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.

Cairo, in Egypt, ranked as the most populated city in Africa as of 2025, with an estimated population of over 23 million inhabitants living in Greater Cairo. Kinshasa, in Congo, and Lagos, in Nigeria, followed with some 17.8 million and 17.2 million, respectively. Among the 15 largest cities in the continent, another one, Kano, was located in Nigeria, the most populous country in Africa. Population density trends in Africa As of 2023, Africa exhibited a population density of 50.1 individuals per square kilometer. Since 2000, the population density across the continent has been experiencing a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 58.5 by the year 2030. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 627 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 45.5 percent in 2024. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating only around a third of the total population then. This trajectory is projected to continue its rise in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2024, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. As of the same year, Africa's population was estimated to expand by 2.27 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.3 percent, reaching its pinnacle at 2.63 percent in 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

Actual value and historical data chart for South Africa Population In Largest City

Johannesburg was the wealthiest city in Africa as of 2021. South Africa's biggest city held *** billion U.S. dollars in private wealth, while Cape Town followed with *** 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.

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;

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.; ;

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 below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

In 2024, Pietermaritzburg in South Africa ranked first in the crime index among African cities, scoring **** index points. The six most dangerous areas on the continent were South African cities. Furthermore, Pretoria and Johannesburg followed, with a score of **** and **** points, respectively. 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). Contact crimes are common in South Africa Contact crimes in South Africa include violent crimes such as murder, attempted murder, and sexual offenses, as well as common assault and robbery. In fiscal year 2022/2023, the suburb of Johannesburg Central in the Gauteng province of South Africa had the highest number of contact crime incidents. Common assault was the main contributing type of offense to the overall number of contact crimes. Household robberies peak in certain months In South Africa, June, July, and December experienced the highest number of household robberies in 2023. June and July are the months that provide the most hours of darkness, thus allowing criminals more time to break in and enter homes without being detected easily. In December, most South Africans decide to go away on holiday, leaving their homes at risk for a potential break-in. On the other hand, only around ** percent of households affected by robbery reported it to the police in the fiscal year 2022/2023.

Amidst the COVID-19 vaccination, Twitter is one of the most popular platforms for discussions about the COVID-19 vaccination. These types of discussions most times lead to a compromise of public confidence toward the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis at the provincial, country, or continent level without considering the local communities. The aim of this study is to use clustered geo-tagged Twitter posts to inform city-level variations in sentiments toward COVID-19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). VADER, an NLP pre-trained model was used to label the Twitter posts according to their sentiments with their associated intensity scores. The outputs were validated using NB (0.68), LR (0.75), SVMs (0.70), DT (0.62), and KNN (0.56) machine learning classification algorithms. The number of new COVID-19 cases significantly positively correlated with the number of Tweets in South Africa (Corr = 0.462, P < 0.001). Out of the 10 topics identified from the tweets using the LDA model, two were about the COVID-19 vaccines: uptake and supply, respectively. The intensity of the sentiment score for the two topics was associated with the total number of vaccines administered in South Africa (P < 0.001). Discussions regarding the two topics showed higher intensity scores for the neutral sentiment class (P = 0.015) than for other sentiment classes. Additionally, the intensity of the discussions on the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (P < 0.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (P = 0.003), (P = 0.002), and (P < 0.001) for positive, negative, and neutral sentiments classes, respectively. The outcome of this research showed that clustered geo-tagged Twitter posts can be used to better analyse the dynamics in sentiments toward community–based infectious diseases-related discussions, such as COVID-19, Malaria, or Monkeypox. This can provide additional city-level information to health policy in planning and decision-making regarding vaccine hesitancy for future outbreaks.

District Municipalities 2018 is a shapefile and attributes information of all the district municipalities in South Africa. In the hierarchy of local government structure, the District Municipalities are contained within Provinces, then District Municipalities contain Local Municipalities. District Municipalities 2018 was published in the Year 2018 after the municipal boundaries had minor technical adjustments. The district (Category C) municipalities are municipalities that are comprised of local (Category B) municipalities. The Metropolitan (Category A) Municipalities are municipalities with the major cities as the core (e.g. City of Johannesburg) and they are outside the District Municipalities. When the boundaries of local municipalities change and affect the boundary of district municipalities, the new district municipal boundary is generated. In the District Municipalities 2018 shapefile there are 44 District Municipalities and 8 Metropolitan Municipalities. Note that Metropolitan Municipalities are included in the District Municipalities shapefile to ensure that the layer is continuous throughout the country. If the Metropolitan Municipalities were left out, there will be void spaces in the layer.

