The 1970 South African Population Census was an enumeration of the population and housing in South Africa.The census collected data on dwellings and individuals' demographic, migration, family and employment details.

ABSTRACT: The 1970 South African Population Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria). Variables include: Particulars of dwellings- type of dwelling, number of rooms, and ownership; Particulars of person- relationship within household, sex, age, marital status, population group, birthplace, country of citizenship, duration of residence at normal dwelling, religion/denomination, languages and literacy, level of education, sport and recreation, occupation, work status, identity of employer, economic sector and income; Particulars of the family including children at boarding school, university or undergoing military training.

In October 2001, South Africans were enumerated to collect information on persons and households throughout the country, using a uniform methodology.
Household data collected included data on each household and each person present in the household on Census night, as well as data on services available to the household. Data on household residents, and residents of hostels and the other types of collective living quarters was also captured, as well as data on individuals who spent census night in institutions and hotels.

No description is available. Visit https://dataone.org/datasets/peggym.113.6 for complete metadata about this dataset.

Abstract

The Recorded Live Births (RLB) dataset provides information on all registered live births in South Africa. The RLB dataset is part of a regular series of cumulative releases that is published by Statistics South Africa (Stats SA) and based on data collected through the civil registration system. RLB 1998-2023 is the latest release in the series, which replaces and includes the data of the previous release (i.e. RLB 1998-2023 includes the data from RLB 1998-2022). The main objective of this dataset is to outline emerging trends and differentials in birth occurrence and registration, by selected socio-demographic and geographic characteristics, in South Africa over time. Reliable birth statistics are necessary for population health assessment, health policy, service planning and programme evaluation. These data are particularly critical for planning, implementing and monitoring development policies and programmes such as the National Development Plan (NDP) in South Africa, Agenda 2063 at regional level and the Sustainable Development Goals (SDGs) at international level.

Geographic coverage

This dataset has national coverage.

Analysis unit

Individuals

Universe

This dataset is based on information on birth occurences from the South African civil registration system. It covers all birth notification forms from the Department of Home Affairs (DHA) for births that occurred from 1998-2023 and that were registered between January 2023 and February 2024. The dataset excludes all births that occurred in South Africa but where the parents were non-South African citizens or not permanent residents.

Kind of data

Administrative records

Mode of data collection

Other

Research instrument

The form used to record live births is the Notice of Birth form of the Department of Home Affairs (Form DHA-24). Previously there were three forms used:

Form BI-24 (for births registered within the first year) Form BI-24/1 (for births registered between a year and 14 years) Form BI-24/15 (for births registered after 15 years or more)

Data appraisal

The Statistics South Africa metadata document mentions two birth forms, however this seems incorrect. There is only one form used, the DHA-24.

No description is available. Visit https://dataone.org/datasets/peggym.126.18 for complete metadata about this dataset.

No description is available. Visit https://dataone.org/datasets/peggym.130.8 for complete metadata about this dataset.

Abstract

The Community Survey (CS) is a nationally representative, large-scale household survey which was conducted from February to March 2007. The Community Survey is designed to provide information on the trends and levels of demographic and socio-economic data, such as population size and distribution; the extent of poor households; access to facilities and services, and the levels of employment/unemployment at national, provincial and municipality level. The data can be used to assist government and the private sector in the planning, evaluation and monitoring of programmes and policies. The information collected can also be used to assess the impact of socio-economic policies and provide an indication as to how far the country has gone in its strides to eradicate poverty.

Censuses 1996 and 2001 are the only all-inclusive censuses that Statistics South Africa has thus far conducted under the new democratic dispensation. Demographic and socio-economic data were collected and the results have enabled government and all other users of this information to make informed decisions. When cabinet took a decision that Stats SA should not conduct a census in 2006, it created a gap in information or data between Census 2001 and the next Census scheduled to be carried out in 2011. A decision was therefore taken to carry out the Community Survey in 2007.

The main objectives of the survey were: · To provide estimates at lower geographical levels than existing household surveys; · To build human, management and logistical capacities for Census 2011; and · To provide inputs into the preparation of the mid-year population projections.

The wider project strategic theme is to provide relevant statistical information that meets user needs and aspirations. Some of the main topics that are covered by the survey include demography, migration, disability and social grants, educational levels, employment and economic activities.

Geographic coverage

The survey covered the whole of South Africa, including all nine provinces as well as the four settlement types - urban-formal, urban-informal, rural-formal (commercial farms) and rural-informal (tribal areas).

Analysis unit

Households

Universe

The Community Survey covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions. However, an approximation of the out-of-scope population was made from the 2001 Census and added to the final estimates of the CS 2007 results.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

The sampling procedure that was adopted for the CS was a two-stage stratified random sampling process. Stage one involved the selection of enumeration areas, and stage tow was the selection of dwelling units.

Since the data are required for each local municipality, each municipality was considered as an explicit stratum. The stratification is done for those municipalities classified as category B municipalities (local municipalities) and category A municipalities (metropolitan areas) as proclaimed at the time of Census 2001. However, the newly proclaimed boundaries as well as any other higher level of geography such as province or district municipality, were considered as any other domain variable based on their link to the smallest geographic unit - the enumeration area.

The Frame

The Census 2001 enumeration areas were used because they give a full geographic coverage of the country without any overlap. Although changes in settlement type, growth or movement of people have occurred, the enumeration areas assisted in getting a spatial comparison over time. Out of 80 787 enumeration areas countrywide, 79 466 were considered in the frame. A total of 1 321 enumeration areas were excluded (919 covering institutions and 402 recreational areas).

On the second level, the listing exercise yielded the dwelling frame which facilitated the selection of dwellings to be visited. The dwelling unit is a structure or part of a structure or group of structures occupied or meant to be occupied by one or more households. Some of these structures may be vacant and/or under construction, but can be lived in at the time of the survey. A dwelling unit may also be within collective living quarters where applicable (examples of each are a house, a group of huts, a flat, hostels, etc.).

The Community Survey universe at the second-level frame is dependent on whether the different structures are classified as dwelling units (DUs) or not. Structures where people stay/live were listed and classified as dwelling units. However, there are special cases of collective living quarters that were also included in the CS frame. These are religious institutions such as convents or monasteries, and guesthouses where people stay for an extended period (more than a month). Student residences - based on how long people have stayed (more than a month) - and old-age homes not similar to hospitals (where people are living in a communal set-up) were treated the same as hostels, thereby listing either the bed or room. In addition, any other family staying in separate quarters within the premises of an institution (like wardens' quarters, military family quarters, teachers' quarters and medical staff quarters) were considered as part of the CS frame. The inclusion of such group quarters in the frame is based on the living circumstances within these structures. Members are independent of each other with the exception that they sleep under one roof.

The remaining group quarters were excluded from the CS frame because they are difficult to access and have no stable composition. Excluded dwelling types were prisons, hotels, hospitals, military barracks, etc. This is in addition to the exclusion on first level of the enumeration areas (EAs) classified as institutions (military bases) or recreational areas (national parks).

The Selection of Enumeration Areas (EAs)

The EAs within each municipality were ordered by geographic type and EA type. The selection was done by using systematic random sampling. The criteria used were as follows: In municipalities with fewer than 30 EAs, all EAs were automatically selected. In municipalities with 30 or more EAs, the sample selection used a fixed proportion of 19% of all sampled EAs. However, if the selected EAs in a municipality were less than 30 EAs, the sample in the municipality was increased to 30 EAs.

The Selection of Dwelling Units

The second level of the frame required a full re-listing of dwelling units. The listing exercise was undertaken before the selection of DUs. The adopted listing methodology ensured that the listing route was determined by the lister. Thisapproach facilitated the serpentine selection of dwelling units. The listing exercise provided a complete list of dwelling units in the selected EAs. Only those structures that were classified as dwelling units were considered for selection, whether vacant or occupied. This exercise yielded a total of 2 511 314 dwelling units.

The selection of the dwelling units was also based on a fixed proportion of 10% of the total listed dwellings in an EA. A constraint was imposed on small-size EAs where, if the listed dwelling units were less than 10 dwellings, the selection was increased to 10 dwelling units. All households within the selected dwelling units were covered. There was no replacement of refusals, vacant dwellings or non-contacts owing to their impact on the probability of selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

Consultation on Questionnaire Design Ten stakeholder workshops were held across the country during August and September 2004. Approximately 367 stakeholders, predominantly from national, provincial and local government departments, as well as from research and educational institutions, attended. The workshops aimed to achieve two objectives, namely to better understand the type of information stakeholders need to meet their objectives, and to consider the proposed data items to be included in future household surveys. The output from this process was a set of data items relating to a specific, defined focus area and outcomes that culminated with the data collection instrument (see Annexure B for all the data items).

