Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
License information was derived automatically
These data include gridded estimates of population sizes at approximately 100 m resolution with national coverage across South Sudan. This includes estimates of total population sizes and population counts in 40 different age-sex groups. It also includes a breakdown of the total population sizes into internally displaced persons (IDPs) and non-IDPs. These results were produced using publicly available census projections from the South Sudan National Bureau of Statistics and displacement data from the International Organisation for Migration (IOM) and the United Nations Refugee Agency (UNHCR), as well as building footprints from Maxar/Ecopia that were derived from recent satellite imagery. Note that this dataset is most likely to represent South Sudan's population distribution as of September 2020 given the age of the input data.1. SSD_population_v2_0_gridded.zipThis zip file contains three rasters in geotiff format:SSD_population_v2_0_gridded_population.tif This geotiff raster contains estimates of total population size for each approximately 100 m grid cell (0.0008333 decimal degrees grid) across South Sudan. NA values represent grid cells where no building footprints were present. Zero values represent grid cells that contain building footprints but are estimated to contain no people due to displacement of people away from those grid cells. These population estimates include decimals (e.g. 10.3 people). This provides more accurate population totals when grid cells are summed. A population estimate of 0.5 people in each of two neighboring grid cells would indicate an expectation that one person lives somewhere within those two grid cells. SSD_population_v2_0_gridded_nonidps.tif This geotiff raster contains estimates of non-internally displaced persons (non-IDPs) for each approximately 100 m grid cell (0.0008333 decimal degrees grid) across South Sudan, i.e. the number of people who have not been displaced from another area. This raster plus the SSD_population_v2_0_gridded_idps.tif raster equal the values given in the SSD_population_v2_0_gridded_population.tif raster. SSD_population_v2_0_gridded_idps.tif This geotiff raster contains estimates of internally displaced persons (IDPs) for each approximately 100 m grid cell (0.0008333 decimal degrees grid) across South Sudan, i.e. the number of people who have been displaced from another area. This raster plus the SSD_population_v2_0_gridded_nonidps.tif raster equal the values given in the SSD_population_v2_0_gridded_population.tif raster.2. SSD_population_v2_0_agesex.zip This zip file contains 40 rasters in geotiff format:Each raster provides gridded population estimates for an age-sex group. These were derived from the SSD_population_v2_0_gridded_population.tif raster. Note that, in this dataset, we do not provide age-sex group estimates for non-IDPs and IDPs separately. We provide 36 rasters for the commonly reported age-sex groupings of sequential age classes for males and females separately. These are labelled with either an “m” (male) or an “f” (female) followed by the number of the first year of the age class represented by the data. “f0” and “m0” are population counts of under 1 year olds for females and males, respectively. “f1” and “m1” are population counts of 1 to 4 year olds for females and males, respectively. Over 4 years old, the age groups are in five year bins labelled with a “5”, “10”, etc. Eighty year olds and over are represented in the groups “f80” and “m80”. We provide an addition four rasters that represent demographic groups often targeted by programmes and interventions. These are “under1” (all females and males under the age of 1), “under5” (all females and males under the age of 5), “under15” (all females and males under the age of 15) and “f15_49” (all females between the ages of 15 and 49, inclusive). These data were produced by the WorldPop Research Group at the University of Southampton. Data Citation: WorldPop (School of Geography and Environmental Science, University of Southampton). 2021. South Sudan 2020 gridded population estimates from census projections adjusted for displacement, version 2.0. WorldPop, University of Southampton. doi: 10.5258/SOTON/WP00709 CREDITS: The modelling work was led by Claire Dooley with support from Chris Jochem and oversight by WorldPop director Andy Tatem and GRID3 lead Attila Lazar. The support of the whole WorldPop group is acknowledged, as well as the our GRID3 partners (UNFPA, Columbia University and Flowminder). This work was supported with funding from the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Department for International Development (DFID).This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office. It is implemented by Columbia University’s Center for International Earth Science Information Network (CIESIN), the United Nations Population Fund (UNFPA), WorldPop at the University of Southampton, and the Flowminder Foundation. The primary intended use of these data was aiding the BMGF field teams.The downloadable Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data CitedContact release@worldpop.org for more information or go to here.
These 28 tiff files represent 2015 population estimates. However, please note that many of the country-level files include 2020 population estimates including: Angola, Benin, Botswana, Burundi, Cameroon, Cabo Verde, Cote d'Ivoire, Djibouti, Eritrea, Eswatini, The Gambia, Ghana, Lesotho, Liberia, Mozambique, Namibia, Sao Tome & Principe, Sierra Leone, South Africa, Togo, Zambia, and Zimbabwe. South Sudan, Sudan, Somalia and Ethiopia are intentionally omitted from this dataset. However, a country-level dataset for Ethiopia can be found at https://data.humdata.org/dataset/ethiopia-high-resolution-population-density-maps-demographic-estimates.
Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.
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Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
License information was derived automatically
These data include gridded estimates of population sizes at approximately 100 m resolution with national coverage across South Sudan. This includes estimates of total population sizes and population counts in 40 different age-sex groups. It also includes a breakdown of the total population sizes into internally displaced persons (IDPs) and non-IDPs. These results were produced using publicly available census projections from the South Sudan National Bureau of Statistics and displacement data from the International Organisation for Migration (IOM) and the United Nations Refugee Agency (UNHCR), as well as building footprints from Maxar/Ecopia that were derived from recent satellite imagery. Note that this dataset is most likely to represent South Sudan's population distribution as of September 2020 given the age of the input data.1. SSD_population_v2_0_gridded.zipThis zip file contains three rasters in geotiff format:SSD_population_v2_0_gridded_population.tif This geotiff raster contains estimates of total population size for each approximately 100 m grid cell (0.0008333 decimal degrees grid) across South Sudan. NA values represent grid cells where no building footprints were present. Zero values represent grid cells that contain building footprints but are estimated to contain no people due to displacement of people away from those grid cells. These population estimates include decimals (e.g. 10.3 people). This provides more accurate population totals when grid cells are summed. A population estimate of 0.5 people in each of two neighboring grid cells would indicate an expectation that one person lives somewhere within those two grid cells. SSD_population_v2_0_gridded_nonidps.tif This geotiff raster contains estimates of non-internally displaced persons (non-IDPs) for each approximately 100 m grid cell (0.0008333 decimal degrees grid) across South Sudan, i.e. the number of people who have not been displaced from another area. This raster plus the SSD_population_v2_0_gridded_idps.tif raster equal the values given in the SSD_population_v2_0_gridded_population.tif raster. SSD_population_v2_0_gridded_idps.tif This geotiff raster contains estimates of internally displaced persons (IDPs) for each approximately 100 m grid cell (0.0008333 decimal degrees grid) across South Sudan, i.e. the number of people who have been displaced from another area. This raster plus the SSD_population_v2_0_gridded_nonidps.tif raster equal the values given in the SSD_population_v2_0_gridded_population.tif raster.2. SSD_population_v2_0_agesex.zip This zip file contains 40 rasters in geotiff format:Each raster provides gridded population estimates for an age-sex group. These were derived from the SSD_population_v2_0_gridded_population.tif raster. Note that, in this dataset, we do not provide age-sex group estimates for non-IDPs and IDPs separately. We provide 36 rasters for the commonly reported age-sex groupings of sequential age classes for males and females separately. These are labelled with either an “m” (male) or an “f” (female) followed by the number of the first year of the age class represented by the data. “f0” and “m0” are population counts of under 1 year olds for females and males, respectively. “f1” and “m1” are population counts of 1 to 4 year olds for females and males, respectively. Over 4 years old, the age groups are in five year bins labelled with a “5”, “10”, etc. Eighty year olds and over are represented in the groups “f80” and “m80”. We provide an addition four rasters that represent demographic groups often targeted by programmes and interventions. These are “under1” (all females and males under the age of 1), “under5” (all females and males under the age of 5), “under15” (all females and males under the age of 15) and “f15_49” (all females between the ages of 15 and 49, inclusive). These data were produced by the WorldPop Research Group at the University of Southampton. Data Citation: WorldPop (School of Geography and Environmental Science, University of Southampton). 2021. South Sudan 2020 gridded population estimates from census projections adjusted for displacement, version 2.0. WorldPop, University of Southampton. doi: 10.5258/SOTON/WP00709 CREDITS: The modelling work was led by Claire Dooley with support from Chris Jochem and oversight by WorldPop director Andy Tatem and GRID3 lead Attila Lazar. The support of the whole WorldPop group is acknowledged, as well as the our GRID3 partners (UNFPA, Columbia University and Flowminder). This work was supported with funding from the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Department for International Development (DFID).This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office. It is implemented by Columbia University’s Center for International Earth Science Information Network (CIESIN), the United Nations Population Fund (UNFPA), WorldPop at the University of Southampton, and the Flowminder Foundation. The primary intended use of these data was aiding the BMGF field teams.The downloadable Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data CitedContact release@worldpop.org for more information or go to here.