Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Key information about South Africa population

• The South Africa population reached 63.0 million people in Jun 2024, compared with the previously reported figure of 62.2 million people in Jun 2023
• The data reached an all-time high of 63.0 million people in Jun 2024 and a record low of 16.4 million people in Jun 1960

• In the latest reports, South Africa Unemployment Rate dropped to 32.1 % in Sep 2024
• Monthly earnings of the South Africa population was 1,547.4 USD in Aug 2024
• South Africa Labour Force Participation Rate dropped to 60.2 % in Sep 2024

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

Abstract

Every person, household and institution present in South Africa on Census Night, 9-10 October 1996, should have been enumerated in Census '96. The intent was to provide a count of all persons present within the territory of the Republic of South Africa at that time. More specifically, the purpose of this census was to collect, process and disseminate detailed statistics on population size, composition and distribution at a small area level. The 1996 South African population Census contains data collected on HOUSEHOLDS and INSTITUTIONS: dwellling type, home ownership, household assets, access to services and energy sources; INDIVIDUALS: age, population group, language, religion, citizenship, migration, fertility, mortality and disability; and economic characteristics of individuals, including employment activities and unemployment.

Geographic coverage

The South African Census 1996 has national coverage.

Analysis unit

The units of analysis for the South Africa Census 1996 were households, individuals and institutions

Universe

The South African Census 1996 covered every person present in South Africa on Census Night, 9-10 October 1996 (except foreign diplomats and their families).

Kind of data

Census/enumeration data [cen]

Sampling procedure

The data in the South African Census 1996 data file is a 10% unit level sample drawn from Census 1996 as follows:

1) Households: • A 10% sample of all households (excluding special institutions and hostels)

2) Persons: • A 10% sample of all persons as enumerated in the 1996 Population Census in South Africa

The census household records were explicitly stratified according to province and district council. Within each district council the records were further implicitly stratified by local authority. Within each implicit stratum the household records were ordered according to the unique seven-digit census enumerator area number, of which the first three digits are the (old) magisterial district number.

Mode of data collection

Face-to-face [f2f]

Research instrument

Different methods of enumeration were used to accommodate different situations and a variety of questionnaires were used. The information collected with each questionnaire differed slightly. The questionnaires used were as follows:

Questionnaire 1: (Household and personal questionnaire) This questionnaire was used in private households and within hostels which provided family accommodation. It contained 50 questions for each person and 15 for each household. Every household living in a private dwelling should have been enumerated on a household questionnaire. This questionnaire obtained information about the household and about each person who was present in the household on census night.

Questionnaire 2: (Summary book for hostels) This questionnaire was used to list all persons/households in the hostel and included 9 questions about the hostel. A summary book for hostels should have been completed for each hostel (that is, a compound for workers provided by mines, other employers, municipalities or local authorities). This questionnaire obtained information about the hostel and also listed all household and/or persons enumerated in the hostel. Some hostels contain people living in family groups. Where people were living as a household in a hostel, they were enumerated as such on a household questionnaire (which obtained information about the household and about each person who was present in the household on Census Night). On the final census file, they will be listed as for any other household and not as part of a hostel. Generally, hostels accommodate mostly individual workers. In these situations, persons were enumerated on separate personal questionnaires. These questionnaires obtained the same information on each person as would have been obtained on the household questionnaire. The persons will appear on the census file as part of a hostel. Some hostels were enumerated as special institutions and not on the questionnaires designed specifically for hostels.

Questionnaire 3: (Enumerator's book for special enumeration) This questionnaire was used to obtain very basic information for individuals within institutions such as hotels, prisons, hospitals etc. as well as for homeless persons. Only 6 questions were asked of these people. The questionnaire also included 9 questions about the institution. An enumerator's book for special enumeration should have been completed for each institution such as prisons and hospitals. This questionnaire obtained information on the institution and listed all persons present. Each person was asked a brief sub-set of questions - just 7 compared to around 50 on the household and personal questionnaires. People in institutions could not be enumerated as households. Homeless persons were enumerated during a sweep on census night using a special questionnaire. The results were later transcribed to standard enumerator's books for special enumeration to facilitate coding and data entry.