With a business score of ****, Nairobi ranked as the best African city for startups in 2021, according to data provided by StartupBlink. Johannesburg and Cape Town followed with **** points each. South Africa ranked first in Africa and 156th worldwide in the quantity ranking. The business score is a mix of business and economic indicators at the national level, discounted for cities that have not reached a critical mass either for quantity or quality scores.

Abstract

The Measuring Living Standards in Cities (MLSC) survey is a new instrument designed to enhance understanding of cities in Africa and support evidence based policy design. The instrument was developed under the World Bank's Spatial Development of African Cities Program, and was piloted in Dar es Salaam (Tanzania) and Durban (South Africa) over the course of 2014/15.

Geographic coverage

The survey covered households in Durban, South Africa.

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The total nominal sample of 2400 households in Durban was, selected in four stages rather than two. These were: (i) selection of 200 EAs with probability proportional to size; (ii) large EAs were segmented into area units of roughly the same size (using GIS data), and one segments was selected randomly with equal probability; (iii) following listing of buildings, 15 were selected using systematic equal probability sampling; (iv) households in the 15 selected buildings were listed so that 12 households could then be selected per EA by systematic equal probability sampling. This approach reduced the need to enter as many buildings as would otherwise have been necessary, without reducing the representativeness of the sample.

For further details on sampling strategy, see Survey Methodology section of World Measuring Living Standards within Cities report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

The refusal and non-completion rate was 41 percent.

Interviews were conducted with multiple stakeholders in South Africa so as to investigate barriers and opportunities for energy services delivery to informal settlements in the country during the 2010s, although account was also taken of the historical and political context that impacts on energy delivery in South Africa. The interviews were conducted in South Africa, and took place in Cape Town, Johannesburg, and Polokwane. The interviews are with multiple categories of stakeholders, namely: 1.) the electricity supply industry; 2.) the national government; 3.) the provincial government; 4.) the municipal government; 5.) academics; and 6.) NGO/civil society actors. The range of interviewee categories was aimed at constructing a rounded and in-depth qualitative picture of barriers and opportunities for energy service delivery in situations of housing and settlement informality.

Energy is a critical enabler of development. Energy transitions, involving changes to both systems of energy supply and demand, are fundamental processes behind the development of human societies and are driven by technical, economic, political and social factors. Historical specificities and geography influence the character of energy transitions. In a world that is experiencing unprecedented urban growth, modern urbanised societies are highly dependent on energy. By 2030, more than 50% of people in developing countries are expected to live in cities, which is a figure set to grow to 66% by 2050. This urbanisation trend is even more prominent in South Africa, where 64% of its population already live in urban areas and is expected to rise to 70% by 2030. South African cities are highly dependent on energy, and access to and the provision of energy services affects urban energy transitions. Furthermore, access to affordable and reliable energy services is fundamental to reducing poverty and advancing economic growth. In response to this, many cities in South Africa and beyond have adopted sustainable energy provision strategies and solutions as a way of promoting economic development and greening of urban economies. However, Sustainable Energy Africa (SEA)'s State of the Energy in South African Cities report (2015) identifies that much remains to be done in order to transform South African cities towards a more sustainable urban energy profile, which is in turn aimed at improving welfare, supporting economic activity, creating 'green collar' and other jobs, and reducing carbon emissions. The project's focus on urban energy transitions is therefore both timely and necessary.

Cities in South Africa are notable for their central role in the governance of energy. Municipalities are constitutionally mandated to serve as electricity distributors and are responsible for maintaining infrastructure, providing new connections and setting minimum service level standards as well as pricing and subsidies levels for poor consumers. Therefore, municipalities have become major actors in urban energy infrastructures. Nonetheless, systemic change is hampered by: a.) the lack of integrated energy strategies; b.) the declining performance of energy supply networks in South Africa; c.) the high carbon intensity of South Africa's energy supply, at a time when South Africa is actively seeking to decarbonize the economy; d.) a stalled level of electrification in certain poor urban areas in South African cities; and e.) the continued prevalence of energy poverty, even in grid-connected South African urban households. A key issue is the continued prevalence of a focus on energy supply, as opposed to the broader and more complex notion of energy services.

It is clear that municipal processes and systems will have to change in order for energy transitions to occur. This project investigates the dynamics and co-evolution of municipal processes so as to create pathways to new, greener and fairer urban energy configurations. The project establishes a dialogue between work on socio-technical transitions and on energy geographies to analyze and identify energy transition pathways towards municipal-scale energy services regimes. The project's embeddedness in ongoing urban energy transition work will provide an evidence-base for co-designing pathways for energy services provision in South Africa's cities, alongside exploring opportunities in new energy configurations for transformations to urban green economies. This research project consists of SA research partners (the University of Cape Town's Energy Research Centre) and UK partners (King's College London; the University of Manchester; Plymouth University and the University of Sussex), together with the local energy transition expertise of Sustainable Energy Africa.

