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

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

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

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.

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.

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

This is an excel mapping tool that was built based on Cuba administrative boundaries (admin2) - extracted from the GADM database (www.gadm.org), version 2.8, November 2015. Available on HDX: https://data.humdata.org/dataset/cuba-administrative-boundaries-levels-0-and-1-from-gadm). The population dataset is a sample data. The tool is built to help people to quickly map their datasets.

NOTE: We plan to no longer update this dataset after May 22 2022.

These data sets are intended to inform researchers and public health experts about how populations are responding to physical distancing measures. In particular, there are two metrics, Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day.

Full details, including the privacy protections in this data, are available here: https://research.fb.com/blog/2020/06/protecting-privacy-in-facebook-mobility-data-during-the-covid-19-response/

The land cover maps published here were produced for four regions of the northern part of Chin state in Myanmar: Hakha, Falam, Tedim and Thantlang. This work was carried out as part of the ALIVE FNS project to monitor land use (forests, cultivated land, built-up areas, etc.). We used the Moringa processing chain, which is based on satellite imagery (Sentinel 2 free of charge time series and SPOT6-7 very high spatial resolution) and a supervised classification algorithm (Random Forest) trained on a reference database made of polygons associated with a land cover class. Generally, this database comes from ground GPS surveys, but it can be replaced by photo-interpretation of very high spatial resolution images if field collection is unavailable or impossible, as it is the case here in the State of Chin. The database was therefore obtained by photo-interpretation of Spot6/7 images acquired as part of the Dinamis programme. The nomenclature includes 4 crop classes (irrigated crops - mainly rice, shifting cultivation, new shifting cultivation, old shifting cultivation) and 6 non-crop classes (open spaces with little or no vegetation, herbaceous vegetation, shrubland, wooded vegetation, water, built-up areas). The maps are available, for the years 2020 and 2021, at a spatial resolution of 1.5 m over the parts covered by SPOT6/7 imagery (approximately half of the study area) and at a spatial resolution of 10m using only Sentinel-2 imagery over the whole area comprising the 4 regions: Hakha, Falam, Tedim, Thantlang. The overall and class accuracies (f-score) of the maps are available in a text file included in the archive containing the maps. Les cartes d'occupation du sol diffusées ici ont été produites sur quatre régions situées au Nord de l’état du Chin au Mynanmar : Hakha, Falam, Tedim, Thantlang. Ces travaux ont été réalisés dans le cadre du projet ALIVE FNS pour observer l'occupation des sols (forêts, terres cultivées, surfaces bâties, etc.). Nous avons utilisé la chaine Moringa qui s'appuie sur l'imagerie satellite (Sentinel 2 et SPOT6-7) et un algorithme de classification supervisée entraîné à partir d'une base de données de référence représentative de l'occupation des sols. Généralement, cette base de données est constituée à partir de relevés GPS sur le terrain, mais elle peut être remplacée par une photo-interprétation sur des images à très haute résolution spatiale si la collecte sur le terrain n'est pas disponible ou impossible, comme c'est le cas ici dans l'État de Chin. La base de données a donc été obtenue par photo-interprétation d’images Spot56/7 acquises dans le cadre du dispositif Dinamis. La nomenclature comprend 4 classes de cultures (cultures irriguées - principalement le riz, cultures itinérantes, nouvelles cultures itinérantes, anciennes cultures itinérantes) et 6 classes de non-cultures (espaces ouverts avec peu ou pas de végétation, végétation herbacée, zones arbustives, végétation boisée, eau, surfaces bâties). Les cartes sont disponibles, pour les années 2020 et 2021, à une résolution spatiale de 1,5m sur les parties couvertes par l'imagerie SPOT6/7 (non gratuites) et à une résolution spatiale de 10m utilisant uniquement des images Sentinel-2 (gratuites) sur une zone plus grande comprenant l’ensembles dans 4 régions : Hakha, Falam, Tedim, Thantlang. Les précisions globales et par classes des cartes sont disponible dans un fichier texte inclus dans l’archive contenant les cartes.

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. Malaysia data available from WorldPop here.

Countries from Natural Earth 50M scale data with a Human Development Index attribute for each of the following years: 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2013, 2015, & 2017. The Human Development Index measures achievement in 3 areas of human development: long life, good education and income. Specifically, the index is computed using life expectancy at birth, Mean years of schooling, expected years of schooling, and gross national income (GNI) per capita (PPP $). The United Nations categorizes the HDI values into 4 groups. In 2013 these groups were defined by the following HDI values: Very High: 0.736 and higher High: 0.615 to 0.735 Medium: 0.494 to 0.614 Low: 0.493 and lower

In 2015 & 2017 these groups were defined by the following HDI values: Very High: 0.800 and higher High: 0.700 to 0.799 Medium: 0.550 to 0.699 Low: 0.549 and lower

Human Development Index attributes are from The World Bank: HDRO calculations based on data from UNDESA (2013a), Barro and Lee (2013), UNESCO Institute for Statistics (2013), UN Statistics Division(2014), World Bank (2014) and IMF (2014). 2015 & 2017 values source: HDRO calculations based on data from UNDESA (2017a), UNESCO Institute for Statistics (2018), United Nations Statistics Division (2018b), World Bank (2018b), Barro and Lee (2016) and IMF (2018).