Response rate

The final calculation of the undercount of persons, based on analysis of a post-enumeration survey (PES) conducted shortly after the original census, was performed by Statistics South Africa. The estimated reponse rates are detailed below, both according to stratum and for the country as a whole. An estimated 10,7% of the people in South Africa, through the course of the census process, were not enumerated. For more information on the undercount and PES, see the publication, "Calculating the Undercount in Census '96", Statistics South Africa Report No. 03-01-18 (1996) which is included in the external documents section.

Undercount of persons by province (stratum, in %):

Western Cape 8,69
Eastern Cape 10,57
Northern Cape 15,59
Free State 8,75
KwaZulu-Natal 12,81
North West 9,37
Gauteng 9,99
Mpumalanga 10,09
Northern Province 11,28

South Africa 10,69

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents.

The data-sets contain the population surfaces, metadata, and data quality layers. The population data surfaces are stored as GeoTIFF files for use in remote sensing or geographic information system (GIS) software.

The data can also be explored via an interactive map - http://columbia.maps.arcgis.com/apps/View/index.html?appid=ce441db6aa54494cbc6c6cee11b95917

Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

South Africa ZA: Completeness of Death Registration with Cause-of-Death Information data was reported at 91.000 % in 2009. This records a decrease from the previous number of 92.300 % for 2008. South Africa ZA: Completeness of Death Registration with Cause-of-Death Information data is updated yearly, averaging 70.400 % from Dec 1992 (Median) to 2009, with 5 observations. The data reached an all-time high of 92.300 % in 2008 and a record low of 0.000 % in 1997. South Africa ZA: Completeness of Death Registration with Cause-of-Death Information 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. Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country.; ; World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/gho/data/node.main.1?lang=en).; Weighted average;

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation. The Post-Apartheid South African government has conducted four Censuses, in 1996, 2001, 2011 and 2022.

Geographic coverage

The South African Census 2022 has national coverage.

Analysis unit

Households and individuals

Universe

The South African Census 2022 covered every person present in South Africa on the Census reference night, midnight of 2-3 February 2022 including all de jure household members and residents of institutions.

Kind of data

Census/enumeration data

Sampling procedure

Census 2022 micro-data were sampled from the latest census data based on prescribed business requirements to ensure the generated estimates match the census population counts at local municipal level by age, sex, and population group. The Census 2022 Household record file and Person record file were used as a basis for the creation of the household and person sampling frame respectively. Households and household members in the in-scope households (records of the household questionnaire) formed part of the 10% sample. The municipal sample sizes for households were determined by taking 10% of the respective municipal measure of sizes. The household frame was implicitly stratified within each local municipality using household characteristics. Systematic samples of households were selected in each local municipality from the implicitly sorted household frame. The procedure used the allocated sample within each local municipality to produce the sample of households as well as the sampling weights, which are the inverse of the inclusion probabilities.

A Post-enumeration Survey (PES) is an independent sample survey that is conducted immediately after the completion of census enumeration to evaluate the coverage and content errors of the census. A sample of 840 sub-enumeration areas was selected across South Africa's nine provinces for the PES sampling frame. A mixed-mode data collection methodology was implemented to counteract the effects of the COVID19 pandemic. This was made possible by having integrated, digitally enabled survey processes with a geo-spatial information frame as a base.

Mode of data collection

Face-to-Face and Computer Assisted Personal and Telephone Interview

Research instrument

One questionnaire was used to capture the Census 2022 which included information on: 1. Households - particulars of the household (cover page of the household questionnaire) and household information sections of the household questionnaire. 2. Persons - particulars of the household member and person information sections of the household questionnaire. 3. Geography - geographic information associated with households and persons

A Post-Enumeration Survey was carried out after the census, which used a PES questionnaire.

