Cote D Ivoire GIS data

geoBoundaries ADM2 boundaries for Côte d'Ivoire: 33 units, representing 2016. Source: World Bank.

geoBoundaries ADM1 boundaries for Côte d'Ivoire: 14 units, representing 2016. Source: World Bank Group, OCHA Côte d’Ivoire.

geoBoundaries ADM0 boundaries for Côte d'Ivoire: 1 units, representing 2018. Source: Natural Earth.

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

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.

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Côte d'Ivoire. 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

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Côte d'Ivoire. 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 driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other 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

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 Côte d'Ivoire **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.1795 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0186 million km (10.3493%) - Unpaved roads: 0.1288 million km (71.7843%) About 0.0321 million km (17.8665%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (0.078%) 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

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access 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: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation 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: highway - Part of the country exports on HDX by HOT

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. 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: waterway, water, natural - Part of the country exports on HDX by HOT

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

Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. 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 - Part of the country exports on HDX by HOT

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

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. 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: railway - Part of the country exports on HDX by HOT

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. 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: place, landuse - Part of the country exports on HDX by HOT

Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. 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: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. 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, building - Part of the country exports on HDX by HOT

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban 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: building - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. 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, healthcare - Part of the country exports on HDX by HOT

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

Vector dataset representing optimal and recommended locations for cage farming systems in the Republic of Côte d'Ivoire, based on top suitability scores classified into two percentile ranges: 50th–70th and above the 70th percentile. Produced under FAO’s Hand-in-Hand Initiative using Geographical Information Systems (GIS) and Multi-Criteria Evaluation (MCE) suitability assessments. Intensive systems are characterized by high fish densities and the use of artificial feed. Considered locations for cage systems require permanent waters with a minimum surface of 5 acres. The score is determined by processing outputs from sub-models that describe biophysical, infrastructure, and demand factors: Criteria considered: a. Market accessibility - population and asset wealth index in urban areas. b. Crop input - availability of agricultural by-products. c. Livestock input - based on the weighted animal density aggregation. The score is calculated as a simple arithmetic weighted sum of normalized or scaled grids, theoretically ranging from 0 to 100: (“Accessibility MajorUrbanAreas” X 0.50) + (“CropsInput” X 0.25) + (“LivestockInput” X 0.25) **Data creation:** 2025-12-15 **Data publication:** 2026-01-13 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** Data sources, FAO Hand-in-Hand Geospatial Platform and OpenStreetMap (open data) including the following datasets: a. Market Accessibility (urban areas) - OpenStreetMap and Atlas AI - Population Density (Africa, 2020) - Estimated total number of people per grid-cell 1km. (Urban areas = pop density>1500 habitants/km² AND area>25km²) b. Input crops - IFPRI Global Spatially-Disaggregated Crop Production Statistics Data for 2017 (MAPSPAM) - https://data.apps.fao.org/map/catalog/srv/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a c. Input livestock – GLW4 (https://data.apps.fao.org/catalog/iso/15f8c56c-5499-45d5-bd89-59ef6c026704) : weighted animal density aggregate d. Global Surface Water 1984-2021 (European Commission Joint Research Centre) https://global-surface-water.appspot.com/ **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**

This dataset maps Côte d’Ivoire’s road network—including highways, paved roads, unpaved roads, and rural tracks—to support connectivity and market‑access analysis within the FAO Hand‑in‑Hand agropoles (Centre, Nord, Nord‑Est). The information helps assess transport efficiency, identify infrastructure gaps, and guide agricultural investment planning. **Data creation:** 2026-01-12 **Data publication:** 2026-07-08 **Supplemental Information:** Produced under the TCP/IVC/3908 geospatial study, this dataset complements thematic layers used in the Hand‑in‑Hand platform, supporting accessibility assessments, value‑chain diagnostics, and territorial planning in Côte d’Ivoire. **Citation:** FAO. [YYYY (year of last update)]. [Name of database: Name of dataset OR Name of database]. [Accessed on [DD Month YYYY]]. [URL] Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [FAO-Data](mailto:FAO.data@fao.org) Responsible Party: [Nelson Ribeiro](mailto:nelson.rosasribeirofilho@fao.org) **Data lineage:** Road features were compiled from national sources, institutional datasets, field information, and openly available geospatial data. Data were harmonized and validated within the TCP/IVC/3908 study and standardized to EPSG:4326 following FAO GIS practices. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Zipped vector SHP file for "Road Network FAO Hand‑in‑Hand Agropoles – TCP/IVC/3908 (Côte d’Ivoire)"

This dataset provides authoritative boundaries for protected areas within the FAO TCP/IVC/3908 Area of Interest in Côte d’Ivoire, covering the Centre, Nord, and Nord‑Est agropoles (approx. 134,614 km², ~41.75% of national territory). It identifies 84 protected areas—82 classified forests and 2 national parks: Parc national de la Comoé (Bounkani Region) and Parc national d’Abokouamékro (Yamoussoukro Autonomous District). Designed to support Hand‑in‑Hand investment targeting and land‑use planning, the data enable risk‑informed rural development while safeguarding critical ecosystems and restoration zones. Intended uses include project siting, due diligence, and policy analysis across agriculture, infrastructure, and conservation. **Data creation:** 2025-08-07 **Data publication:** 2026-07-08 **Supplemental Information:** Objective: support prioritized investment planning tied to food security, climate resilience, and wealth creation, using advanced geospatial analytics and partnerships under FAO’s Hand‑in‑Hand approach. **Citation:** FAO. [YYYY (year of last update)]. [Name of database: Name of dataset OR Name of database]. [Accessed on [DD Month YYYY]]. [URL] Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [FAO-Data](mailto:FAO.data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:nelson.rosasribeirofilho@fao.org) **Data lineage:** Boundaries compiled and validated within the TCP/IVC/3908 geospatial study for Côte d’Ivoire’s agropoles; integrated with thematic layers on vegetation/land cover and planning baselines developed in the study, Rapport de l’étude SIG: Contribution à l’opérationnalisation des agropoles à travers la mise en œuvre de l’initiative "Main dans la Main en Côte d’Ivoire" (TCP/IVC/3908). **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Zipped vector SHP file for "Protected Areas within FAO Hand‑in‑Hand Agropoles - TCP/IVC/3908 (Côte d’Ivoire)"

Locations of seaports, harbours and maritime infrastructure in Cote dIvoire. 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.

geoBoundaries ADM3 boundaries for Côte d'Ivoire: 510 units, representing 2021. Source: Comité National de Télédétection et d'Information Géographique (CNTIG), OCHA ROWCA.

Water resources and water infrastructure in Cote dIvoire 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.

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. Cote d'Ivoire 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 District Department

First-level administrative divisions of Cote dIvoire such as regions, provinces or states. Use this layer for regional analysis, planning, resource allocation, and thematic mapping at the provincial scale.

Côte d'Ivoire administrative level 0-3 boundaries (COD-AB) dataset version 01. This dataset is structured into 3 levels: - Admin 1: 33 District, Autonomous City - Admin 2: 108 Region - Admin 3: 510 Department Dates associated with this dataset: - 30 October 2025: dataset reviewed for accuracy and completeness - 06 July 2018: valid for use by the humanitarian community - 06 August 2018: last edit to the dataset before publishing - 06 August 2018: 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)

A raster of population for Côte d'Ivoire for 2020. Each cell value represents the estimated number of people in that cell. Published by Orion Terrametrics.