Questionnaire Design The design of the CS questionnaire was household-based and intended to collect information on 10 people. It was developed in line with the household-based survey questionnaires conducted by Stats SA. The questions were based on the data items generated out of the consultation process described above. Both the design and questionnaire layout were pre-tested in October 2005 and adjustments were made for the pilot in February 2006. Further adjustments were done after the pilot results had been finalised.

Cleaning operations

Editing The automated cleaning was implemented based on an editing rules specification defined with reference to the approved questionnaire. Most of the editing rules were categorised into structural edits looking into the relationship between different record type, the minimum processability rules that removed false positive readings or noise, the logical editing that determine the inconsistency between fields of the same statistical unit, and the inferential editing that search similarities across the domain. The edit specifications document for the structural, population, mortality and housing edits was developed by a team of Stats SA subject-matter specialists, demographers, and programmers. The process was successfully

This dataset includes imputation for missing data in key variables in the ten percent sample of the 2001 South African Census. Researchers at the Centre for the Analysis of South African Social Policy (CASASP) at the University of Oxford used sequential multiple regression techniques to impute income, education, age, gender, population group, occupation and employment status in the dataset. The main focus of the work was to impute income where it was missing or recorded as zero. The imputed results are similar to previous imputation work on the 2001 South African Census, including the single ‘hot-deck’ imputation carried out by Statistics South Africa.

No description is available. Visit https://dataone.org/datasets/peggym.138.2 for complete metadata about this dataset.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

This contains data from 1970 until 1978 and then again for 1983

As of January 2024, there were 45.34 million active internet users in South Africa. According to the same report, close to 26 million internet users in the country used social media, around 42.8 percent of the total population. The future of internet usage in South Africa: projected growth and mobile dominance South Africa's digital population grew significantly during the last decade. In 2023, almost 44 million people were connected to the internet, up from around 25 million in 2013. Furthermore, the majority of the South African population, specifically 78.7 percent, utilized mobile devices to access the internet in 2022. This proportion will increase to over 90 percent by 2027. Additionally, the number of mobile internet users in South Africa was almost 47.8 million in 2022. Social media usage in South Africa: popularity and demographics The country's most popular social media platform during the third quarter of 2022 was Meta’s instant messaging application WhatsApp. Facebook and Instagram ranked second and third among South African internet users. Moreover, a closer look into the demographics of social media users in the country reveals that people between the ages of 25 to 34 years made up the highest share of users in South Africa.

This dataset is not spatially explicit up until 2006. There was no survey conduectd in 2008

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration.
The results are used to ensure:
• equity in distribution of government services
• distributing and allocating government funds among various regions and districts for education and health services
• delineating electoral districts at national and local levels, and
• measuring the impact of industrial development, to name a few
The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis.

Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included:
- To provide statistics on population, demographic, social, economic and housing characteristics;
- To provide a base for the selection of a new sampling frame;
- To provide data at lowest geographical level; and
- To provide a primary base for the mid-year projections.

DATASET: Alpha version 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/). REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. DATE OF PRODUCTION: January 2013

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated South Africa’s yearly population growth has been fluctuating since 2013, with the growth rate dropping below the world average in 2024. The majority of people lived in the borders of Gauteng, the smallest of the nine provinces in terms of land area. The number of people residing there amounted to 16.6 million in 2023. Although the Western Cape was the third-largest province, the city of Cape Town had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the fourth quarter of 2024, the unemployment rate reached close to 60 percent and 384 percent among people aged 15-24 and 25–34 years, respectively. In the same period, some 27 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 74.3 percent.

ABSTRACT: During October 1996, under the Census 1996 motto "Count us in", a hundred thousand employees of Statistics South Africa fanned out across the cities, towns, townships, informal settlements, villages, farms and rural areas of the country. Their task was to record the details of people living in more than nine million households of South Africa, as well as those in hostels, hotels and prisons. By contrast, Census 1996 was the first nation wide census since the splitting up of the country under apartheid after 1970 and sought to apply the same methodology to everyone: visiting the household, and obtaining details about all its members from a representative who was either interviewed, or else filled in the questionnaire in the language of choice.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

From the late 1970's the KNP made use of aerial surveys to estimate ungulate populations in the KNP. In the beginning the entire KNP was subdivided in smaller blocks and the entire area was covered. This method was very time and labour intensive, expensive and the estimates had no confidence intervals associated with them. It was also suspected that this technique resulted in an undercount bias. For these reasons in 1997 it was decided to change to a sample count using transects placed from east to west and use the distance statistical method to analyse the data. There was no survey in 2009 or in 2011