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. Democratic Republic of the Congo data available from WorldPop here.

Democratic Republic of the Congo: Population density, people per square km: The latest value from 2021 is 42 people per square km, an increase from 41 people per square km in 2020. In comparison, the world average is 456 people per square km, based on data from 196 countries. Historically, the average for Democratic Republic of the Congo from 1961 to 2021 is 19 people per square km. The minimum value, 7 people per square km, was reached in 1961 while the maximum of 42 people per square km was recorded in 2021.

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.

The share of urban population in Dem. Rep. Congo saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 47.44 percent. Still, the share reached its highest value in the observed period in 2023. A population may be defined as urban depending on the size (population or area) or population density of the village, town, or city. The urbanization rate then refers to the share of the total population who live in an urban setting. International comparisons may be inconsistent due to differing parameters for what constitutes an urban center.Find more key insights for the share of urban population in countries like Cameroon and Chad.

Estimated density of gorillas at five sites from line-transect surveys.

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.

BackgroundThe dynamics of the spread of cholera epidemics in the Democratic Republic of the Congo (DRC), from east to west and within western DRC, have been extensively studied. However, the drivers of these spread processes remain unclear. We therefore sought to better understand the factors associated with these spread dynamics and their potential underlying mechanisms.MethodsIn this eco-epidemiological study, we focused on the spread processes of cholera epidemics originating from the shores of Lake Kivu, involving the areas bordering Lake Kivu, the areas surrounding the lake areas, and the areas out of endemic eastern DRC (eastern and western non-endemic provinces). Over the period 2000–2018, we collected data on suspected cholera cases, and a set of several variables including types of conflicts, the number of internally displaced persons (IDPs), population density, transportation network density, and accessibility indicators. Using multivariate ordinal logistic regression models, we identified factors associated with the spread of cholera outside the endemic eastern DRC. We performed multivariate Vector Auto Regressive models to analyze potential underlying mechanisms involving the factors associated with these spread dynamics. Finally, we classified the affected health zones using hierarchical ascendant classification based on principal component analysis (PCA).FindingsThe increase in the number of suspected cholera cases, the exacerbation of conflict events, and the number of IDPs in eastern endemic areas were associated with an increased risk of cholera spreading outside the endemic eastern provinces. We found that the increase in suspected cholera cases was influenced by the increase in battles at lag of 4 weeks, which were influenced by the violence against civilians with a 1-week lag. The violent conflict events influenced the increase in the number of IDPs 4 to 6 weeks later. Other influences and uni- or bidirectional causal links were observed between violent and non-violent conflicts, and between conflicts and IDPs. Hierarchical clustering on PCA identified three categories of affected health zones: densely populated urban areas with few but large and longer epidemics; moderately and accessible areas with more but small epidemics; less populated and less accessible areas with more and larger epidemics.ConclusionOur findings argue for monitoring conflict dynamics to predict the risk of geographic expansion of cholera in the DRC. They also suggest areas where interventions should be appropriately focused to build their resilience to the disease.

This dataset contains individuals of Baillonella toxisperma, Entandrophragma cylindricum, Erythrophleum suaveolens, Dacryodes Buettnerii and Gambeya spp('spp'), around 10 villages ('vil'), and within five logging concessions ('conc') in Cameroon, Gabon and the DRC ('pays'). There are columns indicating the diameter at breast height ('dbh' – cm). To determine the density and abundance of the selected species within the concession and around the villages, trees were identified and sampled on plots. Within the concessions, sample plots were established in the 2012 cutting area. Five plots of 5 ha each were established at random within each of the four quadrants (North, South, East and West) of the 5 000 ha cutting area, for a total of 20 sample plots. To evaluate the density of trees around villages, 21 sample plots of 5 ha each were laid out around each village along three transects extending from the village centre towards the forest concession to a maximum distance of 10 km. The total sample area described a half circle of 157 km2 (15,700 ha). The central transect (“B”) was oriented towards the forest concession and the transects “A” and “C” were laid out at 450 C to each side of it (Figure 2). Sample plots around villages were also stratified among four different distance bands, 1-1.9 km (stratum A), 2-3.9 km (stratum B), 4-6.9 km (stratum C) and 7-10 km (stratum D) from the village centre. To obtain a sampling intensity of 0.5% in each stratum, the number of plots per stratum increased in each band of increasing distance. In both the concession and around villages, sample plots were 100 m x 500 m in size. Around villages, plot boundaries extended 100 m along the transect line and 500 m to one side or the other, alternating. Within each plot, all individuals of target species (dbh ≥ 20 cm) were identified and their diameters at breast height (dbh) measured using diameter tapes. When trees had buttresses, diameters were measured at 50 cm above the buttresses with GPS coordinates recorded at each corner of the sampled plots. Stumps were also sought, identified and measured.

BackgroundThe dynamics of the spread of cholera epidemics in the Democratic Republic of the Congo (DRC), from east to west and within western DRC, have been extensively studied. However, the drivers of these spread processes remain unclear. We therefore sought to better understand the factors associated with these spread dynamics and their potential underlying mechanisms.MethodsIn this eco-epidemiological study, we focused on the spread processes of cholera epidemics originating from the shores of Lake Kivu, involving the areas bordering Lake Kivu, the areas surrounding the lake areas, and the areas out of endemic eastern DRC (eastern and western non-endemic provinces). Over the period 2000–2018, we collected data on suspected cholera cases, and a set of several variables including types of conflicts, the number of internally displaced persons (IDPs), population density, transportation network density, and accessibility indicators. Using multivariate ordinal logistic regression models, we identified factors associated with the spread of cholera outside the endemic eastern DRC. We performed multivariate Vector Auto Regressive models to analyze potential underlying mechanisms involving the factors associated with these spread dynamics. Finally, we classified the affected health zones using hierarchical ascendant classification based on principal component analysis (PCA).FindingsThe increase in the number of suspected cholera cases, the exacerbation of conflict events, and the number of IDPs in eastern endemic areas were associated with an increased risk of cholera spreading outside the endemic eastern provinces. We found that the increase in suspected cholera cases was influenced by the increase in battles at lag of 4 weeks, which were influenced by the violence against civilians with a 1-week lag. The violent conflict events influenced the increase in the number of IDPs 4 to 6 weeks later. Other influences and uni- or bidirectional causal links were observed between violent and non-violent conflicts, and between conflicts and IDPs. Hierarchical clustering on PCA identified three categories of affected health zones: densely populated urban areas with few but large and longer epidemics; moderately and accessible areas with more but small epidemics; less populated and less accessible areas with more and larger epidemics.ConclusionOur findings argue for monitoring conflict dynamics to predict the risk of geographic expansion of cholera in the DRC. They also suggest areas where interventions should be appropriately focused to build their resilience to the disease.

Assessment of Plasmodium infection by PCR analysis in DRC.