South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

Lagos, in Nigeria, ranked as the most populated city in Africa as of 2024, with an estimated population of roughly nine million inhabitants living in the city proper. Kinshasa, in Congo, and Cairo, in Egypt, followed with some 7.8 million and 7.7 million dwellers. Among the 15 largest cities in the continent, another two, Kano, and Ibadan, were located in Nigeria, the most populated country in Africa. Population density trends in Africa As of 2022, Africa exhibited a population density of 48.3 individuals per square kilometer. At the beginning of 2000, the population density across the continent has experienced a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 54 by the year 2027. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 640 individuals per square kilometre. Mauritius possesses one of the most compact territories on the continent, a factor that significantly influences its high population density. Urbanization dynamics in Africa The urbanization rate in Africa was anticipated to reach close to 44 percent in 2021. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating 35 percent of the total population. This trajectory is projected to continue its ascent in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2021, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. In 2023, Africa's population was estimated to expand by 2.35 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.45 percent, reaching its pinnacle at 2.59 percent between 2012 and 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

Population in largest city in South Africa was reported at 6324351 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Johannesburg was the wealthiest city in Africa as of 2021. South Africa's biggest city held 239 billion U.S. dollars in private wealth, while Cape Town followed with 131 billion U.S. dollars. The country led the ranking of wealthiest nations in Africa. The wealth value referred to assets such as cash, properties, and business interests held by individuals living in each country, less liabilities. Moreover, government funds were excluded.

Population in the largest city (% of urban population) in South Africa was reported at 14.26 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

South Africa ZA: Population in Largest City data was reported at 9,822,625.000 Person in 2017. This records an increase from the previous number of 9,615,976.000 Person for 2016. South Africa ZA: Population in Largest City data is updated yearly, averaging 3,628,124.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9,822,625.000 Person in 2017 and a record low of 2,136,849.000 Person in 1960. South Africa ZA: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

South Africa ZA: Population in Largest City: as % of Urban Population data was reported at 26.327 % in 2017. This records an increase from the previous number of 26.291 % for 2016. South Africa ZA: Population in Largest City: as % of Urban Population data is updated yearly, averaging 23.218 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 26.327 % in 2017 and a record low of 18.806 % in 1991. South Africa ZA: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted Average;

In 2024, Pietermaritzburg (South Africa) ranked first in the crime index among African cities, with a rating of roughly ** index points. The six most dangerous areas on the continent were South African cities. The index estimates the overall level of crime in a specific territory. According to the score, crime levels are classified as very high (over 80), high (60-80), moderate (40-60), low (20-40), and very low (below 20). South Africa’s crime situation According to the crime index ranking, ************ was the most dangerous country in Africa in 2023, followed by ***************** and ******. Murder and organized crime are particularly widespread in South Africa. In 2023, the country had one of the highest murder rates globally, registering around ** homicides per 100,000 inhabitants. Moreover, South Africa’s crime scene is also characterized by the presence of organized criminal activities, for which the country ranked third in Africa. Reflecting these high levels of crime, a survey conducted in 2023 showed that around ** percent of South Africans were worried about crime and violence in the country. Crime risks in Africa The African continent hosts some of the most dangerous places worldwide. In 2023, *********** and the ******************************** were the least peaceful countries in Africa, according to the Global Peace Index. Worldwide, they ranked fourth and fifth, respectively, behind Afghanistan, Yemen, and Syria. Terrorism is a leading type of crime perpetrated in Africa. Home to Boko Aram, Nigeria is among the countries with the highest number of terrorism-related deaths globally. Furthermore, Burkina Faso had the highest number of fatalities in the world. Human trafficking is also widespread, predominantly in West Africa. The most common forms of exploitation of victims of trafficking in persons are forced labor and sexual exploitation.

Abstract

The World Values Survey aims to attain a broad understanding of socio-political trends (i.e. perceptions, behaviour and expectations) among adults across the world.

