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. South Sudan data available from WorldPop here.

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.

South Sudan data available from WorldPop here.

Source: Map created by EPI (Elephant Protection Initiative) with data from CIESIN, Columbia University, USA. The map is published on UNEP's South Sudan: First State of Environment and Outlook Report 2018, using data from WCS. The UNEP's report could be found here

The map shows the population distribution in South Sudan. Jonglei is the most populous area, with 16 per cent of the total population, and Western Bahr el Ghazal is the least populous area with only 4 per cent of the total. The highest population densities are along the Nile River and their tributaries.

This zip file contains 28 cloud optimized tiff files that cover the continent of Africa. Each of the 28 files represents a region or area - these are not divided by country.

Notes:

• The country-by-country files that were previously hosted here have been moved into separate datasets. You can find all of them here.
• South Sudan, Sudan, Somalia and Ethiopia are intentionally omitted from this dataset. However, a country-level dataset for Ethiopia can be found 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.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in South Sudan. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). 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. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

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.

DATASET: Version 4.0 2010 estimates of numbers of people per grid square for 2010, 2015, 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: WorldPop naming convention applied; example SSD_ppp_2010_adj_v4.tif = South Sudan population per pixel (ppp) map for 2010 adjusted to match UN national estimates (adj), dataset version 4 (v4). DATE OF PRODUCTION: Jan 2013 (Updated July 2018) CITATION: WorldPop. 2013. South Sudan 100m Population, Version 4. University of Southampton. DOI: 10.5258/SOTON/WP00642.

Vulnerable population identified by the nutritional status of children (weight for age and weight for height) as indicators for food security, in sample of households in West Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age and weight for height) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable.

This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d’Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

Data publication: 2013-08-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Lia van Wesenbeeck

Resource Contact: Ben Sonneveld

Resource constraints:

copyright

Online resources:

Weight for age <-3DS, % of population - Distribution in sample of households in West Africa

Weight for age -2SD --3SD, % of population - Distribution in sample of households in West Africa

Weight for age -2SD--0, % of population - Distribution in sample of households in West Africa

Weight for age >0SD, % of population - Distribution in sample of households in West Africa

A spatially explicit assessment of specific vulnerabilities of the food system due to climate change and the identification of their causes; Technical report

Scenarios of major production systems in Africa

Climafrica - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations