In 2024, the population density in Africa was 51.3 inhabitants per square kilometer. From 2000 onwards, the density of the population on the continent has increased annually. Moreover, the average number of people living within a square kilometer was expected to increase to around 58.5 by 2030. Mauritius, Rwanda, and Burundi were the African countries with the highest population density as of 2023.

Mauritius had the highest population density level in Africa as of 2023, with nearly *** inhabitants per square kilometer. The country has also one of the smallest territories on the continent, which contributes to the high density. As a matter of fact, the majority of African countries with the largest concentration of people per square kilometer have the smallest geographical area as well. The exception is Nigeria, which ranks among the largest territorial countries in Africa and is very densely populated at the same time. After all, Nigeria has also the largest population on the continent.

The average for 2021 based on 47 countries was 119 people per square km. The highest value was in Mauritius: 634 people per square km and the lowest value was in Namibia: 3 people per square km. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

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.

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 Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

Our Population Density Grid Dataset for Eastern Africa offers detailed, grid-based insights into the distribution of population across cities, towns, and rural areas. Free to explore and visualize, this dataset provides an invaluable resource for businesses and researchers looking to understand demographic patterns and optimize their location-based strategies.

By creating an account, you gain access to advanced tools for leveraging this data in geomarketing applications. Perfect for OOH advertising, retail planning, and more, our platform allows you to integrate population insights with your business intelligence, enabling you to make data-driven decisions for your marketing and expansion strategies.

The population density in South Africa saw no significant changes in 2022 in comparison to the previous year 2021 and remained at around 51.42 inhabitants per square kilometer. Still, the population density reached its highest value in the observed period in 2022. Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Kingdom of Eswatini and Botswana.

Population Density in Africa. Data come from Wikidata, originally from https://datacatalog.worldbank.org/

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. Sri Lanka data available from WorldPop here.

From the AfriPop website..."High resolution, contemporary data on human population distributions are a prerequisite for the accurate measurement of the impacts of population growth, for monitoring changes and for planning interventions. The AfriPop project was initiated in July 2009 with an aim of producing detailed and freely-available population distribution maps for the whole of Africa. Based on the approaches outlined in detail here and here, and summarized on the methods page, fine resolution satellite imagery-derived settlement maps are combined with land cover maps to reallocate contemporary census-based spatial population count data. Assessments have shown that the resultant maps are more accurate than existing population map products, as well as the simple gridding of census data. Moreover, the 100m spatial resolution represents a finer mapping detail than has ever before been produced at national extents. The approaches used in AfriPop dataset production are designed with operational application in mind, using simple and semi-automated methods to produce easily updatable maps. Given the speed with which population growth and urbanisation are occurring across much of Africa, and the impacts these are having on the economies, environments and health of nations, such features are a necessity for both research and operational applications."Data Source: AfriPop.org

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.

Our Population Density Grid Dataset for Southern Africa offers detailed, grid-based insights into the distribution of population across cities, towns, and rural areas. Free to explore and visualize, this dataset provides an invaluable resource for businesses and researchers looking to understand demographic patterns and optimize their location-based strategies.

By creating an account, you gain access to advanced tools for leveraging this data in geomarketing applications. Perfect for OOH advertising, retail planning, and more, our platform allows you to integrate population insights with your business intelligence, enabling you to make data-driven decisions for your marketing and expansion strategies.

The population density in Central African Republic saw no significant changes in 2022 in comparison to the previous year 2021 and remained at around 8.18 inhabitants per square kilometer. With a decline of 0.03 inhabitants per square kilometer (-0.37 percent), there is no significant change to 2021. Population density is calculated by dividing the total population by the total land area, to show the average number of people living there per square kilometer of land.Find more key insights for the population density in countries like Republic of the Congo and Cameroon.

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.

Population density (people per sq. km of land area) in South Africa was reported at 51.42 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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. Sudan data available from WorldPop here.

VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in South Africa: (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

Historical chart and dataset showing Sub-Saharan Africa population density by year from 1961 to 2022.

Population density estimates per sqkm.