Geographic coverage

National The sample was distributed as follows: 60% metropolitan (large cities with populations of 250 000+); 40% non-metropolitan (including cities, large towns, small towns, villages and rural areas)

Analysis unit

Individual

Universe

The sample included adults 16 years+ in South Africa

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample had to be representative of urban as well as rural populations. Roughly the distribution was as follows: - South Africa: 60% metropolitan (large cities with populations of 250 000+); 40% non-metropolitan (including cities, large towns, small towns, villages and rural areas).

A standard form of sampling instructions was sent to each agency to ensure uniformity in the sampling procedure. Markinor stratified the samples for each country by region, sex and community size. To this end, statistics and figures that were supplied to us by the agencies were used. However, we requested the agencies to revise these where necessary or where alternatives would be more effective. The agencies then supplied the street names for the urban starting points, and made suggestions for sampling procedures in rural areas where neither maps nor street names were available. From sample-point level, the respondent selection was done randomly according to a selection grid used by Markinor (the first two pages of the master questionnaire).

Substitution was permitted after three unsuccessful calls. Six interviews were conducted at each sample point. The male/female split was 50/50. The urban sample included all community sizes greater than 500 and the rural sample all community sizes less than 500. This is the definition of urban and rural used in South Africa.

Remarks about sampling: -Final numbers of clusters or sampling points: 500 -Sample unit from office sampling: Street Names

Mode of data collection

Face-to-face [f2f]

Research instrument

The WVS questionnaire was translated from the English questionnaire by a specialist translator The translated questionnaire was pre-tested. The pre-tests were part of the general pilots. In total 20 pilots were conducted. The English questionnaire from the University of Michigan was used to make the WVS. Extra questions were added at the end of the questionnaire. Also, country specific questions were included at the end of the questionnaire, just before the demographics.The sample was designed to be representative of the entire adult population, i.e. 18 years and older, of your country. The lower age cut-off for the sample was 16 and there was not any upper age cut-off for the sample.

Cleaning operations

Some measures of coding reliability were employed. Each questionnaire is coded against the coding frame. A minimum of 10% of each coders work is checked to ensure consistency in interpretation. If any discrepancies in interpretation are World Values Survey (1999-2004) - South Africa 2001 v.2015.04.18 discovered, a 100% check is carried out on that particular coders work. Errors were corrected individually and automatically.

Sampling error estimates

The error margins for this survey can be calculated by taking the following factors into account: - all samples were random (as opposed to quota-controlled) - the sample size per country (or segment being analysed) - the substitution rate per country (or segment being analysed) - the rates were recorded on CARD 1; col. 805 of the questionnaire. From the substitution rate, the response rate can be calculated.

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead

The fastest growing city in Africa is Bujumbura, in Burundi. In 2020, this city had an estimated population of about one million. By 2035, the population of Bujumbura could increase by 123 percent and reach roughly 2.3 million people. Zinder, in Niger, had about half million inhabitants in 2020 and, with a growth rate of 118 percent, is Africa's second fastest growing city. In 2035, Zinder could have over one million residents.

As of 2021, the largest city in whole Africa is Lagos, in Nigeria. Other highly populated cities in Africa are Kinshasa, in Congo, Cairo, and Alexandria, both located in Egypt.

