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 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.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. More information.

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Chad: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click here. For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

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 Chad. 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.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

The raster dataset consists of a 500m score grid for cotton storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location.

The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse locations: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure.

It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.1) + (”Regional Cities Accessibility” * 0.2) + (”Asset Wealth” * 0.1)

Data publication: 2021-10-15

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Justeen De Ocampo

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets: 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. 4. Altas AI - Asset Wealth Index 2020.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped TIF raster file for cotton location score (Chad - ~ 500 m)

Raster dataset representing a potential/suitability score for non-intensive and integrated, small-scale, African Catfish and Nile Tilapia fish farming systems, using ponds and small water bodies (SWB), in asset wealth index bellow the national average regions of the Republic of Chad. Produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location.

Non-intensive aquaculture systems are considered based on natural food supply from SWB or ponds, from integrated systems (crop/livestock byproducts or waste), or with complementary feeding resourcing to on-farm or locally produced feed.

The score results from combining sub-model outputs that characterize natural geographical and economical factors:

1. Farm-gate sales - based on population density classification

2. Water balance - precipitation/evapotranspiration

3. Soil/slope suitability.

4. Inputs - Crop and livestock byproducts

It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("WaterBalance" X 0.5) + ("Soil/Slope " X 0.25) + (“Byproducts” X 0.125) + (”FarmgateSales” X 0.125)

Considered constraints or exclusive criteria are:

1. Urban areas

2. Protected areas

3. Asset wealth Index national average

Data publication: 2021-11-01

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Nelson Ribeiro

Data lineage:

Data sources, FAO Hand-in-Hand Geospatial Platform and OpenStreetMap (open data) including the following datasets:

1. Atlas AI - Asset Wealth Index and Population Density (Africa, 2020).

2. WaPOR_2 - Water Balance: precipitation and evapotranspiration monthly time-series (2009 to 2020) mean water balance modelling values: (Precipitation 1.1) - (evapotranspiration1.3) https://wapor.apps.fao.org/catalog/2

3. Soil/Slope - (1.5X soils) + Slope. Soil data from FAO (soil suitability for ponds), slope HydroSHEDS DEM 30s (https://www.hydrosheds.org/hydrosheds-core-downloads) classification: Class 4 - Very suitable: <2 Class 3 – Moderately suitable: 2 - 5 Class 2 – Marginally suitable: 5 - 8 Class 1 – Unsuitable: > 8

4. IFPRI MapSPAM 2017 - Production aggregate. https://data.apps.fao.org/map/catalog/srv/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

5. Gridded Livestock of the World (GLW 4:) - Chicken and duck. https://data.apps.fao.org/catalog/iso/15f8c56c-5499-45d5-bd89-59ef6c026704

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Download printable map, Chad

Download printable map, Sudanian agroecological zone