Algeria is the biggest country in Africa, with an area exceeding 2.38 million square kilometers as of 2020. The Democratic Republic of the Congo and Sudan follow with a total area of around 2.34 million and 1.88 million square kilometers, respectively. On the other hand, Seychelles is the smallest country on the continent, with an area of only 460 square kilometers. Overall, Africa’s total area exceeds 30 million square kilometers, being the second largest continent in the world after Asia. Nigeria and Ethiopia lead the ranking of the most populated countries in Africa.

How have the African countries been formed?

The political geography of Africa has been influenced by its colonial history. Between the 19th and 20th Century, the European colonizers have divided up Africa. The partition of the territories was merely driven by strategic purposes: Borders between countries were artificially created in the absence of a geographic border. Following the decolonization, most countries gained their independence in the second half of the 1900s. The newest country in Africa is South Sudan, which became independent in 2011.

Africa's physical geography

Geographically, the African continent is mostly constituted by plains and tablelands. Inner plateaus are prevalent in the sub-Saharan region. In the center-north, the arid Sahara Desert extends for around nine million square kilometers, being the largest subtropical desert in the world. The continent also has some of the biggest water basins worldwide, namely the Nile, Congo, and Niger rivers. East Africa has, instead, the highest summit on the continent, the Kilimanjaro. Peaking at 5,895 meters, the mountain dominates Tanzania’s landscape and attracts thousands of climbers each year.

This dataset provides values for SURFACE AREA SQ KM WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2024, the population of Africa was projected to grow by 2.27 percent compared to the previous year. The population growth rate on the continent has been constantly over 2.5 percent from 2000 onwards, and it peaked at 2.63 percent in 2013. Despite a slowdown in the growth rate after that, the continent's population will continue to increase significantly in the coming years. The second-largest population worldwide In 2023, the total population of Africa amounted to almost 1.5 billion. The number of inhabitants had grown steadily in the previous decades, rising from approximately 831 million in 2000. Driven by a decreasing mortality rate and a higher life expectancy at birth, the African population was forecast to increase to about 2.5 billion individuals by 2050. Africa is currently the second most populous continent worldwide after Asia. However, forecasts showed that Africa could gradually close the gap and almost reach the size of the Asian population in 2100. By that year, Africa might count 3.8 billion people, compared to 4.6 billion in Asia. The world's youngest continent The median age in Africa corresponded to 19.2 years in 2024. Although the median age has increased in recent years, the continent remains the youngest worldwide. In 2023, roughly 40 percent of the African population was aged 15 years and younger, compared to a global average of 25 percent. Africa recorded not only the highest share of youth but also the smallest elderly population worldwide. As of the same year, only three percent of Africa's population was aged 65 years and older. Africa and Latin America were the only regions below the global average of ten percent. On the continent, Niger, Uganda, and Angola were the countries with the youngest population in 2023.

South Africa’s forests accounted for ***** percent of the country’s land area in 2022, continuing a decline since 2010, when forests covered ***** percent of the land. By comparison, Africa had **** percent of its land area under forest cover in 2020.

This dataset provides values for LAND AREA HECTARES WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Central African Republic: Population size, in millions: The latest value from is million, unavailable from million in . In comparison, the world average is 0.00 million, based on data from countries. Historically, the average for the Central African Republic from to is million. The minimum value, million, was reached in while the maximum of million was recorded in .

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Human genetic variation particularly in Africa is still poorly understood. This is despite a consensus on the large African effective population size compared to populations from other continents. Based on sequencing of the mitochondrial Cytochrome C Oxidase subunit II (MT-CO2), and genome wide microsatellite data we observe evidence suggesting the effective size (Ne) of humans to be larger than the current estimates, with a foci of increased genetic diversity in east Africa, and a population size of east Africans being at least 2-6 fold larger than other populations. Both phylogenetic and network analysis indicate that east Africans possess more ancestral lineages in comparison to various continental populations placing them at the root of the human evolutionary tree. Our results also affirm east Africa as the likely spot from which migration towards Asia has taken place. The study reflects the spectacular level of sequence variation within east Africans in comparison to the global sample, and appeals for further studies that may contribute towards filling the existing gaps in the database. The implication of these data to current genomic research, as well as the need to carry out defined studies of human genetic variation that includes more African populations; particularly east Africans is paramount.