The South African data center construction market is projected to grow at a CAGR of 10.27% during the forecast period (2025-2033), reaching a market size of $1.31 million by 2033. The growth of the market is attributed to the increasing demand for data storage and processing services, driven by the adoption of cloud computing and big data analytics. Moreover, the presence of several major financial institutions and telecommunication companies in the country has further fueled the demand for data center infrastructure. The major drivers of the market include the rising demand for cloud computing, big data analytics, and artificial intelligence applications, which require significant data storage and processing capabilities. Additionally, the increasing need for data security and compliance has led to the adoption of tier 3 and tier 4 data centers, driving the growth of the market. However, the market also faces challenges such as rising construction costs and the shortage of skilled professionals, which may restrain its growth to some extent. Recent developments include: February 2024: Equinix Inc. decided to invest USD 390 million in Africa over the next five years. The investment will focus on constructing new data centers and bolstering its existing operations, primarily in South Africa and the western regions of the continent.January 2023: Africa Data Centres, a subsidiary of Cassava Technologies, a prominent pan-African technology conglomerate, revealed plans for its second data center in Cape Town. Positioned in the northern region of the city, this new facility is set to accommodate an IT load of 20 MW. With construction already in progress, the center is slated for completion and operational status by 2024.. Key drivers for this market are: 4., Government Support for Data Center Development4.; Advent of Cloud, Big Data, and IoT Technologies Driving Investments. Potential restraints include: 4., Government Support for Data Center Development4.; Advent of Cloud, Big Data, and IoT Technologies Driving Investments. Notable trends are: Tier 3 Data Centers Holding Significant Market Share.

As of 2023, South Africa's population increased and counted approximately 62.3 million inhabitants in total, of which the majority inhabited Gauteng, KwaZulu-Natal, and the Western-Eastern Cape. Gauteng (includes Johannesburg) is the smallest province in South Africa, though highly urbanized with a population of over 16 million people according to the estimates. Cape Town, on the other hand, is the largest city in South Africa with nearly 3.43 million inhabitants in the same year, whereas Durban counted 3.12 million citizens. However, looking at cities including municipalities, Johannesburg ranks first. High rate of young population South Africa has a substantial population of young people. In 2024, approximately 34.3 percent of the people were aged 19 years or younger. Those aged 60 or older, on the other hand, made-up over 10 percent of the total population. Distributing South African citizens by marital status, approximately half of the males and females were classified as single in 2021. Furthermore, 29.1 percent of the men were registered as married, whereas nearly 27 percent of the women walked down the aisle. Youth unemployment Youth unemployment fluctuated heavily between 2003 and 2022. In 2003, the unemployment rate stood at 36 percent, followed by a significant increase to 45.5 percent in 2010. However, it fluctuated again and as of 2022, over 51 percent of the youth were registered as unemployed. Furthermore, based on a survey conducted on the worries of South Africans, some 64 percent reported being worried about employment and the job market situation.

The smart cities market will rise from USD 848.0 Billion in 2025 to USD 5,647.6 Billion by 2035, at a 28.2% CAGR, driven by urban growth and smart technologies.

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.

Spatial analysis at different levels can help understand spatial variation of human immunodeficiency virus (HIV) infection, disease drivers, and targeted interventions. Combining spatial analysis and the evaluation of the determinants of the HIV burden in Southern African countries is essential for a better understanding of the disease dynamics in high-burden settings.The study countries were selected based on the availability of demographic and health surveys (DHS) and corresponding geographic coordinates. We used multivariable regression to evaluate the determinants of HIV burden and assessed the presence and nature of HIV spatial autocorrelation in six Southern African countries.The overall prevalence of HIV for each country varied between 11.3% in Zambia and 22.4% in South Africa. The HIV prevalence rate was higher among female respondents in all six countries. There were reductions in prevalence estimates in most countries yearly from 2011 to 2020. The hotspot cluster findings show that the major cities in each country are the key sites of high HIV burden. Compared with female respondents, the odds of being HIV positive were lesser among the male respondents. The probability of HIV infection was higher among those who had sexually transmitted infections (STI) in the last 12 months, divorced and widowed individuals, and women aged 25 years and older.Our research findings show that analysis of survey data could provide reasonable estimates of the wide-ranging spatial structure of the HIV epidemic in Southern African countries. Key determinants such as individuals who are divorced, middle-aged women, and people who recently treated STIs, should be the focus of HIV prevention and control interventions. The spatial distribution of high-burden areas for HIV in the selected countries was more pronounced in the major cities. Interventions should also be focused on locations identified as hotspot clusters.