Population data are from (1) United Nations Population Division. World Population Prospects, (2) United Nations Statistical Division. Population and Vital Statistics Report (various years), (3) Census reports and other statistical publications from national statistical offices, (4) Eurostat: Demographic Statistics, (5) Secretariat of the Pacific Community: Statistics and Demography Programme, and (6) U.S. Census Bureau: International Database.

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

VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Egypt: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click here For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

Based on open access data, 79 Mediterranean passenger ports are analyzed to compare their infrastructure, hinterland accessibility and offered multi-modality categories. Comparative Geo-spatial analysis is also carried out by using the data normalization method in order to visualize the ports' performance on maps. These data driven comprehensive analytical results can bring added value to sustainable development policy and planning initiatives in the Mediterranean Region. The analyzed elements can be also contributed to the development of passenger port performance indicators. The empirical research methods used for the Mediterranean passenger ports can be replicated for transport nodes of any region around the world to determine their relative performance on selected criteria for improvement and planning.

The Mediterranean passenger ports were initially categorized into cruise and ferry ports. The cruise ports were identified from the member list of the Association for the Mediterranean Cruise Ports (MedCruise), representing more than 80% of the cruise tourism activities per country. The identified cruise ports were mapped by selecting the corresponding geo-referenced ports from the map layer developed by the European Marine Observation and Data Network (EMODnet). The United Nations (UN) Code for Trade and Transport Locations (LOCODE) was identified for each of the cruise ports as the common criteria to carry out the selection. The identified cruise ports not listed by the EMODnet were added to the geo-database by using under license the editing function of the ArcMap (version 10.1) geographic information system software. The ferry ports were identified from the open access industry initiative data provided by the Ferrylines, and were mapped in a similar way as the cruise ports (Figure 1).

Based on the available data from the identified cruise ports, a database (see Table A1–A3) was created for a Mediterranean scale analysis. The ferry ports were excluded due to the unavailability of relevant information on selected criteria (Table 2). However, the cruise ports serving as ferry passenger ports were identified in order to maximize the scope of the analysis. Port infrastructure and hinterland accessibility data were collected from the statistical reports published by the MedCruise, which are a compilation of data provided by its individual member port authorities and the cruise terminal operators. Other supplementary sources were the European Sea Ports Organization (ESPO) and the Global Ports Holding, a cruise terminal operator with an established presence in the Mediterranean. Additionally, open access data sources (e.g. the Google Maps and Trip Advisor) were consulted in order to identify the multi-modal transports and bridge the data gaps on hinterland accessibility by measuring the approximate distances.

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia 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 on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

As part of THEIA (the French Data and Services center for continental surfaces) 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 all distributed on CIRAD's spatial data catalogue in Réunion: http://aware.cirad.fr/ This Dataverse entry concerns the maps produced, for the year 2017, 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 distinguishing crop types has an overall accuracy of 86% 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 0.94. 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) et sont toutes diffusées sur 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 2017, 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 le plus détaillé distinguant les types de cultures présente une précision globale de 86% 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 0,94. Une fiche détaillée présentant la méthode et les résultats de validation est téléchargeable.

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

This site complements PSA OpenSTAT portal allowing data users to visualize data on maps. Users can also explore and download published data, discover and build web maps and apps, and analyze and combine datasets using maps.This site is under construction and data published here are not official!

The United Nations Sustainable Development Goals (SDGs) challenge the global community to build a world where no one is left behind.

Since 2018, Elsevier has generated SDG search queries to help researchers and institutions track and demonstrate progress toward the SDG targets. In the past 5 years, these queries, along with the university’s own data and evidence supporting progress and contributions to the particular SDG outside of research-based metrics, are used for the THE Impact Rankings.

For 2023, the SDGs use the exact same search query and ML algorithm as the Elsevier 2022 SDG mappings, with only minor modifications to five SDGs, namely SDG 1, 4, 5, 7 and 14. In these cases, the queries were shortened by removing exclusion lists based on journal identifiers. These exclusion lists often contained thousands of items to filter out content in journals that were not core to the SDGs.

To replicate the effect of these journal exclusions, sets of keywords were used to closely mimic the effects the journal exclusions had on the SDG content, while greatly reducing the overall query size and complexity. By following this approach, we were able to limit the changes to the publications in each SDG by less than 2 percent for most SDGs, while reducing the query size by 50 percent or more.

These shortened queries also have the added benefit of running faster in Scopus, allowing further analysis of the SDG data to be done more easily.

For each SDG, the full search query, along with further details about the top keyphrases, subfields, journals and keyphrases are available for download.