Southern African cities face several challenges including management of rapid urbanization, rising populations, expanding informal settlements; adequate water and other service provision, and a host of governance challenges. Climate change and variability add a compounding effect to this complex, multi stressor context. Addressing the complexity requires an understanding of urban ecosystems functioning and interactions amongst the built and natural environment (climate) and human systems. In this paper we argue that learning is essential for cities to be resilient to current and future challenges. We profile the Future Resilience for African CiTies And Lands (FRACTAL) project which contributed towards climate resilient development by providing relevant climate information for decision-making at the city regional scale in southern Africa. Following FRACTAL’s city-to-city learning approach of sharing good practices, knowledge and experiences framed around transdisciplinary research, the study cities of Harare, Lusaka, Windhoek and Durban conducted city learning exchange visits between 2017 and 2018. We used a mixed methods approach to collect and analyze historical climate and hydrological data and current socio-economic and development data among the cities. A qualitative, in-depth, case study comparative analysis was used to identify similarities and differences as well as lessons drawn from the learning process during the city exchanges and these were complimented by desktop studies. Results showed water scarcity, large informal settlements, reliance on external water and energy sources, inadequate protection of ecologically sensitive resources and service provision as some of the common complications in the cities. Several lessons and transferable practices learnt from the cities included effective water conservation and waste management and the use of public-private partnerships in Windhoek, community engagements in Durban and Lusaka while lessons on decisive leadership in dealing with informal settlements emanated from Harare’s limited informal settlements. Lastly, Durban’s Adaptation Charter and integrated climate planning provided lessons for biodiversity protection and mainstreaming climate change at city governance level. While we recognize that cities are context-specific we consider these good practices as being broadly transferable to other southern African cities. We conclude that social, experiential and structured learning can be an innovative way of multi-stakeholder engagement and a useful approach to increase city resilience planning across southern Africa and cities that face similar developmental challenges.

Abstract

This study covers Cape Town, one of four African cities surved between 2013 and 2019 by the African Center for Cities. The African Center for cities is based at the University of Cape Town and is a partner of the Hungry Cities Partnership (HCP).

The HCP studies include household data on food insecurity, household food purchasing dynamics, nutritional discounting taking place in households, foods consumed and multidimensional measures of poverty. The household data is complimented with household member data and food retailer (vendor) data, including infomation on vendor employees.

The Hungry Cities Partnership is an international network of cities and city-based partner organizations which focuses on the relationships between rapid urbanization, informality, inclusive growth and urban food systems in the Global South.

Geographic coverage

The household sample is deisgned to be representative of the city of Cape Town.

Analysis unit

Households and individuals

Universe

Households and Vendors in Cape Town.

Kind of data

Sample survey data

Sampling procedure

Household sampling: the sample for the 2013 Food Security Study was designed to be two-stage and stratified, using a random probability sample of 2,500 Cape Town households .Enumeration areas were taken from Statistics SAs master lists and used as the primary sampling unit. Households were the secondard sampling unit. Strafitication was done by income group of the household. Some areas were over-sampled to improve accuracy. In each of the drawn EAs, six households were systematically selected, with the exception of the EAs in DuNoon (where 10 households were systematically selected). Starting points were allocated to ensure coverage of the entire EA. The household was defined by everyone who regularly "ate from the same pot".

Vendor sampling: The survey team documentation reads as follows: A strategy of maximum variation sampling was used to ensure a mix of commercial, formal residential, informal residential, mixed formal and informal residential, and industrial retail sites. In these areas, the main street served as the primary site of research. Informal food vending businesses were selected randomly. In total, 1,018 food vendors were interviewed over a three-week period.

For more on sampling see the study documentation.

Sampling deviation

In cases, xenophobic violence made vendor interviews dangerous in some areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are two questionnaires per city, a household questionnaire and a vendor questionnaire. The household questionnaire has a subsection for household members (persons), and the vendor quesitonnaire has a subsection for employees. Answers to these subsections are supplied in separete datafiles, which can be matched to (merged with) the questoinnaire as necessary.

Vendor surveys were administered to the person directly responsible for the running of the business using handheld tablets. The household survey was administered to a senior adult member of the household, someone who could speak for the household.

Note that for the household questionnaire, the question 8 section changed slightly for Cape Town, in that the answers are not stored in 'wide' format like the other cities. Rather, if a respondent provided more than one answer, additional variables were created. This is why the dataset has less variables and the question 8 section looks different. Only up to three locations were recorded in section 8, even if the repondent mentioned more than 3 sources of food.