Background: To optimally allocate limited health resources in responding to the HIV epidemic, South Africa has undertaken to generate local epidemiological profiles identifying high disease burden areas. Central to achieving this, is the need for readily available quality health data linked to both large and small geographic areas. South Africa has relied on national population-based surveys: the Household HIV Survey and the National Antenatal Sentinel HIV and Syphilis Prevalence Survey (ANC) amongst others for such data for informing policy decisions. However, these surveys are conducted approximately every 2 and 3 years creating a gap in data and evidence required for policy. At subnational levels, timely decisions are required with frequent course corrections in the interim. Routinely collected HIV testing data at public health facilities have the potential to provide this much needed information, as a proxy measure of HIV prevalence in the population, when survey data is not available. The South African District health information system (DHIS) contains aggregated routine health data from public health facilities which is used in this article.Methods: Using spatial interpolation methods we combine three “types” of data: (1) 2015 gridded high-resolution population data, (2) age-structure data as defined in South Africa mid-year population estimates, 2015; and (3) georeferenced health facilities HIV-testing data from DHIS for individuals (15–49 years old) who tested in health care facilities in the district in 2015 to delineate high HIV disease burden areas using density surface of either HIV positivity and/or number of people living with HIV (PLHIV). For validation, we extracted interpolated values at the facility locations and compared with the real observed values calculating the residuals. Lower residuals means the Inverse Weighted Distance (IDW) interpolator provided reliable prediction at unknown locations. Results were adjusted to provincial published HIV estimates and aggregated to municipalities. Uncertainty measures map at municipalities is provided. Data on major cities and roads networks was only included for orientation and better visualization of the high burden areas.Results: Results shows the HIV burden at local municipality level, with high disease burden in municipalities in eThekwini, iLembe and uMngundgudlovu; and around major cities and national routes.Conclusion: The methods provide accurate estimates of the local HIV burden at the municipality level. Areas with high population density have high numbers of PLHIV. The analysis puts into the hand of decision makers a tool that they can use to generate evidence for HIV programming. The method allows decision makers to routinely update and use facility level data in understanding the local epidemic.

Market Size and Growth: The hospitality industry in South Africa is experiencing steady growth, reaching a market size of USD 1.36 billion in 2025. It is projected to continue expanding at a CAGR of 4.43% from 2025 to 2033, driven by factors such as increasing tourism, government initiatives, and growing demand for luxury accommodations. The industry is segmented into various types, including chain hotels, independent hotels, service apartments, budget and economy hotels, mid and upper mid-scale hotels, and luxury hotels. Key Trends and Challenges: The hospitality industry in South Africa is influenced by several trends, including the rise of online booking platforms, the growing popularity of eco-tourism, and the increasing focus on personalized experiences. However, the industry also faces challenges, such as rising costs, competition from the informal sector, and the impact of economic downturns. The major companies operating in the market include Hilton Worldwide Holdings Inc., InterContinental Hotels Group, Marriott International Inc., and Accor SA. Market concentration is relatively low, with a diverse range of players in the market and no single dominant competitor. Recent developments include: In March 2022, Kasada announced the purchase of the Cap Grace Hotel in Cape Town, South Africa. Kasada's hotel acquisition marks the company's first foray into the South African hotel operator market. It also helps Kasada's strategy of expanding into all major cities in Sub-Saharan Africa., In May 2022, Millat Investments took over the iconic Winston Hotel in Rosebank, Johannesburg, South Africa, adding another key property to its rapidly expanding hospitality portfolio. The purchase of the Winston property comes on the heels of the successful Africa Travel Indaba in Durban, an event aimed at reviving tourism to South Africa and the continent following the global pandemic lockdown.. Key drivers for this market are: Rising Tourism in the United Arab Emirates Bolsters the Growth in Hospitality Sector, The Rise in the Mice Industry in the United Arab Emirates Drives the Hospitality Sector. Potential restraints include: High Rentals in the United Arab Emirates Pose a Restraint to the Hospitality Sector. Notable trends are: Growth in Tourism Sector in South Africa is Expected to Outpace Hospitality Industry.

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.