United Nations map (known as UNmap) is a worldwide geospatial database consisting of country and geographic name information on a global scale. The data is designed for the production of cartographic documents and maps, including their dissemination via public electronic networks, for the Secretariat of the United Nations.The United Nations maintains the Data as a courtesy to those who may choose to access the Data. The Data is provided “as is”, without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose and non-infringement.

Disclaimers: - The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations. - The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. - 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. - Final boundary between the Republic of Sudan and the Republic of South Sudan has not yet been determined. - Final status of the Abyei area is not yet determined. - A dispute exists between the Governments of Argentina and the United Kingdom of Great Britain and Northern Ireland concerning sovereignty over the Falkland Islands (Malvinas).

Generalization parametrisation for the data is developed based on the work of Douglas and Peucker (1973), Wang (1996) and the Polynomial Approximation with Exponential Kernel algorithm.The adequate generalized data should be used for the intended dissemination scale and not rely on software or platform-automated generalization as some specific geographic features are removed at scales. For instance, the region of Abyei is not included at the scale of 1:25 million but is included at lower scales.

Maps produced using this layer should be featured with the appropriate disclaimer depending on the shown area.

Source: United Nations International and Administrative Boundaries Resources

International boundaries provided by United Nations Clear Map. The United Nations Clear Map (hereinafter “Clear Map”) is a background reference web mapping service produced to facilitate “the issuance of any map at any duty station, including dissemination via public electronic networks such as Internet” and “to ensure that maps meet publication standards and that they are not in contravention of existing United Nations policies” in accordance with the in the Administrative Instruction on “Regulations for the Control and Limitation of Documentation – Guidelines for the Publication of Maps” of 20 January 1997 (http://undocs.org/ST/AI/189/Add.25/Rev.1) Clear Map is created for the use of the United Nations Secretariat and community. All departments, offices and regional commissions of the United Nations Secretariat including offices away from Headquarters using Clear Map remain bound to the instructions as contained in the Administrative Instruction and should therefore seek clearance from the UN Geospatial Information Section (formerly Cartographic Section) prior to the issuance of their thematic maps using Clear Map as background reference. Disclaimers: The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Credits (Attribution) Produced by: United Nations Geospatial Contributor: UNGIS, UNGSC, Field Missions CONTACT US: Your feedback is appreciated and should be sent directly to: Email:Clearmap@un.org / gis@un.org (UNCLASSIFIED) © UNITED NATIONS 2018 More information on the United Nations Clear Map website at https://geoportal.dfs.un.org/arcgis/sharing/rest/content/items/541557fd0d4d42efb24449be614e6887/data

This is a PDF format map of the country, as released by the United Nations.

This is a PDF format map of the country, as released by the United Nations.

The United Nations Geospatial Data, or Geodata, is a worldwide geospatial dataset of the United Nations. The United Nations Geodata is provided to facilitate the preparation of cartographic materials in the United Nations includes geometry, attributes and labels to facilitate the adequate depiction and naming of geographic features for the preparation of maps in accordance with United Nations policies and practices. The geospatial dataset include polygons/areas of countries (BNDA_simplified). Please refer this page for more information.

This is a PDF format map of the country, as released by the United Nations.

Hand-in-Hand has brought together over 20 FAO units across multiple domains, from Animal Health to Trade and Markets, integrating data from across FAO on Soil, Land, Water, Climate, Fisheries, Livestock, Crops, Forestry, Trade, Social and Economics, etc.Data has also been sourced from FAO partners and public data providers across the UN and NGOs, private sector and space agencies.

The tls_all_background dataset provides comprehensive geospatial information for Timor-Leste. This dataset is sourced from bkk_tls_data. It is designed to support a wide range of applications, including urban planning, environmental monitoring, and infrastructure development. The dataset offers detailed background mapping to ensure accuracy and reliability for projects requiring geospatial data in this area. For additional context and technical details, please refer to the linked repository.

UNOSAT code: FL20241022SEN This application provides geospatial information regarding the floods in Senegal

Important note: The boundaries and names shown, and the designations used on this map do not imply official endorsement or acceptance by the United Nations. The United Nations Satellite Centre. UNOSAT is not responsible for the misuse or misrepresentation of the map.

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

Worldwide geospatial database consisting of country and geographic name information on a global scale. The data is designed for the production of cartographic documents and maps, including their dissemination via public electronic networks, for the Secretariat of the United Nations, as set forth in the Administrative Instruction of the Secretary-General of the United Nations concerning. Source: UNmap The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. 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. Final boundary between the Republic of Sudan and the Republic of South Sudan has not yet been determined. Final status of the Abyei area is not yet determined. A dispute exists between the Governments of Argentina and the United Kingdom of Great Britain and Northern Ireland concerning sovereignty over the Falkland Islands (Malvinas).

This is a PDF format map of the country, as released by the United Nations.

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.

The Country boundaries layer at level 0 (National level) is part of the Global Administrative Unit Layers (GAUL) version 2 dataset series, produced by FAO from 2022 to 2024 in the framework of the Hand-in-Hand Initiative and the Geospatial Data Platform activities.

The dataset sourced and processed from the United Nations Second Administrative Level Boundaries (UN-SALB) programme, was complemented by the UN-FAO-CSI AgroInformatics Geospatial Analysis team with data from official geospatial data producers and finally processed against UN official recognized borders (UN-map 2018).