Sampling error estimates

Coverage errors are a measure of how many persons or households were missed or counted more than once in the census. The final net coverage error rate relative to the final true population of 61,4 million persons is thus 31,1%. The final net coverage error rate relative to the final true population of 19,3 million households is 30,5%.

Content errors indicate the quality of key characteristics in the census. With respect to content errors, six variables were tested for consistency in terms of the responses that were recorded in the Census and the PES. The aggregated index of inconsistency was 7,5% for population group, 8,2% for sex, and 13,6% for age group, indicating a high level of agreement. The aggregated index of inconsistency for marital status was 23,0%, relationship to head of household was 34,8%, and country of birth was 42,3%, indicating moderate rates of agreement.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Population demographics, income statistics, and LSM levels across South Africa

The tabular and visual dataset focuses on South African basic education and provides insights into the distribution of schools and basic population statistics across the country. This tabular and visual data are stratified across different quintiles for each provincial and district boundary. The quintile system is used by the South African government to classify schools based on their level of socio-economic disadvantage, with quintile 1 being the most disadvantaged and quintile 5 being the least disadvantaged. The data was joined by extracting information from the debarment of basic education with StatsSA population census data. Thereafter, all tabular data and geo located data were transformed to maps using GIS software and the Python integrated development environment. The dataset includes information on the number of schools and students in each quintile, as well as the population density in each area. The data is displayed through a combination of charts, maps and tables, allowing for easy analysis and interpretation of the information.

VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in South Africa: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click here. For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. More information.

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

The 1998 South Africa Demographic and Health Survey (SADHS) is the first study of its kind to be conducted in South Africa and heralds a new era of reliable and relevant information in South Africa. The SADHS, a nation-wide survey has collected information on key maternal and child health indicators, and in a first for international demographic and health surveys, the South African survey contains data on the health and disease patterns in adults. Plans to conduct the South Africa Demographic and Health Survey go as far back as 1995, when the Department of Health National Health Information Systems of South Africa (NHIS/SA) committee, recognised serious gaps in information required for health service planning and monitoring. Fieldwork was conducted between late January and September 1998, during which time 12,247 households were visited, 17,500 people throughout nine provinces were interviewed and 175 interviewers were trained to interview in 11 languages. The aim of the 1998 South Africa Demographic and Health Survey (SADHS) was to collect data as part of the National Health Information System of South Africa (NHIS/SA). The survey results are intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving health services in the country. A variety of demographic and health indicators were collected in order to achieve the following general objectives: (i) To contribute to the information base for health and population development programme management through accurate and timely data on a range of demographic and health indicators. (ii) To provide baseline data for monitoring programmes and future planning. (iii) To build research and research management capacity in large-scale national demographic and health surveys. The primary objective of the SADHS is to provide up-to-date information on: basic demographic rates, particularly fertility and childhood mortality levels, awareness and use of contraceptive methods, breastfeeding practices, maternal and child health, awareness of HIV/AIDS, chronic health conditions among adults, lifestyles that affect the health status of adults, and anthropometric indicators.

Geographic coverage

The 1985 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

The 1985 Census dataset contains 9 data files. These refer to Development Regions demarcated by the South African Government according to their socio-economic conditions and development needs. These Development Regions are labeled A to J (there is no Region I, presumably because Statistics SA felt an "I" could be confused with the number 1). The 9 data files in the 1985 Census dataset refer to the following areas:

DEV REGION AREA COVERED A Western Cape Province including Walvis Bay B Northern Cape C Orange Free State and Qwaqwa D Eastern Cape/Border E Natal and Kwazulu F Eastern Transvaal, KaNgwane and part of the Simdlangentsha district of Kwazulu G Northern Transvaal, Lebowa and Gazankulu H PWV area, Moutse and KwaNdebele J Western Transvaal

Analysis unit

The units of analysis under observation in the South African census 1985 are households and individuals

Universe

The South African census 1985 census covered the provinces of the Cape, the Orange Free State, Transvaal, and Nata and the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Although the census was meant to cover all residents of the so called white areas of South Africa, in 88 areas door-to-door surveys were not possible and the population in these areas was enumerated by means of a sample survey conducted by the Human Sciences Research Council.

