Global places data

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

Point locations of named places in Cape Verde, including populated places, streams and other features. Each point records a name, coordinates, feature class and code, country, administrative unit, population and timezone. Published by Orion Terrametrics.

Key geographic reference lines at 1:10 million scale from Natural Earth, including the Equator, Tropics of Cancer and Capricorn, Arctic and Antarctic Circles, and the International Date Line. Use this layer for cartographic reference, climate zone delineation, and geographic education mapping globally. No additional information provided.

Global populated places point dataset at 1:10 million scale from Natural Earth, covering cities, towns, and villages from Tokyo to small settlements. Includes population estimates, administrative codes, and scale rank attributes for cartographic filtering by city size. Use this layer for settlement mapping, demographic analysis, place name labelling, and location-based services globally. No additional information provided.

Point locations of named places in Mozambique, including populated places, islands and streams, with coordinates, administrative divisions, population, elevation and timezone. Each feature represents one named place. Published by Orion Terrametrics.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['place'] IN ('isolated_dwelling', 'town', 'village', 'hamlet', 'city') OR tags['landuse'] IN ('residential') Features may have these attributes: - name - name:en - place - landuse - population - is_in - source - name:no This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: place IN ('isolated_dwelling','town','village','hamlet','city') Features may have these attributes: - population - is_in - place - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

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

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: place IN ('isolated_dwelling','town','village','hamlet','city') Features may have these attributes: - population - is_in - place - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

The "Major Cities" layer is derived from the "World Cities" dataset provided by ArcGIS Data and Maps group as part of the global data layers made available for public use. "Major cities" layer specifically contains National and Provincial capitals that have the highest population within their respective country. Cities were filtered based on the STATUS (“National capital”, “National and provincial capital”, “Provincial capital”, “National capital and provincial capital enclave”, and “Other”). Majority of these cities within larger countries have been filtered at the highest levels of POP_CLASS (“5,000,000 and greater” and “1,000,000 to 4,999,999”). However, China for example, was filtered with cities over 11 million people due to many highly populated cities. Population approximations are sourced from US Census and UN Data. Disclaimer: The designations employed and the presentation of material at this site 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. **Data publication:** 2021-03-12 **Citation:** Credits: ESRI, CIA World Factbook, GMI, NIMA, UN Data, UN Habitat, US Census Bureau **Contact points:** Resource Contact: [ESRI - ArcGIS Data and Maps](mailto:support@esri.ca) Metadata Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** World Cities layer from ArcGIS Data & Maps ArcGIS Data and Maps group background and available datasets.

International place labels based on the M49 “Standard Country or Area Codes for Statistical Use” by the Statistics Division of the United Nations Secretariat. The "Place Labels" layer is derived from the centroids of the 'UN Country Boundaries of the World" polygon dataset to which country names from the United Nations Terminology Database (UNTERM) have been associated. The centroid points are used to move the labels in appropriate locations within the country boundaries. **Data publication:** 2020-11-25 **Citation:** Credits: UN Statistics Division - https://unstats.un.org/unsd/methodology/m49/#fn2 **Contact points:** Resource Contact: [Statistics Division](mailto:statistics@un.org) Metadata Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement The "Place Labels" is derived from the centroids of the 'UN Country Boundaries of the World" polygon dataset. The centroid points are used to move the labels in appropriate locations within the country boundaries. Labels are continuously checked and updated when required. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**

Named settlements from OpenStreetMap (nodes tagged place: city, town, village, hamlet), with population where mapped.

The Places to Watch (PTW) initiative is an automated workflow to identify high-priority areas of deforestation alerts each quarter, based on the intersection of the integrated deforestation alerts with other datasets. There are three separate alert filtering approaches which result in the three PTW types in the legend: Mongabay reporting, Oil Palm, and Soy.   #Mongabay Reporting   These locations were generated by the Places to Watch method which filters the deforestation alerts to those in protected areas and primary forest or intact forest landscapes and identifies the 5x5 km grid cells containing the most alerts in each tropical region. These locations are selected as potential Places to Watch and, after a curation process to identify alert drivers and provide further context, are shared with Global Nature Watch’s “Action Network” of environmental journalists. The goal is to get this information into the hands of journalists who can sound the alarm on new frontiers of deforestation and inspire intervention. These organizations have contact with journalists or activists on the ground, who can then investigate further. If there are articles that are written as a result of sharing the top PTW with the media, the articles are added to the GNW map. The locations and articles on the map are regularly replaced with new articles from more recent curations which may highlight new frontiers of deforestation or provide updates on previous PTW. PTW has proven to be useful in providing a starting point for investigative journalists, contributing to numerous articles reporting on the causes and context of deforestation across the globe. More information on the methodology and curation process involved in Places to Watch is available here: [](    #Places to Watch – Commodities (Oil Palm and Soy)  Similarly, these locations are the result of filtering recent deforestation alerts within key datasets. The PTW – Commodities (PTWC) workflow a descendant of the quarterly, global Places to Watch analysis, which provides inputs for curated stories about emerging deforestation. For PTWC, these alerts are related to current palm and soy plantation areas that are likely to be in noncompliance with zero-deforestation commitments made by large companies if planted with oil palm in Southeast Asia, specifically Indonesia and Malaysia, and if planted with soy in various countries in South America. In particular, the PTWC – Soy  method picks up large clearance events most likely caused by industrial palm concessions or large soy farms. The analysis is an automated process that is executed once a quarter with the curated results displayed as a layer on the Global Nature Watch flagship platform. The main differences between PTWC and global PTW is that PTWC focuses only on potential oil palm expansion in Southeast Asia and potential soy expansion in South America rather than on any deforestation in the entire tropics.   PTWC – Oil Palm: The palm approach filters alerts that intersect either peat, protected peat, or primary forest or intact forest in protected area, and fall within 50 km from palm oil mills or 10 km from oil palm plantations.    PTWC – Soy: The soy approach filters alerts that fall within a 10 km buffer around existing soy plantations.  More information on the Places to Watch – Commodities methodology is available here:  [  ](    If you have additional information to share about a Place to Watch, or are interested in joining our Action Network to provide more information about places near you, please email us at gfw@wri.org.  Sign up here to receive each Places to Watch curation in your email, and share these places on social media using the hashtag #PlacestoWatch!  Identify areas of high-priority deforestation alerts on a quarterly basis and inform investigative journalism. Sign up for the newsletter here.