Sudan had the largest agricultural land area in Africa in 2022, corresponding to around 112.7 million hectares. Following, South Africa and Nigeria had roughly 96.3 million and 69.8 million hectares of land under agricultural activities, respectively. In proportion to the total land area, Lesotho was the African country with the largest share of land devoted to agriculture.

Abstract

The Afrobarometer is a comparative series of public attitude surveys that assess African citizen's attitudes to democracy and governance, markets, and civil society, among other topics. The surveys have been undertaken at periodic intervals since 1999. The Afrobarometer's coverage has increased over time. Round 1 (1999-2001) initially covered 7 countires and was later extended to 12 countries. Round 2 (2002-2004) surveyed citizens in 16 countries. Round 3 (2005-2006) 18 countries, and Round 4 (2008) 20 countries.The survey covered 34 countries in Round 5 (2011-2013), 36 countries in Round 6 (2014-2015), and 34 countries in Round 7 (2016-2018). Round 8 covered 34 African countries. The 34 countries covered in Round 8 (2019-2021) are:

Angola, Benin, Botswana, Burkina Faso, Cabo Verde, Cameroon, Côte d'Ivoire, eSwatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia and Zimbabwe.

Geographic coverage

The survey has national coverage in the following 34 African countries: Angola, Benin, Botswana, Burkina Faso, Cabo Verde, Cameroon, Côte d'Ivoire, eSwatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia and Zimbabwe.

Analysis unit

Households and individuals

Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

Kind of data

Sample survey data

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalised settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewers alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

To keep the costs and logistics of fieldwork within manageable limits, eight interviews are clustered within each selected PSU.

Data weights For some national surveys, data are weighted to correct for over or under-sampling or for household size. "Withinwt" should be turned on for all national -level descriptive statistics in countries that contain this weighting variable. It is included as the last variable in the data set, with details described in the codebook. For merged data sets, "Combinwt" should be turned on for cross-national comparisons of descriptive statistics. Note: this weighting variable standardizes each national sample as if it were equal in size.

Further information on sampling protocols, including full details of the methodologies used for each stage of sample selection, can be found in Section 5 of the Afrobarometer Round 5 Survey Manual

Mode of data collection

Face-to-face

Research instrument

The questionnaire for Round 3 addressed country-specific issues, but many of the same questions were asked across surveys. The survey instruments were not standardized across all countries and the following features should be noted:

• In the seven countries that originally formed the Southern Africa Barometer (SAB) - Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe - a standardized questionnaire was used, so question wording and response categories are the generally the same for all of these countries. The questionnaires in Mali and Tanzania were also essentially identical (in the original English version). Ghana, Uganda and Nigeria each had distinct questionnaires.

• This merged dataset combines, into a single variable, responses from across these different countries where either identical or very similar questions were used, or where conceptually equivalent questions can be found in at least nine of the different countries. For each variable, the exact question text from each of the countries or groups of countries ("SAB" refers to the Southern Africa Barometer countries) is listed.

• Response options also varied on some questions, and where applicable, these differences are also noted.