Mode of data collection

Face-to-face [f2f]

Research instrument

The1985 population census questionnaire was administered to each household and collected information on household and area type, and information on household members, including relationship within household, sex, age, marital status, population group, birthplace, country of citizenship, level of education, occupation, identity of employer and the nature of economic activities

Data appraisal

UNDER-ENUMERATION: The following under-enumeration figures have been calculated for the 1985 census. Estimated percentage distribution of undercount by race according to the HSRC: Percent undercount
Whites 7.6%
Blacks in the “RSA” 20.4% Blacks in the “National States” 15.1% Coloureds 1.0% Asians 4.6%

Longitudinal datasets of demographic, social, medical and economic information from a rural demographic in northern KwaZulu-Natal, South Africa where HIV prevalence is extremely high. The data may be filtered by demographics, years, or by individuals questionnaires. The datasets may be used by other researchers but the Africa Centre requests notification that anyone contact them when downloading their data. The datasets are provided in three formats: Stata11 .dta; tables in a MS-Access .accdb database; and worksheets in a MS-Excel .xlsx workbook. Datasets are generated approximately every six months containing information spanning the whole period of surveillance from 1/1/2000 to present.

South Africa administrative levels 0 (country), 1 (province), 2 (district), and 3 (local municipality) population statistics.

These CSV files are suitable for database or GIS linkage to the South Africa administrative levels 0-4 boundaries shapefiles.

Abstract

Every person, household and institution present in South Africa on Census Night, 9-10 October 1996, should have been enumerated in Census 1996. The purpose of the census was to provide a count of all persons present within the territory of the Republic of South Africa at that time. More specifically, the purpose of this census was to collect, process and disseminate detailed statistics on population size, composition and distribution at a small area level.

Geographic coverage

The South African Census 1996 has national coverage.

Analysis unit

Households and individuals

Universe

The South African census 1996 covered every person present in South Africa on Census Night, 9-10 October 1996 (except foreign diplomats and their families).

Kind of data

Census enumeration data

Sampling procedure

The data in the South African Census 1996 data file is a 10% unit level sample drawn from Census 1996 as follows:

1) Households: • A 10% sample of all households (excluding special institutions and hostels)

2) Persons: • A 10% sample of all persons as enumerated in the 1996 Population Census in South Africa

The census household records were explicitly stratified according to province and district council. Within each district council the records were further implicitly stratified by local authority. Within each implicit stratum the household records were ordered according to the unique seven-digit census enumerator area number, of which the first three digits are the (old) magisterial district number.

Mode of data collection

Face-to-face [f2f]

Research instrument

Different methods of enumeration were used to accommodate different situations and a variety of questionnaires were used. The information collected with each questionnaire differed slightly. The questionnaires used were as follows:

Questionnaire 1: (Household and personal questionnaire) This questionnaire was used in private households and within hostels which provided family accommodation. It contained 50 questions for each person and 15 for each household. Every household living in a private dwelling should have been enumerated on a household questionnaire. This questionnaire obtained information about the household and about each person who was present in the household on census night.