Cleaning operations

Datafiles were received by DataFirst in SPSS (.sav) and Excel (.xlsx) format. Variables had to be named and variable labels were taken from question text. Variables were named accoriding to question number and subject matter, in a hierachical fasion.

An effort was made to keep question numbers consistent across cities where the same questions were asked for the 2013-2019 surveys. For the vendor data, Cape Town, Maputo and Nairobi had almost identical questionnaires and so the question numbers were naturally the same across these cities (harmonized). For the household data, Maputo, Nairobi and Windhoek were similar and could be harmonized. This means users could try stack these datafiles. The Cape Town household questionnaire was more different to the others, and variable names would required adjusting to match with the other cities.

Missing values of 97, 98, and 99 were converted to -97, -98 and -99. There were some question numbers wrong in the vendor data questionnaires (typos) that were corrected.

Data appraisal

It seems that there is slight mismatch between the Cape Town household questionnaire provided and the lists in the datafile, for an example see the question 15 income sources.

In the Cape Town household data, data was not collected for the quetion 10.c and 10.d, about crops and time to travel to crops.

In general, the lists change subtly between cities, for example the lists of foods in question 8 of the household data. As such the user should take caution when comparing across cities, and refer to the questionnaires. When the lists differed, list item letters (a-z) were left in the variable name as a second way for the user to check that the data match the questionnaire in the expected way. In Cape Town an answer to questions 15a and b "support from relatives" was captured although it does not reflect in the questionnaire.

Malaysia Tourist Arrival: Sightseeing In Cities: South Africa data was reported at 84.100 % in 2015. This records an increase from the previous number of 80.400 % for 2014. Malaysia Tourist Arrival: Sightseeing In Cities: South Africa data is updated yearly, averaging 84.100 % from Dec 2001 (Median) to 2015, with 15 observations. The data reached an all-time high of 98.000 % in 2013 and a record low of 50.000 % in 2003. Malaysia Tourist Arrival: Sightseeing In Cities: South Africa data remains active status in CEIC and is reported by Tourism Malaysia. The data is categorized under Global Database’s Malaysia – Table MY.Q009: Tourist Arrivals By Major Activities Engaged.

In 2023, according to data provided by StartupBlink, the best city for startups in Africa was Lagos, in Nigeria, with a total score of **** points. The largest city in Africa and an important financial hub for Nigeria and the whole continent, Lagos ranked **** among 1,000 cities worldwide. Cairo, in Egypt, and Cape Town, in South Africa, followed as leading cities for startups on the African continent.

Part of the British Academy Knowledge Frontiers: International Interdisciplinary Research funding programme. Funded by the Department for Business, Energy and Industrial Strategy (BEIS). We are one of nine research projects bringing together novel, interdisciplinary ideas from across the humanities and social sciences in collaboration with the natural, medical and engineering sciences to propose solutions to international challenges past, present and future.• Theme of ‘What is a good city?’• 2-year projects with interdisciplinary and international teams• Projects “strengthen understanding of international challenges … and engage with questions concerning the relationship between expertise, public understanding and policy delivery internationally.” (British Academy)This research project investigated the challenge of food insecurity in cities as experienced by migrant communities and explored the role of traditional foods in well-being. The global population is increasingly urbanised, with Sub-Saharan Africa experiencing the fastest rate of urban population growth. South Africa is a centre for regional migration, with Johannesburg being the destination for the largest proportion of both within-country and international migrants. The project focused on two migrant groups in Johannesburg - South African rural-to-urban migrants and international regional migrants. Urban populations are dependent on food markets for daily sustenance and nutrition, hence access to affordable, acceptable and nutritious food through markets must be prioritised by cities. By identifying the drivers of food choice in urban migrant and immigrant populations around traditional foods, barriers to consumption and engaging with those involved in knowledge in urban planning and development, this project aimed to go some way towards tackling the problem of urban food insecurity and malnutrition.

Reliable market accessibility data is critical to developing agricultural policies and investment plans for ensuring smallholder farmers’ market participation and their profitable farming, yet this data is less frequently updated. Most of the publicly available data are outdated and hard to reflect the rapid development of transportation infrastructure in African countries. For this, using a newly available accessibility model input dataset, such as new land cover data from satellites, crowdsourced road network data, and the updated population of major human settlements in Tanzania are used to update the existing market accessibility data and provides new market accessibility data layers benchmarking around the year 2015. The dataset includes three data layers representing travel time to the nearest market of five sizes (population of 20K, 50K, 100K), respectively, on 1 arc-minute (~1km) grids in Tanzania.