Background The distribution of resources can affect animal range sizes, which in turn may alter infectious disease dynamics in heterogenous environments. The risk of pathogen exposure or the spatial extent of outbreaks may vary with host range size. This study examined the range sizes of herbivorous anthrax host species in two ecosystems and relationships between spatial movement behavior and patterns of disease outbreaks for a multi-host environmentally transmitted pathogen. Methods We examined range sizes for seven host species and the spatial extent of anthrax outbreaks in Etosha National Park, Namibia and Kruger National Park, South Africa, where the main host species and outbreak sizes differ. We evaluated host range sizes using the local convex hull method at different temporal scales, within-individual temporal range overlap, and relationships between ranging behavior and species contributions to anthrax cases in each park. We estimated the spatial extent of annual anthrax mortalities and evaluated whether the extent was correlated with case numbers of a given host species. Results Range size differences among species were not linearly related to anthrax case numbers. In Kruger the main host species had small range sizes and high range overlap, which may heighten exposure when outbreaks occur within their ranges. However, different patterns were observed in Etosha, where the main host species had large range sizes and relatively little overlap. The spatial extent of anthrax mortalities was similar between parks but less variable in Etosha than Kruger. In Kruger outbreaks varied from small local clusters to large areas and the spatial extent correlated with case numbers and species affected. Secondary host species contributed relatively few cases to outbreaks; however, for these species with large range sizes, case numbers positively correlated with outbreak extent. Conclusions Our results provide new information on the spatiotemporal structuring of ranging movements of anthrax host species in two ecosystems. The results linking anthrax dynamics to host space use are correlative, yet suggest that, though partial and proximate, host range size and overlap may be contributing factors in outbreak characteristics for environmentally transmitted pathogens. Methods We compiled movement data from GPS (Global Positioning System) collars including newly collected and previously published datasets on springbok (Antidorcas marsupialis) from Etosha National Park, Namibia, impala (Aepyceros melampus) and African buffalo (Syncerus caffer) from Kruger National Park, South Africa, and greater kudu (Tragelaphus strepsiceros), blue wildebeest (Connochaetes taurinus), and plains zebra (Equus quagga) from both parks between 2006 – 2020. Newly collected data included movement datasets on kudu, wildebeest and zebra (2018–2020) in Etosha and impala and kudu in Kruger. Details of the numbers, time periods and data sources of these movement data can be found in the corresponding research paper. Because of different sampling intensities and irregular intervals of the telemetry data, we thinned the data to three readings a day for more comparable relocation data across different species and tracking periods among species. We then prepared three different datasets at bimonthly, monthly and seasonal scales for each species by park and further prepared a fourth dataset at seasonal scales with only individuals tracked across at least three seasons. Details of dataset preparation can be found in the corresponding paper. The data here are thinned movement datasets.

The Central African Republic: Population size, number of people: The latest value from is people, unavailable from people in . In comparison, the world average is 0 people, based on data from countries. Historically, the average for the Central African Republic from to is people. The minimum value, people, was reached in while the maximum of people was recorded in .

This dataset provides values for ROAD DENSITY KM OF ROAD PER SQ KM OF LAND AREA WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

South Africa: Arable land, percent of total land area: The latest value from 2022 is 9.9 percent, unchanged from 9.9 percent in 2021. In comparison, the world average is 14.5 percent, based on data from 189 countries. Historically, the average for South Africa from 1961 to 2022 is 10.4 percent. The minimum value, 9.9 percent, was reached in 1961 while the maximum of 11.4 percent was recorded in 2000.

Created as part of the USGS’s Africa Ecosystems Mapping project, the Africa Land Surface Forms layer classifies the landscape of Africa into seven classes: Smooth plains, irregular plains, escarpments, hills, breaks, low mountains, and high mountains/deep canyons.This layer provides access to a 100m cell size raster derived from SRTM and other data that divides the African landscape into seven classes based on land form. The data covers Africa, Madagascar, and other coastal islands near Africa. It was published in 2009 by the USGS Rocky Mountain Geographic Science Center.This layer was used as an input for the Africa Terrestrial Ecosystems mapping project. Link to source metadata Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 24,000 x 24,000 pixels. What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services, which represents an area roughly 2,400 kilometers on a side. The source data for this layer are available here.Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

This dataset provides values for EXPORTS EURO AREA COUNTRIES 17 reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

African states are both unusually large and well known for having artificial borders created during the colonial period. While African state size and shape have been previously shown to be correlated with negative development outcomes, no one has heretofore examined the origins of either phenomenon. Here, I show that African state size and shape are not arbitrary but are rather a consequence of Africa's low pre-colonial population density, whereby low-density areas were consolidated into unusually large colonial states with artificial borders. I also show that state size has a strong negative relationship with pre-colonial trade and that trade and population density alone explain the majority of the variation in African state size. Finally, I do not find a relationship between population density and state size or shape among non-African former colonies, thereby emphasizing the distinctiveness of modern African state formation.