Questionnaire 2: (Summary book for hostels) This questionnaire was used to list all persons/households in the hostel and included 9 questions about the hostel. A summary book for hostels should have been completed for each hostel (that is, a compound for workers provided by mines, other employers, municipalities or local authorities). This questionnaire obtained information about the hostel and also listed all household and/or persons enumerated in the hostel. Some hostels contain people living in family groups. Where people were living as a household in a hostel, they were enumerated as such on a household questionnaire (which obtained information about the household and about each person who was present in the household on Census Night). On the final census file, they will be listed as for any other household and not as part of a hostel. Generally, hostels accommodate mostly individual workers. In these situations, persons were enumerated on separate personal questionnaires. These questionnaires obtained the same information on each person as would have been obtained on the household questionnaire. The persons will appear on the census file as part of a hostel. Some hostels were enumerated as special institutions and not on the questionnaires designed specifically for hostels.

Questionnaire 3: (Enumerator's book for special enumeration) This questionnaire was used to obtain very basic information for individuals within institutions such as hotels, prisons, hospitals etc. as well as for homeless persons. Only 6 questions were asked of these people. The questionnaire also included 9 questions about the institution. An enumerator's book for special enumeration should have been completed for each institution such as prisons and hospitals. This questionnaire obtained information on the institution and listed all persons present. Each person was asked a brief sub-set of questions - just 7 compared to around 50 on the household and personal questionnaires. People in institutions could not be enumerated as households. Homeless persons were enumerated during a sweep on census night using a special questionnaire. The results were later transcribed to standard enumerator's books for special enumeration to facilitate coding and data entry.

Response rate

The final calculation of the undercount of persons, based on analysis of a post-enumeration survey (PES) conducted shortly after the original census, was performed by Statistics South Africa. The estimated reponse rates are detailed below, both according to stratum and for the country as a whole. An estimated 10,7% of the people in South Africa, through the course of the census process, were not enumerated. For more information on the undercount and PES, see the publication, "Calculating the Undercount in Census '96", Statistics South Africa Report No. 03-01-18 (1996) which is included in the external documents section.

Undercount of persons by province (stratum, in %):

Western Cape 8,69
Eastern Cape 10,57
Northern Cape 15,59
Free State 8,75
KwaZulu-Natal 12,81
North West 9,37
Gauteng 9,99
Mpumalanga 10,09
Northern Province 11,28

South Africa 10,69

Abstract

The 2003-2004 South African Demographic and Health Survey is the second national health survey to be conducted by the Department of Health, following the first in 1998. Compared with the first survey, the new survey has more extensive questions around sexual behaviour and for the first time included such questions to a sample of men. Anthropometric measurements were taken on children under five years, and the adult health module has been enhanced with questions relating to physical activity and micro-nutrient intake, important risk factors associated with chronic diseases. The 2003-2004 SADHS has introduced a chapter reporting on the health, health service utilisation and living conditions of South Africa's older population (60 years or older) and how they have changed since 1998. This has been introduced because this component of the population is growing at a much higher rate than the other age groups. The chapter on adolescent health in 1998 focussed on health risk-taking behaviours of people aged 15-19 years. The chapter has been extended in the 2003-2004 SADHS to include indicators of sexual behaviour of youth aged 15-24 years.

A total of 10 214 households were targeted for inclusion in the survey and 7 756 were interviewed, reflecting an 85 percent response rate. The survey used a household schedule to capture basic information about all the members of the household, comprehensive questionnaires to all women aged 15-49, as well as anthropometric measurements of all children five years and younger. In every second household, interviews of all men 15-59 were conducted and in the alternate households, interviews and measurements of all adults 15 years and older were done including heights, weights, waist circumference, blood pressure and peak pulmonary flow. The overall response rate was 75 percent for women, 67 percent for men, 71 percent for adults, and 84 percent for children. This is slightly lower than the overall response rate for the 1998 SADHS, but varied substantially between provinces with a particularly low response rate in the Western Cape.

OBJECTIVES

In 1995 the National Health Information System of South Africa (NHIS/SA) committee identified the need for improved health information for planning services and monitoring programmes. The first South African Demographic and Health Survey (SADHS) was planned and implemented in 1998. At the time of the survey it was agreed that the survey had to be conducted every five years to enable the Department of Health to monitor trends in health services.

