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. Credits: ESRI, CIA World Factbook, GMI, NIMA, UN Data, UN Habitat, US Census Bureau 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.

The full resolution dataset of towns was developed from data from the National Imagery and Mapping Agency (NIMA).

General point features, robust derivative of VMap0 - Ed5 data layers with harmonized encoding. The GNFTR_PT shapefile data layer is comprised of 17136 derivative vector framework library features derived based on 1:1 000 000 data originally from VMap0, 5th Edition. The layer provides nominal analytical/mapping at 1:1 000 000. Data processing complete globally, this is an African subset. Acronyms and Abbreviations: VMap0 - Vector Map for Level 0.

The full resolution dataset of towns was developed from data from the National Imagery and Mapping Agency (NIMA).

The full resolution dataset of towns was developed from data from the National Imagery and Mapping Agency (NIMA).

Point based representation of populated place with encoding differentiating national and subnational capitals and other related administrative centres. The RWDB_PPL shapefile data layer is comprised of 55038 derivative vector framework library features derived based on 1:3 000 000 data originally from RWDBII. The layer provides nominal analytical/mapping at 1:3 000 000. Data processing complete globally. Acronyms and Abbreviations: RWDB2 or RWDB II- Relational World Database II.

Seamless and topologically robust derivative of VMap0 - Ed5 data layers. The PPL_PY shapefile data layer is comprised of 1505 derivative vector framework library features derived based on 1:1 000 000 data originally from VMap0, 5th Edition. The layer provides nominal analytical/mapping at 1:1 000 000. Data processing complete globally, this is an African subset. Acronyms and Abbreviations: VMap0 - Vector Map for Level 0.

Robust derivative of VMap0 - Ed5 data layers with harmonized encoding. The CAPTL_PT shapefile data layer is comprised of 55 derivative vector framework library features derived based on 1:1 000 000 data originally from VMap0, 5th Edition. The layer provides nominal analytical/mapping at 1:1 000 000. Data processing complete globally, this is an African subset. Acronyms and Abbreviations: VMap0 - Vector Map for Level 0.

Full value-added derivative of ASCII GEOnet January 2006 gazetteer baseline, including non-diacritical names. ASCII is American Standard Code for Information Interchange. The GNS_GZTR shapefile data layer is comprised of 1083354 derivative point gazetteer features derived based on 1:250 000 data originally from GEOnet. The layer provides nominal analytical/mapping at 1:250 000. Data processing is complete globally, this is an African subset. Acronyms and Abbreviations: ASCII - American Standard Code for Information Interchange; GNS/GeoNet - NIMA?s Geographic Names Server Gazetteer of Named Locations.

The full resolution dataset of towns was developed from data from the National Imagery and Mapping Agency (NIMA).