The GAUL v.2 aims at maintaining global layers with a unified coding system at country, first (e.g. departments), and second (e.g. districts) administrative levels.

For more information on GAUL v.2, please refer to the metadata of the Global Administrative Unit Layers v. 2 (GAUL 2024).

This dataset is available for download in both vector and tabular formats.

Data publication: 2024-07-29

Contact points:

Resource Contact: Food and Agriculture Organization of the United Nations

Metadata Contact: FAO-Data

Data lineage:

Country boundaries were processed against UN official recognized borders (UN-map 2018), and the administrative subdivisions were checked for geometry and topology, then corrected and validated.

Resource constraints:

The GAUL license is subject to the constraints and limitations of the data license of the International Boundary Dataset of the UN Cartographic Section and the one of the Second Administrative Level Boundary (SALB) data (for the countries that use this dataset).

Terms of Use

The GAUL dataset is distributed to the United Nations and other authorized international and national institutions/agencies. FAO grants a license to use, download and print the materials contained in the GAUL dataset solely for non-commercial purposes and in accordance with the conditions specified in the data license.

Online resources:

Switzerland's accession to the United Nations. Map types: Lines, Choropleths. Spatial extent: Switzerland. Time: 2002. Spatial units: Cantons, Communes

The FGGD suitability map using maximising crop and technology mix is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains the suitability index for rainfed crops, using maximising crop and technology mix, 2005 version. The method is described in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

The product data are six statistics that were estimated for the chemical concentration of lithium in the soil C horizon of the conterminous United States. The estimates are made at 9998 locations that are uniformly distributed across the conterminous United States. The six statistics are the mean for the isometric log-ratio transform of the concentrations, the equivalent mean for the concentrations, the standard deviation for the isometric log-ratio transform of the concentrations, the probability of exceeding a concentration of 55 milligrams per kilogram, the 0.95 quantile for the isometric log-ratio transform of the concentrations, and the equivalent 0.95 quantile for the concentrations. Each statistic may be used to generate a statistical map that shows an attribute of the distribution of lithium concentration.

CIRAD's TETIS research unit is developing an automated mapping method based on the Moringa chain that minimizes interactions with users by automating most image analysis and processing. The methodology uses jointly a Very High Spatial Resolution image (Spot6/7 or Pleiades) and one or more time series of High Spatial Resolution optical images such as Sentinel-2 and Landsat-8 for a classification combining segmentation and object classification (use of the Random Forest algorithm) driven by a learning database constituted from in situ collection and photo-interpretation. The land use maps are produced as part of the GABIR project (Gestion Agricole des Biomasses à l'échelle de l'Ile de la Réunion) and are downloadable below or on CIRAD's spatial data catalogue in Réunion: http://aware.cirad.fr/ This Dataverse entry concerns the maps produced, for the year 2018, using a mosaic of Pleiades images to calculate segmentation (extraction of homogeneous objects from the image). We use a field database with a nested nomenclature with 3 levels of accuracy allowing us to produce a classification by level. The most detailed level 3 distinguishing crop types has an overall accuracy of 87% and a Kappa index of 0.85. Level 2, distinguishing crop groups, has an overall accuracy of 92% and a Kappa index of 0.90. Level 1, distinguishing major land use groups, has an overall accuracy of 97% and a Kappa index of 0.95. A detailed sheet presenting the validation method and results is available for download. Dans le cadre du Centre d’Expertise Scientifique Occupation des Sols de THEIA, l’UMR TETIS du CIRAD développe une méthode de cartographie automatisée fondée sur la chaine Moringa qui minimise les interactions avec les utilisateurs par l’automatisation de la plupart des processus d’analyse et de traitement des images. La méthodologie utilise conjointement une image à Très Haute Résolution Spatiale (Spot6/7 ou Pléiades) et une ou plusieurs séries temporelles d’images optiques à Haute Résolution Spatiale type Sentinel-2 et Landsat-8 pour une classification combinant segmentation et classification objet (utilisation de l’algorithme Random Forest) entrainée par une base de données d’apprentissage constituée à partir de collecte in situ et de photo-interprétation. Les cartes d'occupation du sol sont réalisées dans le cadre du projet GABIR (Gestion Agricole des Biomasses à l’échelle de l'Ile de la Réunion). Elles sont téléchargeables ci-dessous ou via le catalogue de données spatiales du Cirad à la Réunion : http://aware.cirad.fr/ Cette fiche du Dataverse concerne les cartes produites, pour l'année 2018, en utilisant une mosaïque d'images Pléiades pour calculer la segmentation (extraction d'objets homogènes à partir de l'image). Nous utilisons une base de données terrain ayant une nomenclature emboitée avec 3 niveaux de précision nous permettant de produire une classification par niveau. Le niveau 3, le plus détaillé distinguant les types de cultures présente une précision globale de 87% et un indice de Kappa est de 0,85. Le niveau 2, distinguant les groupes de cultures présente une précision globale de 92% et un indice de Kappa est de 0,90. Le niveau 1, distinguant les grands groupes d'occupation du sol présente une précision globale de 97% et un indice de Kappa est de 0,95.

Boundaries of the World.The boundaries, and names shown and designations used on maps, tables, documents, and databases do not imply official endorsement or acceptance by the United Nations.The boundaries dataset is suitable at the scale of 1 : 1 million.

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. Palestine data available from WorldPop here.