STUDY LIMITATIONS AND RECOMMENDATIONS

Comparison of the socio-demographic characteristics of the sample with the 2001 Population Census shows an over-representation of urban areas and the African population group, and an under-representation of whites and Indian females. It also highlights many anomalies in the ages of the sample respondents, indicating problems in the quality of the data of the 2003 survey. Careful analysis has therefore been required to distinguish the findings that can be considered more robust and can be used for decision making. This has involved considering the internal consistency in the data, and the extent to which the results are consistent with other studies.

Some of the key demographic and adult health indicators show signs of data quality problems. In particular, the prevalence of hypertension, and the related indicators of quality of care are clearly problematic and difficult to interpret. In addition, the fertility levels and the child mortality estimates are not consistent with other data sources. The data problems appear to arise from poor fieldwork, suggesting that there was inadequate training, supervision and quality control during the implementation of the survey. It is imperative that the next SADHS is implemented with stronger quality control mechanisms in place. Moreover, consideration should be given to the frequency of future surveys. It is possible that the SADHS has become overloaded - with a complex implementation required in the field. Thus it may be appropriate to consider a more frequent survey with a rotation of modules as has been suggested by the WHO.

Geographic coverage

The SADHS sample was designed to be a nationally representative sample.

Analysis unit

Households and individuals

Universe

The population covered by the 2003-2004 SADHS is defined as the universe of all women age 15-49, all men 15-59 in South Africa.

Kind of data

Sample survey data

Sampling procedure

The SADHS sample was designed to be a nationally representative probability sample of approximately 10000 households. The country was stratified into the nine provinces and each province was further stratified into urban and non-urban areas.

The sampling frame for the SADHS was provided by Statistics South Africa (Stats SA) based on the enumeration areas (EAs) list of approximately 86000 EAs created during the 2001 census. Since the Indian population constitutes a very small fraction of the South African population, the Census 2001 EAs were stratified into Indian and non-Indian. An EA was classified as Indian if the proportion of persons who classified themselves as Indian during Census 2001 enumeration in that EA was 80 percent or more, otherwise it was classified as Non-Indian. Within the Indian stratum, EAs were sorted descending by the proportion of persons classified as Indian. It should be noted that some provinces and non-urban areas have a very small proportion of the Indian population hence the Indian stratum could not be further stratified by province or urban/non-urban. A sample of 1000 households was allocated to the stratum. Probability proportional to size (PPS) systematic sampling was used to sample EAs and the proportion of Indian persons in an EA was the measure of size. The non-Indian stratum was stratified explicitly by province and within province by the four geo types, i.e. urban formal, urban informal, rural formal and tribal. Each province was allocated a sample of 1000 households and within province the sample was proportionally allocated to the secondary strata, i.e. geo type. For both the Indian and Non-Indian strata the sample take of households within an EA was sixteen households. The number of visited households in an EA as recorded in the Census 2001, 09 Books was used as the measure of size (MOS) in the Non-Indian stratum.

The second stage of selection involved the systematic sampling of households/stands from the selected EAs. Funds were insufficient to allow implementation of a household listing operation in selected EAs. Fortunately, most of the country is covered by aerial photographs, which Statistics SA has used to create EA-specific photos. Using these photos, ASRC identified the global positioning system (GPS) coordinates of all the stands located within the boundaries of the selected EAs and selected 16 in each EA, for a total of 10080 selected. The GPS coordinates provided a means of uniquely identifying the selected stand. As a result of the differing sample proportions, the SADHS sample is not self-weighting at the national level and weighting factors have been applied to the data in this report.

