This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100 m grid cells) with national coverage for Nigeria, along with estimates of the number of people belonging to various age-sex groups. Version 2.0 is an update of the previous version 1.2 gridded population estimates and is based on more recent and detailed settlement information and a different regional boundary definition. These model-based population estimates most likely represent the time period around 2019, corresponding to the period when the satellite imagery was processed to generate building footprints. Populations are mapped only in areas where residential settlements are predicted.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

The data were produced by WorldPop at the University of Southampton. These data include gridded population estimates, at approximately 100m resolution, for 40 countries in Latin America and the Caribbean (Appendix A). These results were created using official population estimates at the finest-available resolution provided by National Statistic Offices (NSOs) throughout the region, and built-up area, height and volume covariates produced from World Settlement Footprint 3D (WSF3D) datasets1. We acknowledge the contribution of WorldPop’s partners, notably the United Nations Population Fund (UNFPA) Latin America and Caribbean Regional Office in supporting the collection of population and administrative boundary data, and to the German Aerospace Center (DLR) for preparing and providing built settlement data from the WSF3D framework. Modelling work and geospatial data processing was carried out by McKeen T., Bondarenko M., Kerr D. and Sorichetta A. Esch T., Marconcini M., Zeidler J. and Palacios-Lopez D. prepared and provided the WSF3D datasets. Juran S. and Valle C. aided with population and administrative boundary data collection. Oversight was provided by Andrew J. Tatem fourth and final part.

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the United Nations Children's Fund (UNICEF) - Population Modelling for use in Routine Health Planning and Monitoring project (contract no. 43335861). Projects partners included the Kenya Unicef Regional and Country Offices, WorldPop research group at the University of Southampton and the Center for International Earth Science Information Network in the Columbia Climate School at Columbia University. Assane Gadiaga (WorldPop) led the input processing and the modelling work following the Random Forest (RF)-based dasymetric mapping approach developed by Stevens et al. (2015). Thomas Abbott supported the covariates processing work. In-country engagements were done by David Kyalo, Olena Borkovska (GRID3 Inc), Maria Muniz (Unicef). Using the 2009 and 2019 census data from the Kenya’s National Bureau of Statistics (KNBS), the US Census Bureau released the census-based total population projections, population by age and sex and digital sub-counties boundaries. Duygu Cihan helped in the preparation of these input population data. Attila N Lazar, Edith Darin and Heather Chamberlain advised on the modelling procedure. The work was overseen by Attila N Lazar and Andy J Tatem.

Chlamydia trachomatis is the most common sexually transmitted infection (STI) in England. Our objective was to perform a detailed survey of the molecular epidemiology of C. trachomatis in the population of Southampton UK attending the genitourinary medicine clinic (GUM) to seek evidence of sexual network activity. Our hypothesis was that certain genotypes can be associated with specific demographic determinants. 380 positive samples were collected from 375 C. trachomatis positive GUM attendees out of the 3118 who consented to be part of the survey. 302 of the positive samples were fully genotyped. All six of the predominant genotypes possessed ompA locus type E. One ward of Southampton known to contain a large proportion of students had a different profile of genotypes compared to other areas of the city. Some genotypes appeared embedded in the city population whilst others appeared transient. Predominant circulating genotypes remain stable within a city population whereas others are sporadic. Sexual networks could be inferred but not conclusively identified using the data from this survey.

London was by far the largest urban agglomeration in the United Kingdom in 2023, with an estimated population of 9.65 million people, more than three times as large as Manchester, the UK’s second-biggest urban agglomeration. The agglomerations of Birmingham and Leeds / Bradford had the third and fourth-largest populations respectively, while the biggest city in Scotland, Glasgow, was the fifth largest. Largest cities in Europe Two cities in Europe had larger urban areas than London, with the Russian capital Moscow having a population of almost 12.7 million. The city of Paris, located just over 200 miles away from London, was the second-largest city in Europe, with a population of more than 11.2 million people. Paris was followed by London in terms of population-size, and then by the Spanish cities of Madrid and Barcelona, at 6.75 million and 5.68 million people respectively. Russia's second-biggest city; St. Petersburg had a population of 5.56 million, followed by Rome at 4.3 million, and Berlin at 3.5 million. London’s population growth Throughout the 1980s, the population of London fluctuated from a high of 6.81 million people in 1981 to a low of 6.73 million inhabitants in 1988. During the 1990s, the population of London increased once again, growing from 6.8 million at the start of the decade to 7.15 million by 1999. London's population has continued to grow since the turn of the century, reaching a peak of 8.96 million people in 2019, and is forecast to reach 9.8 million by 2043.

