries created by the source Contributed by [OCHA West and Central Africa (ROWCA)](https://data.humdata.org/organization/ocha-rowca). Quality assured, configured, and published by [OCHA Field Information Services (FIS)](https://data.humdata.org/organization/ocha-fiss) and [HDX](https://data.humdata.org/organization/hdx). Part of the dataset: [Global Subnational Administrative Boundaries](https://data.humdata.org/dataset/cod-ab-global)
Railway and rail transport network of Togo showing the extent and classification of rail infrastructure. Includes passenger and freight lines. Use this layer for rail transport planning, freight logistics analysis, infrastructure assessment, and multimodal transport mapping.
Locations of seaports, harbours and maritime infrastructure in Togo. 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.
Spatial distribution of estimated pregnancies across Togo at 1km resolution. Each grid cell shows the estimated number of pregnancies including live births, stillbirths and abortions. Use this layer for maternal health planning, antenatal care programme design, health facility placement, and reproductive health analysis.
Number of Deaths, Injured, Missing, Houses Destroyed, Houses Damaged, Victims Affected, Relocated, Evacuated, Losses and Damages in crops by climate change event
Togo administrative level 0-3 edge-matched gazetteer, shapefiles, geodatabase, and geoservice. COD-EM datasets do not replace the authoritative COD-AB available here; however COD-EM datasets may be preferred for cartographic purposes. See caveats. These layers are suitable for database or GIS linkage to the Togo - Subnational Population Statistics tables. Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.
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: FL20191029TGO** Cette carte illustre l'étendue des eaux de surface détectées par images satellite (Sentinel-1, image du 26 Octobre 2019) à dans la préfecture de Lacs et alentours, Région Maritime, Togo. Dans la zone analysée d'environs 250,000 ha, environ 8,000 personnes sont potentiellement exposées aux 4060 ha d'eaux de surface détectées le 26 Octobre 2019. Ceci est une image préliminaite qui n'a pas été validée sur le terrain. Merci d'envoyer vos commentaires à UNITAR-UNOSAT.
A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Togo. 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
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The raster dataset represents top location score areas suitable for fruit storage, filtered by exclusive criteria: access to finance, distance to major roads, access to IT (mobile broadband connection) and electricity. Access to finance and roads are defined using a linear distance threshold: • Banks - 10km buffer radius. • Major roads - 2km buffer radius. Access to IT and electricity is characterized by applying the mobile broadband coverage map and Atlas AI Electrification map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. 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 (optimal location). **Data publication:** 2022-12-23 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Altas AI - Electrification. 6. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Download: Crop Storage Final Location: Fruits (Togo - ~ 500 m)
The raster dataset represents top location score areas suitable for sorghum storage, filtered by exclusive criteria: access to finance, distance to major roads, access to IT (mobile broadband connection) and electricity. Access to finance and roads are defined using a linear distance threshold: • Banks - 10km buffer radius. • Major roads - 2km buffer radius. Access to IT and electricity is characterized by applying the mobile broadband coverage map and Atlas AI Electrification map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. 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 (optimal location). **Data publication:** 2022-12-23 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Altas AI - Electrification. 6. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Crop Storage Final Location: Sorghum (Togo - ~ 500 m)
The raster dataset represents top location score areas suitable for vegetables storage, filtered by exclusive criteria: access to finance, distance to major roads, access to IT (mobile broadband connection) and electricity. Access to finance and roads are defined using a linear distance threshold: • Banks - 10km buffer radius. • Major roads - 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. 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 (optimal location). **Data publication:** 2022-12-23 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Altas AI - Electrification. 6. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Crop Storage Final Location: Vegetables (Togo - ~ 500 m)
The 500 m raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT, access to electricity and distance to urban areas. The layer was produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs characterizing logistical factors dairy processing facilities siting: Supply, demand, Infrastructure/accessibility. The top score is selected/clipped using the exclusive criteria. Access to finance, distance to roads, and urban areas are defined using a linear distance threshold: • Banks - 10km buffer radius. • Major roads - 2km buffer radius. • Urban centers - 10km buffer radius. Access to IT and electricity is characterized by applying the mobile broadband coverage map and the Atlas AI Electrification map. **Data publication:** 2022-12-23 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 2. OpenStreetMap. 3. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. 4. Atlas AI - Asset Wealth Index 2020 and Electrification 2021. 5. Altas AI - Electrification. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Dairy Processing Final Location (Togo- ~ 500 m)
The 500 m raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT, electricity, and distance to urban areas. The layer was produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs characterizing logistical factors for the slaughterhouse facilities siting: Demand, Supply, Infrastructure/accessibility. Access to finance, distance to roads, and urban areas are defined using a linear distance threshold: • Banks - approx. 10km • Major roads - approx. 2km • Urban centers - 10km buffer radius Access to IT and electricity is characterized by applying the mobile broadband coverage map and the Atlas AI Electrification map. **Data publication:** 2022-04-28 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. 5. Atlas AI - Asset Wealth Index 2020 and Electrification 2021. 6. Altas AI - Electrification. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Slaughterhouse Final Location (Togo - ~ 500 m)