Ghana administrative level 0-2 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE: See COD-PS caveats about metropolitan feature consolidation

This COD-AB was most recently reviewed for accuracy and necessary changes in October 2024. The COD-AB does not require any update.

Sourced from Ghana Statistical Services (GSS)

Live geoservices (provided by Information Technology Outreach Services (ITOS) with funding from USAID) are available for this COD-AB. Please see COD_External. (For any earlier versions please see here, here, and here.) Vetting, configuration, and geoservices provision by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Ghana COD-PS.

An edge-matched (COD-EM) version of this COD-AB is available on HDX here.

Please see the COD Portal.

Administrative level 1 contains 16 feature(s). The normal administrative level 1 feature type is ""currently not known"".

Administrative level 2 contains 260 feature(s). The normal administrative level 2 feature type is ""currently not known"".

Recommended cartographic projection: Africa Albers Equal Area Conic

This metadata was last updated on January 13, 2025.

Ghana 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. They are sourced from GhanaPost 2018. These were published in October 2023. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Country District

Download high-quality, up-to-date Ghana shapefile boundaries (SHP, projection system SRID 4326). Our Ghana Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

This is the approved and updated districts boundary of Ghana released by the Ghana Statistical Services

The Map Library is a project of the UK charity “The Map Maker Trust." It aims to make public domain mapping data more readily available for development in all kinds of fields, from health care to urban management, from de-mining to environmental protection.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Ghana data available from WorldPop here.

This dataset represents the second-level administrative unit 'Districts' of Ghana. The dataset was uploaded by the Lands Commission of Ghana in October 2021 to Second Administrative Level Boundaries (SALB) Program of United Nations. The reference scale for this Geospatial data is equivalent to 1:1,000,000 scale, or larger scale. Data was downloaded from UN-SALB site in September 2022, feature topology/geometry was corrected, international borders validated against the United Nations official borders (United Nations Geospatial Information Section - UN-Map 2018). The dataset is part of FAO's Hand-in-Hand (HiH) second administrative level boundaries 2022 dataset series, published on the HiH Geospatial Platform for thematic mapping, integration of geospatially enabled statistics, zonal statistics extraction, and used for HiH initiative geospatial analysis (GIS-MCDA, suitability/location analysis, agricultural typologies).

This dataset was originally obtained from the Ghana Statistical Service. Extracts were made from the data to meet the objective of our project which sought to assess women’s access to water in Ghana. The data comes in three formats. One is in the SPSS format whereas the other is in Stata format. The third dataset is a shapefile which is the district boundary merged with the extracted data for spatial analysis.

This map shows the average household size in Ghana in 2023, in a multiscale map (Country, Region, and District). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by 15-year age increments The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.2043 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0251 and 0.035 (in million kms), corressponding to 12.2772% and 17.1205% respectively of the total road length in the dataset region. 0.1443 million km or 70.6023% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0012 million km of information (corressponding to 0.83% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

The Administrative Boundaries used by the Data in Emergencies Hub are the result of a collection of international and subnational divisions currently used by FAO country offices for mapping and reporting purposes. With only a few exceptions, they are mostly derived from datasets published on The Humanitarian Data Exchange (OCHA).The dataset consists of national boundaries, first subdivision, and second subdivision for Sure! Here's the reformatted list as requested:

Afghanistan, Angola, Bangladesh, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Democratic Republic of the Congo, Ecuador, El Salvador, Federated States of Micronesia, Ghana, Guatemala, Haiti, Honduras, Iraq, Kingdom of Tonga, Kiribati, Kyrgyzstan, Lao People's Democratic Republic, Lebanon, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mozambique, Myanmar, Namibia, Nepal, Niger, Nigeria, Pakistan, Palestine, Philippines, Republic of the Marshall Islands, Saint Lucia, Samoa, Senegal, Sierra Leone, Solomon Islands, Somalia, South Sudan, Sri Lanka, Sudan, Suriname, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Togo, Tuvalu, Uganda, Ukraine, Venezuela, Vietnam, Yemen, and Zimbabwe.In the Feature Layer, the administrative boundaries are represented by closed polygons, administrative levels are nested and multiple distinct polygons are represented as a single record.The Data in Emergencies Hub team is responsible for keeping the layer up to date, so please report any possible errors or outdated information.The boundaries and names shown and the designations used on these map(s) do not imply the expression of any opinion whatsoever on the part of FAO concerning the legal status of any country, territory, city, or area or of its authorities, or concerning the delimitation of its frontiers and boundaries. Dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The final boundary between the Sudan and South Sudan has not yet been determined. The final status of the Abyei area is not yet determined. The dotted line represents approximately the Line of Control in Jammu and Kashmir agreed upon by India and Pakistan. The final status of Jammu and Kashmir has not yet been agreed upon by the parties.

Agronomic Fertilizer Use Efficiency (WUE) for maize biomass in Ashanti region and Brong-Ahafo region in Ghana has been estimated under three different fertilizer application rates using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. Quality/Lineage: The Agronomic fertilizer use efficiency in maize biomass under three different fertilizer application rates, plotted in the map was calculated as the average values of 19 years (the year 1992 -2010). The unit is kilogram per Kilogram (kg/kg). Agronomic fertilizer Use Efficiency (FUE)= (Maize biomass under fertilized condition - Maize biomass under unfertilized condition) (in kg)/ Fertilizer application rate (in kg) The climate data at the national scale was made available from the Terrestrial Hydrology Research Group, Princeton University (http://hydrology.princeton.edu/data.php) at a resolution of 10 km x 10 km. The dataset was constructed by combining a suite of global observation-based datasets with the NCEP/NCAR reanalysis. Soil parameter values were extracted from the soil property maps of Africa at 1 km x 1 km resolution (http://www.isric.org/data/soil-property-maps-africa-1-km). Purpose: To increase food production, identifying the regions with untapped production capacity is of prime importance and can be achieved by quantitative and spatially explicit estimates of Agronomic Fertilizer use efficiency, thus considering the spatial variation in environment and the production system.

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

Agronomic Radiation Use Efficiency (RUE) for maize biomass in Ashanti region and Brong-Ahafo region in Ghana has been estimated under three different fertilizer application rates using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. Purpose: To increase food production, identifying the regions with untapped production capacity is of prime importance and can be achieved by quantitative and spatially explicit estimates of Agronomic Radiation use efficiency, thus considering the spatial variation in environment and the production system.

Water Use Efficiency (WUE) for maize biomass in Ashanti region and Brong-Ahafo region in Ghana has been estimated under three different fertilizer application rates using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management.