A total of 630 Primary Sampling Units (PSUs) were selected for the 2003-2004 SADHS (368 in urban areas and 262 in non-urban areas). This resulted in a total of 10214 households being selected throughout the country1. Every second household was selected for the adult health survey. In this second household, in addition to interviewing all women aged 15-49, all adults aged 15 and over were eligible to be interviewed with the adult health questionnaire. In every alternate household selected for the survey, not interviewed with the adult health questionnaire, all men aged 15-59 years were also eligible to be interviewed. It was expected that the sample would yield interviews with approximately 10000 households, 12500 women aged 15-49, 5000 adults and 5000 men.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for each DHS can be found as an appendix in the final report for each study.

The survey utilised five questionnaires: a Household Questionnaire, a Women's Questionnaire, a Men's Questionnaire, an Adult Health Questionnaire and an Additional Children Questionnaire. The contents of the first three questionnaires were based on the DHS Model Questionnaires. These model questionnaires were adapted for use in South Africa during a series of meetings with a Project Team that consisted of representatives from the National Department of Health, the Medical Research Council, the Human Sciences Research Council, Statistics South Africa, National Department of Social Development and ORCMacro. Draft questionnaires were circulated to other interested groups, e.g. such as academic institutions. The Additional Children and Men's Questionnaires were developed to address information needs identified by stakeholders, e.g. information on children who were not staying with their biological mothers. All questionnaires were developed in English and then translated in all 11 official languages in South Africa (English, Afrikaans, isiXhosa, isiZulu, Sesotho, Setswana, Sepedi, SiSwati, Tshivenda, Xitsonga and isiNdebele).

a) The Household Questionnaire was used to list all the usual members and visitors in the selected households. Basic information was collected on the characteristics of each person listed, including age, sex, education and relationship to the head of the household. Information was collected about social grants, work status and injuries experienced in the last month. An important purpose of the Household Questionnaire was to

Abstract

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.

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

About the Questionnaire : Much emphasis has been placed on the need for a population census to help government direct its development programmes, but less has been written about how the census questionnaire is compiled. The main focus of a population and housing census is to take stock and produce a total count of the population without omission or duplication. Another major focus is to be able to provide accurate demographic and socio-economic characteristics pertaining to each individual enumerated. Apart from individuals, the focus is on collecting accurate data on housing characteristics and services.A population and housing census provides data needed to facilitate informed decision-making as far as policy formulation and implementation are concerned, as well as to monitor and evaluate their programmes at the smallest area level possible. It is therefore important that Statistics South Africa collects statistical data that comply with the United Nations recommendations and other relevant stakeholder needs.

The United Nations underscores the following factors in determining the selection of topics to be investigated in population censuses: a) The needs of a broad range of data users in the country; b) Achievement of the maximum degree of international comparability, both within regions and on a worldwide basis; c) The probable willingness and ability of the public to give adequate information on the topics; and d) The total national resources available for conducting a census.

In addition, the UN stipulates that census-takers should avoid collecting information that is no longer required simply because it was traditionally collected in the past, but rather focus on key demographic, social and socio-economic variables.It becomes necessary, therefore, in consultation with a broad range of users of census data, to review periodically the topics traditionally investigated and to re-evaluate the need for the series to which they contribute, particularly in the light of new data needs and alternative data sources that may have become available for investigating topics formerly covered in the population census. It was against this background that Statistics South Africa conducted user consultations in 2008 after the release of some of the Community Survey products. However, some groundwork in relation to core questions recommended by all countries in Africa has been done. In line with users' meetings, the crucial demands of the Millennium Development Goals (MDGs) should also be met. It is also imperative that Stats SA meet the demands of the users that require small area data.

Accuracy of data depends on a well-designed questionnaire that is short and to the point. The interview to complete the questionnaire should not take longer than 18 minutes per household. Accuracy also depends on the diligence of the enumerator and honesty of the respondent.On the other hand, disadvantaged populations, owing to their small numbers, are best covered in the census and not in household sample surveys.Variables such as employment/unemployment, religion, income, and language are more accurately covered in household surveys than in censuses.Users'/stakeholders' input in terms of providing information in the planning phase of the census is crucial in making it a success. However, the information provided should be within the scope of the census.

