United Republic of Tanzania GIS data

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. Tanzania Mkoa 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. They are sourced from Tanzania National Bureau of Statistics 2018. These were published in October 2023. Other administrative boundaries for this country are also available: Country

geoBoundaries ADM3 boundaries for United Republic of Tanzania: 3644 units, representing 2015. Source: OpenStreetMap.

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. Tanzania Country Boundary 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. They are sourced from Tanzania National Bureau of Statistics 2018. These were published in October 2023. Other administrative boundaries for this country are also available: Mkoa

geoBoundaries ADM2 boundaries for United Republic of Tanzania: 170 units, representing 2021. Source: Tanzania National Bureau of Statistics / UN OCHA ROSA, OCHA ROSA.

Point locations of educational facilities across Tanzania, 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 health facilities in Tanzania 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.

Gridded population density of Tanzania for the year 2020 at 1km resolution, adjusted to match UN national population estimates. Each grid cell shows the estimated number of people per square kilometre. Use this layer for settlement pattern analysis, infrastructure planning, service delivery optimisation, and identifying high and low density areas.

Affected wards, 10 October 2016 earthquake

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.

Tanzania administrative level 0 (country), 1 (region), 2 (district), and 3 (ward) boundary and line shapefiles, geodatabase These boundary files are suitable for database or GIS linkage to the Tanzania administrative level 0-3 population statistics tables. These boundaries reflect the creation of Songwe region (TZ26) in January 2016 from the western half of Mbeya Region.

**UNOSAT code: LS20231208TZA** This map illustrates satellite detected landslides in Hanang District, Manyara Region, Tanzania as observed from a Sentinel-2 acquired on 07 December 2023 at 11:11 local time. Within the analyzed area of 4,000 ha, about 75 ha of landslides scars are observed. 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: LS20231208TZA** This map illustrates satellite detected landslides in Hanang District, Manyara Region, Tanzania as observed from a Sentinel-2 acquired on 19 December 2023 at 11:01 local time. Within the analyzed area of 5,800 ha, about 55 ha of mudflow scars are observed. 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).

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.

**ADAM ID: **sm\_us7000rqva Magnitude 5.4 earthquake at 10.0 depth occurred on Jan 20 2026 10km NE of Somanga. The epicentre of the earthquake was located at latitude -8.3278 longitude 39.3491. It impacted 85522 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

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 United Republic of Tanzania **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.9904 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0289 million km (2.9189%) - Unpaved roads: 0.6462 million km (65.2423%) About 0.3153 million km (31.8387%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0002 million km (0.0722%) 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

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Artisanal mining site visits in North-West Tanzania

The ICA is a process of consultations supported by mapped-out data that produces a strategic plan describing where different combinations of programme themes are appropriate to achieve goals of reducing food insecurity and climate related shock risk. The ICA combines multi-year food security trends with natural shock risk data to highlight sub-national areas where different programme strategies make sense. Food security trend maps shows areas where safety nets can address regular food insecurity, and others where shocks make recovery more important. Climate-related natural shock risk maps show where DRR, preparedness and early warning efforts can complement food-security objectives. Atop this core foundation, mapped data on subjects including nutrition, gender, livelihoods and resilience can enrich theme-level strategic planning in which all pieces work together. The full group of ICA partners discuss these analytical results to arrive at strategic programmatic directions.

The ICA is a process of consultations supported by mapped-out data that produces a strategic plan describing where different combinations of programme themes are appropriate to achieve goals of reducing food insecurity and climate related shock risk. The ICA combines multi-year food security trends with natural shock risk data to highlight sub-national areas where different programme strategies make sense. Food security trend maps shows areas where safety nets can address regular food insecurity, and others where shocks make recovery more important. Climate-related natural shock risk maps show where DRR, preparedness and early warning efforts can complement food-security objectives. Atop this core foundation, mapped data on subjects including nutrition, gender, livelihoods and resilience can enrich theme-level strategic planning in which all pieces work together. The full group of ICA partners discuss these analytical results to arrive at strategic programmatic directions.

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

The Tanzania Rural and Urban Roads Agency - road network, is a linear referencing system of roads showing roads intersection, start/end of the road. (FAO CSI geospatal analysis team - Comment: Abstract needs elaboration...) GCP bucket dataset fao-maps-gsrv-edit/projects/Tanzania **Contact points:** Resource Contact: [Balthazar Faustine](mailto:ceo@tarutra.go.tz) **Online resources:** TARURA Website

Sub-national boundaries at Ward level. A ward (kata) is the lowest government administrative structure at the community level. In urban areas, a ward can include a number of streets while in rural wards are composed of several villages. **Tanzanian Subdivisions** - Region - District - Division - Ward (Urban) > Street - Ward (Rural) > Village > Hamlet Source: Data provided by the Tanzania Veterinary Service (Dr. Makungu) during the Tanzania National Training on RVF DST, October 2022. **Data publication:** 2025-06-27 **Contact points:** Resource Contact: [Claudia Pittiglio](mailto:claudia.pittiglio@fao.org) Metadata Contact: [Alessandra Falcucci](mailto:alessandra.falcucci@fao.org) **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:**

The 1km raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT 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 (UHT/milk powder) facilities siting: Supply, demand, Infrastructure/accessibility. The top 99th percentile is selected/clipped using the exclusive criteria and the resulting raster classified in 3 equal intervals. 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. • Peri urban areas are defined as being less than approx. 10 km (0.09 degree) from urban (WHERE PopDens>1500 AND area larger than 25 km²). Access to IT is characterized applying the mobile broadband coverage map. **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@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. Tanzania National Bureau of Statistics - Cow numbers (2017 5. Global Livestock Production Systems v.5 2011. 6. OpenStreetMap. 7. FAO – FAOSTAT, Rivers of Africa, Inland Waters of Africa, Airports, Ports. **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 Tanzania cattle dairy processing facilities (UHT/powder) final locations.

Gridded raster of population distribution for the United Republic of Tanzania, with cell values measuring population. Published by WorldPop (University of Southampton), the data cover the year 2030.

This soil organic carbon dataset contains the following maps: soil organic carbon concentration (%) for the 0-10 cm, 10-20 cm, 20-30 cm and 0-30 cm soil layers, and bulk density (kg/m3) and soil organic carbon stock (kg/m2) for the 0-30 cm layer. These maps were produced with (geostatistical) regression-kriging models that combined soil data from the NAFORMA survey, the Tanzania National Soil Survey and the African Soil Profiles Database Version 1.1 with a suite of environmental GIS data layers including a land cover map, SOTER soil class map, maps of topographic attributes derived from the SRTM-DEM, maps of surface reflectance and vegetation indices derived from satellite imagery. The regression-kriging models were used to predict carbon concentrations, stocks and bulk density at the nodes of a regular grid with 250 meter cell size covering the Tanzania. Prediction uncertainty was quantified and is available with the data as the lower and upper boundary of the 90% prediction interval. Further details about the input data, modelling framework, and cross validation results are provided in a peer-reviewed, scientific journal article. The project was funded by the UN-REDD Programme Output 2.4 “National Maps inform delivery of the REDD+ Framework” and conducted through a letter of Agreement between Food and Agricultural Organization of the United Nations (FAO) and ISRIC-World Soil Information. The maps were produced by ISRIC - World Soil Information in a collaborative effort with the National Soil Survey, Ministry of Natural Resources and Tourism, Tanzania Forest Services, Sokoine University, and the AfSIS project.

A gridded raster of population distribution for the United Republic of Tanzania, published by WorldPop (University of Southampton). Each cell holds a population value for its location, with the data dated to 2030.