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.

Congo, The Democratic Republic of the CD: Population Density: People per Square Km data was reported at 35.879 Person/sq km in 2017. This records an increase from the previous number of 34.731 Person/sq km for 2016. Congo, The Democratic Republic of the CD: Population Density: People per Square Km data is updated yearly, averaging 14.762 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 35.879 Person/sq km in 2017 and a record low of 6.898 Person/sq km in 1961. Congo, The Democratic Republic of the CD: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.World Bank: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted Average;

Population density (people per sq. km of land area) in Congo was reported at 45.17 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Congo - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

The statistic shows the population density in the Democratic Republic of the Congo from 2012 to 2022. In 2022, the density of the Democratic Republic of the Congo's population amounted to 45.17 inhabitants per square kilometer.

Population density (people per sq. km of land area) in Republic Of The Congo was reported at 17.67 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Republic Of The Congo - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Historical dataset showing Democratic Republic of Congo population density by year from 1961 to 2022.

Congo, Republic of CG: Population Density: Inhabitants per sq km data was reported at 17.670 Person in 2022. This records an increase from the previous number of 17.250 Person for 2021. Congo, Republic of CG: Population Density: Inhabitants per sq km data is updated yearly, averaging 11.240 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 17.670 Person in 2022 and a record low of 6.970 Person in 1990. Congo, Republic of CG: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Congo, Republic of – Table CG.OECD.GGI: Social: Demography: Non OECD Member: Annual.

The statistic shows the population density in the Republic of the Congo from 2012 to 2022. In 2022, the density of the Republic of the Congo's population amounted to 17.67 inhabitants per square kilometer.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Democratic Republic of Congo: (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).

Density of physicians of Democratic Republic of the Congo surged by 5.65% from 0.2 number per thousand population in 2021 to 0.2 number per thousand population in 2022. Since the 53.87% slump in 2019, density of physicians leapt by 11.98% in 2022.

Comprehensive socio-economic dataset for DR Congo including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

Density of nursing and midwifery personnel of Democratic Republic of the Congo jumped by 9.97% from 1.1 number per thousand population in 2021 to 1.2 number per thousand population in 2022. Since the 37.65% drop in 2016, density of nursing and midwifery personnel shot up by 107.85% in 2022.

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 Democratic Republic of the Congo. 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.

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 Democratic Republic of the Congo. 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.

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 Republic of Congo. 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.

This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Kwilu Province in the Democratic Republic of Congo (DRC), along with estimates of the number of people belonging to various age-sex groups. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC for 2022 as well as settlement extents and geospatial covariates, to model and estimate population numbers at grid cell level using a Bayesian hierarchical statistical modelling framework. The approach facilitated accounting for the multiple levels of variability within the data while simultaneously quantifying for uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2022, which is the period following the PDRS survey data collection for Kwilu. Although the methods were robust enough to explicitly account for key random biases and adjust for potential systematic biases within the observed datasets, it is important to note that some systematic biases arising from other sources may remain. These data were produced by the WorldPop Research Group at the University of Southampton. The work was part of the GRID3 – Phase 2 Scaling project, with funding from the Gates Foundation (INV-044979). Project partners included GRID3 Inc, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. A robust Bayesian joint (hurdle) population modelling approach was developed to estimate population density whilst at same time accounting for probability of settlement detection. The final statistical modelling was conceived, designed, and implemented by Chris Nnanatu. Data processing was done by Ortis Yankey, while project oversight was provided by Attila Lazar, and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised, and shared by the PNLP and the implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 6.0) (CIESIN, 2025).

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 Democratic Republic of the Congo. 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 Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 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. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

The gridded geospatial datasets implemented as covariates in the building count and population density model components.

The raster dataset consists of a 500 m score grid for fruits 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: Cereal. • 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" * 0.3) + (“Major Cities Accessibility” * 0.2) + (”Poverty” * 0.1) + ("Human Population Density" * 0.1) + (“Regional Cities Accessibility” * 0.1) +(“Port Accessibility” * 0.2)