1. The Household Questionnaire is divided into the following sections:
2. Household identification particulars

3. Individual particulars Section A: Demographics Section B: Migration Section C: General Health and Functioning Section D: Parental Survival and Income Section E: Education Section F: Employment Section G: Fertility (Women 12-50 Years Listed) Section H: Housing, Household Goods and Services and Agricultural Activities Section I: Mortality in the Last 12 Months The Household Questionnaire is available in Afrikaans; English; isiZulu; IsiNdebele; Sepedi; SeSotho; SiSwati;Tshivenda;Xitsonga

4. The Transient and Tourist Hotel Questionnaire (English) is divided into the following sections:

5. Name, Age, Gender, Date of Birth, Marital Status, Population Group, Country of birth, Citizenship, Province.

6. The Questionnaire for Institutions (English) is divided into the following sections:

7. Particulars of the institution

8. Availability of piped water for the institution

9. Main source of water for domestic use

10. Main type of toilet facility

11. Type of energy/fuel used for cooking, heating and lighting at the institution

12. Disposal of refuse or rubbish

13. Asset ownership (TV, Radio, Landline telephone, Refrigerator, Internet facilities)

14. List of persons in the institution on census night (name, date of birth, sex, population group, marital status, barcode number)

15. The Post Enumeration Survey Questionnaire (English)

These questionnaires are provided as external resources.

Cleaning operations

Data editing and validation system The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).

The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main Census.

Editing team The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors. Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.

The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation/editing were used. Logical imputations were preferred, and in many cases substantial effort was undertaken to deduce a consistent value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following: 0 no imputation was performed; raw data were preserved 1 Logical editing was performed, raw data were blank 2 logical editing was performed, raw data were not blank 3 hot-deck imputation was performed, raw data were blank 4 hot-deck imputation was performed, raw data were not blank

Data appraisal

Independent monitoring and evaluation of Census field activities Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring

The African Cities Population Database (ACPD) has been produced by the Birkbeck College of the University of London in 1990 at the request of the United Nations Environment Programme (UNEP) in Nairobi, Kenya. The database contains head counts for 479 cities in Africa which either have a population of over 20,000 or are capitals of their nation state. Listed are the geographical location of the cities and their population sizes. The material is primarily derived from a 1988 report of the Economic Commission for Africa (ECA) and several issues of the United Nations Demographic Yearbook (1973-81). Severe problems were found with several countries such as Togo, Ghana and South Africa. For South Africa, the data were derived from the United Nations Demographic Yearbook 1987.

WCPD is an Arc/Info point coverage. It has no projection, as the cities are located on the basis of their latitude and longitude. Coordinates were assigned on the basis of gazetteers or African maps. Each record in the data base contains details of the city name, country name, latitude and longitude of the city, and its population at a defined time. The Arc/Info attribute table contains the following fields:

AREA Arc/Info item PERIMETER Arc/Info item ACPD# Arc/Info item ACPD-ID Arc/Info item ID-NUM Unique number for each city CITY City name COUNTRY Country name CITY-POP Population of city proper YEAR Latest available year of collection

ACPD comes as an Arc/Info EXPORT file originally called "ACPD.E00" and contains 67 Kb of data. The file has a record length of 80 and a block size of 8000 (blocking factor = 100). The file can be read from tape using Arc/Info's TAPEREAD command or any other generic copy utility. If distributed on a diskette it can be read using the ordinary DOS 'COPY' command. The file has to be converted to Arc/Info internal format using its IMPORT command.

References to the WCPD data set can be found in:

• SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK.
• D. Rhind. "Cartographically-related research at Birkbeck College 1987-91" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66.

The source of the WCPD data set as held by GRID is Birkbeck College, University of London, Department of Geography, London, UK.