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Rice (Oryza sativaandO. glaberrima) is one of the world"s most important staple food crops. Over half of the world"s population relies on rice. The people in some parts of Africa have been cultivating rice for over 3,500 years. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofrice harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of rice as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Cassava Groundnut (Peanut) Maize (Corn) Millet PotatoSorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started follow these links: Landscape Layers - a reintroductionLiving Atlas Discussion Group

This dataset has the distribution of population by size,class of household and sex.

A flexible model to reconstruct education-specific fertility rates: Sub-saharan Africa case study

The fertility rates are consistent with the United Nation World Population Prospects (UN WPP) 2022 fertility rates.

The Bayesian model developed to reconstruct the fertility rates using Demographic and Health Surveys and the UN WPP is published in a working paper.

Abstract

The future world population growth and size will be largely determined by the pace of fertility decline in sub-Saharan Africa. Correct estimates of education-specific fertility rates are crucial for projecting the future population. Yet, consistent cross-country comparable estimates of education-specific fertility for sub-Saharan African countries are still lacking. We propose a flexible Bayesian hierarchical model to reconstruct education-specific fertility rates by using the patchy Demographic and Health Surveys (DHS) data and the United Nations’ (UN) reliable estimates of total fertility rates (TFR). Our model produces estimates that match the UN TFR to different extents (in other words, estimates of varying levels of consistency with the UN). We present three model specifications: consistent but not identical with the UN, fully-consistent (nearly identical) with the UN, and consistent with the DHS. Further, we provide a full time series of education-specific TFR estimates covering five-year periods between 1980 and 2014 for 36 sub-Saharan African countries. The results show that the DHS-consistent estimates are usually higher than the UN-fully-consistent ones. The differences between the three model estimates vary substantially in size across countries, yielding 1980-2014 fertility trends that differ from each other mostly in level only but in some cases also in direction.

Funding

The data set are part of the BayesEdu Project at Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna) funded from the “Innovation Fund Research, Science and Society” by the Austrian Academy of Sciences (ÖAW).

We provide education-specific total fertility rates (ESTFR) from three model specifications: (1) estimated TFR consistent but not identical with the TFR estimated by the UN (“Main model (UN-consistent)”; (2) estimated TFR fully consistent (nearly identical) with the TFR estimated by the UN ( “UN-fully -consistent”, and (3) estimated TFR consistent only with the TFR estimated by the DHS ( “DHS-consistent”).

For education- and age-specific fertility rates that are UN-fully consistent, please see https://doi.org/10.5281/zenodo.8182960

Variables

Country: Country names

Education: Four education levels, No Education, Primary Education, Secondary Education and Higher Education.

Year: Five-year periods between 1980 and 2015.

ESTFR: Median education-specific total fertility rate estimate

sd: Standard deviation

Upp50: 50% Upper Credible Interval

Lwr50: 50% Lower Credible Interval

Upp80: 80% Upper Credible Interval

Lwr80: 80% Lower Credible Interval

Model: Three model specifications as explained above and in the working paper. DHS-consistent, Main model (UN-consistent) and UN-fully consistent.

List of countries:

Angola, Benin, Burkina Faso, Burundi, Cote D'Ivoire, Cameroon, Central African Republic, Chad, Comoros, Congo, Democratic Republic of Congo, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Tanzania, Togo, Uganda, Zambia, Zimbabwe