Gridded population with 100m spaital resolution of the 34 key areas along One Belt One Road in 2015, which indicates that the population count per pixel (i.e., grid). This data is derived from geodata institute of Southampton University, UK. The prejection transform and extraction processes were done to generate the gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2015. The original gridded popution is spatially downscaled from census data and multisource data by the random forest method. Accurate population data at finer scale are fundamental for a broad range of applications by governments, nongovernmental organizations, and companies, including the urban planing, election, risk estimation, disaster rescue, disease control, and poverty reduction.

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom's Foreign, Commonwealth & Development Office (INV-009579, formerly OPP1182425), and GRID3 COVID-19 Support Scale-up (INV-018067). Project partners included the United Nations Population Fund, Center for International Earth Science Information Network in the Columbia Climate School at Columbia University, and the Flowminder Foundation. The new age-structured population estimates are based on the existing Census-based gridded population estimates for Burkina Faso (2019), version 1.0 (WorldPop and Institut National de la Statistique et de la Demographie du Burkina Faso, 2020). Duygu Cihan, Heather Chamberlain and Thomas Abbott led the data processing, with advice from Édith Darin.RELEASE CONTENT Aggregated_BFA_under18_population_100m.tif Aggregated_BFA_18_45_population_100m.tif Aggregated_BFA_over45_population_100m.tifFILE DESCRIPTIONS The coordinate system for all GIS files is the geographic coordinate system WGS84 (World Geodetic System 1984, EPSG: 4326). Aggregated_BFA_ under18_population _100m.tifThis geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population of persons aged under 18 (0-17) per grid cell across Burkina Faso. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.Aggregated_BFA_18_45_population_100m.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population of persons aged 18 to 45 (18-45) per grid cell across Burkina Faso. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas. Aggregated_BFA_over45_population_100m.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population of persons aged over 45 (46+) per grid cell across Burkina Faso. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.METHODS OVERVIEW Processing: The existing 2019 gridded population estimates (WorldPop and Institut National de la Statistique et de la Demographie du Burkina Faso, 2020) include age- and sex- structured population estimates for 5 year age classes, based on the age and sex breakdown of population totals at the national level, from the preliminary census results. A Sprague multiplier approach was used to further disaggregate the 5-year age classes at the national level, to create three custom age-classes (under 18, 18-45 and over 45). The population for each of these custom age classes, was calculated as the proportion of the total population at the national level. This proportion was applied to the count of total population at the grid cell level.ASSUMPTIONS AND LIMITATIONS The custom age classes are estimated using a Sprague multiplier approach to interpolate the 5-year age classes and provide the population for a single year age class, which is then summed to provide the custom age classes. Interpolation introduces uncertainty in the estimates.The population estimates for the custom age classes were calculated from national level totals for 5-year age classes. A constant age-structure across all grid cells was assumed in applying the national proportions for the custom age classes to the grid cell level.RELEASE HISTORYVersion 1.0 (25/05/2022) - Original release of this data set.WORKS CITEDDooley, C. A. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.0. University of Southampton: Southampton, UK. Source of building Footprints “Ecopia Vector Maps Powered by Maxar Satellite Imagery”© 2020. https://dx.doi.org/10.5258/SOTON/WP00666.WorldPop and Institut National de la Statistique et de la Demographie du Burkina Faso. 2020. Census-based gridded population estimates for Burkina Faso (2019), version 1.0. WorldPop, University of Southampton. https://dx.doi.org/10.5258/SOTON/WP00687

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by the Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646

Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (2019 Revision of World Population Prospects). "NoData" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by Jeremiah J.Nieves et al. 2020.The mapping approach is the Random Forests-based dasymetric redistribution developed by Stevens et al. (2015). The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (Bondarenko et al., 2020), with oversight from Alessandro Sorichetta (WorldPop).SOURCE DATA:This dataset was produced based on the 2020 population census/projection-based estimates for 2020 (information and sources of the input population data can be found here).Built-Settlement Growth Model (BSGM) outputs produced by Jeremiah J.Nieves et al. 2020.Geospatial covariates representing factors related to population distribution, were obtained from the "Global High Resolution Population Denominators Project" (OPP1134076).REFERENCES:- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https://doi.org/10.1371/journal.pone.0107042- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https://doi.org/10.1016/j.compenvurbsys.2019.101444- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https://dx.doi.org/10.5258/SOTON/WP00665

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development (OPP1182408). Project partners included the United Nations Population Fund, Center for International Earth Science Information Network in the Earth Institute at Columbia University, and the Flowminder Foundation. These data may be distributed using a Creative Commons Attribution Share-Alike 4.0 License. Contact release@worldpop.org for more information.

Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. "NoData" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by Jeremiah J.Nieves et al. 2020.The mapping approach is the Random Forests-based dasymetric redistribution developed by Stevens et al. (2015). The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (Bondarenko et al., 2020), with oversight from Alessandro Sorichetta (WorldPop).SOURCE DATA:This dataset was produced based on the 2020 population census/projection-based estimates for 2020 (information and sources of the input population data can be found here).Built-Settlement Growth Model (BSGM) outputs produced by Jeremiah J.Nieves et al. 2020.Geospatial covariates representing factors related to population distribution, were obtained from the "Global High Resolution Population Denominators Project" (OPP1134076).REFERENCES:- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https://doi.org/10.1371/journal.pone.0107042- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https://doi.org/10.1016/j.compenvurbsys.2019.101444- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https://dx.doi.org/10.5258/SOTON/WP00665

MLVA-ompA genotypes represented by three or more samples unique to 2012–13.

Gridded population with 100m spaital resolution of the 34 key areas along One Belt One Road in 2010, which indicates that the population count (Unit: person) per pixel (i.e., grid). This data is derived from geodata institute of Southampton University, UK. The prejection transform and extraction processes were done to generate the gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2010. The original gridded popution is spatially downscaled from census data and multisource data by the random forest method. Accurate population data at finer scale are fundamental for a broad range of applications by governments, nongovernmental organizations, and companies, including the urban planing, election, risk estimation, disaster rescue, disease control, and poverty reduction.

Census/projection-disaggregated gridded population datasets for 189 countries in 2020 using Built-Settlement Growth Model (BSGM) outputs. Available at: https://www.worldpop.org/doi/10.5258/SOTON/WP00684

RF-based gridded population distribution datasets produced in the framework of the Global Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076)

Census/projection-disaggregated gridded population datasets for 51 countries across sub-Saharan Africa in 2020 using building footprints. Source of building footprints "Ecopia Vector Maps Powered by Maxar Satellite Imagery" © 2020.

The UK Generations and Gender Survey (GGS) is conducted by the University of Southampton and the survey agency NatCen Social Research. It is funded by the Economic and Social Research Council (ESRC).

The GGS is one of the main outputs of the Generations and Gender Programme (GGP), an international research infrastructure supported by the European Commission. The GGP aims to understand how individuals and families have been changing over the past two decades. A multi-institutional Consortium Board developed the questionnaire, keeping in mind international comparability.

The UK GGS is a nationally representative online survey that has collected information from around 7,000 respondents aged 18-59. The sampling design uses a sampling framework based on Postcode Address files (PAF). Weights are available with the data.

Further information may be found on the Centre for Population Change Generations and Gender Survey webpage.

These data were produced by WorldPop at the University of Southampton. These data include gridded estimates of population at approximately 100m and 1km for 2022, along with estimates of the number of people belonging to individual age-sex groups. These results were produced using subnational population estimates for Sudan in 2022 provided in the Common Operational Dataset on Population Statistics (COD-PS) and built-up surfaces/volumes covariates extracted from GHSL datasets; GHS-BUILT-Surface epoch 2020 layer, combined with Digitize Africa building footprints, were used to delineate settled areas. The constrained top-down disaggregation method was used to produce the datasets, i.e. population was only estimated within areas classified as containing built settlement. The modelling work and geospatial data processing was led by Bondarenko M. and Leasure D.R.. Oversight was provided by Tatem A.J..

RF-based gridded population distribution datasets produced in the framework of the Global Project adjusted to match UNPD totals (from the 2019 Revision of World Population Prospects)

These data were produced by the WorldPop Research Group at the University of Southampton. This work was funded by the Bill and Melinda Gates Foundation (BMGF) and the United Kingdom's Department for International Development (OPP1182408). The primary intended use of these data was aiding the BMGF field teams. These data may be distributed using a Creative Commons Attribution Share-Alike 4.0 License. Contact release@worldpop.org for more information. This dataset provides population estimates for each settled 100m grid square in South Sudan. The grid square values were derived using the National Bureau of Statistics' 2019 population projection estimates that were adjusted to account for displacement of people. The locations people have been displaced to were directly obtained from IOM's Displacement Tracking Matrix (DTM). The locations people have been displaced from were derived using DTM and the Armed Conflict Locations and Events Database (ACLED). Numbers of displaced people per location were calculated using recorded numbers of international refugees and internally displaced persons.