Mozambique GIS data
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The 500m raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT. 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 99th percentile is selected/clipped using the exclusive criteria. Access to finance, distance to roads and urban areas are defined using a linear distance threshold: • Banks - approx. 20km (0.18 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map. **Data publication:** 2021-11-18 **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. 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 of crop distribution within disaggregated units. 3. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. Poverty- FAO **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 Dairy Processing Final Location: Goat and Sheep (Mozambique - ~ 500m)
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 cattle production potential using proximity to cities and cattle intensification. The score is achieved by processing sub-model outputs that characterize location factors: 1. Supply - Livestock distribution. 2. Demand/Infrastructure - Transportation network (accessibility) It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Cattle Intensification” * 0.4) + (“Major Cities Accessibility” * 0.2) + (”Total Crop Production” *0.3)+ (“Population Density” * 0.1) **Data publication:** 2022-11-17 **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. Global Livestock Production Systems v.5 2011. 3. OpenStreetMap. 4. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 5. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. **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 Dairy Processing Location Score: Cattle (Mozambique - ~ 500 m)
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 goat and sheep production potential using proximity to cities and goat and sheep intensification. The score is achieved by processing sub-model outputs that characterize location factors: 1. Supply - Livestock distribution. 2. Demand/Infrastructure - Transportation network (accessibility) It consists of an arithmetic weighted sum of normalized grids (0 to 100): (Goat and Sheep Intensification” * 0.4) + (“Major Cities Accessibility” * 0.2) + (”Total Crop Production” *0.3)+ (“Population Density” * 0.1) **Data publication:** 2022-11-17 **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. Global Livestock Production Systems v.5 2011 3. OpenStreetMap. 4. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 5. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. **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 Dairy Processing Location Score: Sheep and Goat (Mozambique - ~ 500 m)
Railway and rail transport network of Mozambique 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.
Third-level administrative divisions of Mozambique such as sub-districts or wards. Use this layer for fine-grained spatial analysis, community health planning, and detailed humanitarian response.
First-level administrative divisions of Mozambique such as regions, provinces or states. Use this layer for regional analysis, planning, resource allocation, and thematic mapping at the provincial scale.
Second-level administrative divisions of Mozambique such as districts or departments. Use this layer for local government analysis, service delivery planning, and community-level spatial work.
Water resources and water infrastructure in Mozambique 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.
Mozambique administrative level 0-3 boundaries (COD-AB) dataset version 01. This dataset is structured into 3 levels: - Admin 1: 11 Province - Admin 2: 159 District - Admin 3: 411 Posto Dates associated with this dataset: - 24 March 2024: dataset reviewed for accuracy and completeness - 07 June 2019: valid for use by the humanitarian community - 20 October 1954: last edit to the dataset before publishing - 20 October 1954: boundaries created by the source Contributed by [OCHA Regional Office for Southern and Eastern Africa (ROSEA)](https://data.humdata.org/organization/ocha-rosea). 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)
geoBoundaries ADM3 boundaries for Mozambique: 411 units, representing 2021. Source: Mozambique INE (Instituto Nacional de Estatistica), OCHA ROSEA.
A 1 km population raster for Mozambique published by Orion Terrametrics. Each cell holds a population value for the year 2020.
A 100 m population raster for Mozambique published by Orion Terrametrics. Each cell holds a population value for the year 2015.
Point locations of educational facilities across Mozambique, 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.
The national boundary of Mozambique, 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 Mozambique.
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. Mozambique Province 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
Locations of seaports, harbours and maritime infrastructure in Mozambique. 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.
uav imagery by INGD, acquired 2025-09-01 to 2025-09-02, 5.0 cm per pixel, published on OpenAerialMap.
geoBoundaries ADM2 boundaries for Mozambique: 159 units, representing 2019. Source: Mozambique INE (Instituto Nacional de Estatistica), OCHA ROSEA.
Point locations at the centre of each administrative unit in Mozambique, including place names and administrative codes. Use this layer for labelling maps, geocoding, and location-based services.
Administrative boundary lines of Mozambique as linear features. Use this layer for cartographic production and boundary demarcation visualization.
**ADAM ID: FL-20230217-MOZ-00** Flood covering 334147.0 sq m on Feb 17 2023 in Mozambique. It impacted 204989 people.
Beira and Dondo neighbourhood boundaries. Official Beira bairros from INGC.
This dataset is a compilation of various sources such as Copernicus, Sentinel-1 and Atmospheric and Environmental Research, A Verisk Business, & African Risk Capacity using several days to calculate the maximum flood extent for the whole event.
IDP shapefile collected by MSF Médecins Sans Frontières (MSF) International - doctors without borders during the first week of April 2019 in Mozambique Beire
Adjusted shape file with position and names of dams in Mozambique watersheds.
This dataset contains the shapefile of health facility damages due to Tropical Cyclone Idai as at 8 April 2019
Mozambique school data at lowest administrative level available.
**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected surface waters in Matutuine District, Maputo Province, Mozambique as observed from a Sentinel-1 image acquired on 14 Mar. 2023 at 18:30 local time and using an automated analysis with a machine learning method. Within the analyzed area of about 2,800 km2, about 30 km2 of land appear to be flooded. Water extent appears to have decreased by about 33 km2 since 2 Mar. 2023. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the Bela Vista district, with ~300 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.
This dataset is based on an extract from OpenStreetMap. In addition, it contains access constraints status in the column "status" and is edited regularly with information received by partners of the Logistics cluster in Mozambique. This dataset is the same as used on the Logistics Cluster Access Constraints map published here: https://logcluster.org/sector/cyclone-idai19
Livelihood zone maps define geographic areas of a country where people generally share similar options for obtaining food and income and similar access to markets. An understanding of geographic livelihood systems is a key component of FEWS NET’s food security analysis. The maps are produced through multi-day workshops during which food security stakeholders and country experts identify zones. Factors considered include agro-climatology, elevation, land-cover, market accessibility, sources of food, and major economic activities. A livelihood zone map is typically accompanied by a livelihood description which outlines the key characteristics of each zone. In some cases, maps may also be accompanied by livelihood profiles, which are in-depth descriptions of the characteristics of wealth groups within each zone. The zone maps are available for download as PDFs, PNGs, and GIS shapefiles. The shapefiles and maps are all produced and maintained by FEWS NET. Please consult the logos in maps for the full list of organizations who participated in and/or funded the zoning workshop. For more information on FEWS NET’s livelihood approach and to view our other livelihood products, please click here.
**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected water using Sentinel-2 acquired on 21 March 2023, 09:55 local time over the intersection between Shire river and Zambezi rivers in Sofala, Tete and Zambezia Provinces of Mozambique. Within the cloud free analyzed areas of about 20,000 km², a total of about 2,400 km² of lands appear to be affected with flood waters. Based on Worldpop population data and flood water extent, ~180,000 people are potentially exposed or living close to flooded areas mainly located in Mutarara district, Tete province with ~62,000 people, Caia district, Sofala province with ~38,200 people and Mopeia district, Zambezia province with ~34,000 people This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).
**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected surface waters in Sofala and Zambezia Provinces, Mozambique as observed from a Sentinel-1 image acquired on 17 Mar. 2023 at 03:09 local time. Within the analyzed area of about 27,000 km2, about 1,800 km2 of lands appear to be flooded. Water extent appears to have increased of about 900 km2 since the 13 March 2023. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in Zambezia province, including the district of Namacurra, with ~39,00 people, Nicoadala with ~32,000 people and Maganja Da Costa with ~ 23,000 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
Data produced under Thematic National Mapping and Cartographic updating Project at 1/250 000 scale implemented by National Cartography and Tele-detection Centre, Mozambique (CENACARTA).
Daily extract of Mozambique survey made by WFP to track access constraints
**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected water using Sentinel-2 acquired on 26 March 2023, 09:55 local time over the intersection between Shire river and Zambezi rivers in Sofala, Tete and Zambezia Provinces of Mozambique. Within the cloud-free analyzed areas of about 20,000 km², a total of about 1,800 km² of land appear to be affected by flood waters. Water extent appears to have decreased of about 600 km2 since 21 March 2023. Based on Worldpop population data and flood water extent, ~120,000 people are potentially exposed or living close to flooded areas mainly located in Mutarara district, Tete province with ~46,000 people, Mopeia district, Zambezia province with ~22,000 people, and Caia district, Sofala province with ~20,00 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).