The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

Our Population Density Grid Dataset for Southern Africa offers detailed, grid-based insights into the distribution of population across cities, towns, and rural areas. Free to explore and visualize, this dataset provides an invaluable resource for businesses and researchers looking to understand demographic patterns and optimize their location-based strategies.

By creating an account, you gain access to advanced tools for leveraging this data in geomarketing applications. Perfect for OOH advertising, retail planning, and more, our platform allows you to integrate population insights with your business intelligence, enabling you to make data-driven decisions for your marketing and expansion strategies.

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

Select areas with a particular population density. Data and Resources Population densityGeoJSON A 100km2 grid with population density estimates from Worldpop. Explore More information Go to resource

The dataset is a zip file that contains 28 cloud optimized tiff files that cover the continent of Africa. Each of the 28 files represents a region or area - these are not divided by country. These 28 tiff files represent 2015 population estimates. However, please note that many of the country-level files include 2020 population estimates including: Angola, Benin, Botswana, Burundi, Cameroon, Cabo Verde, Cote d'Ivoire, Djibouti, Eritrea, Eswatini, The Gambia, Ghana, Lesotho, Liberia, Mozambique, Namibia, Sao Tome & Principe, Sierra Leone, South Africa, Togo, Zambia, and Zimbabwe. To create the high-resolution maps, machine learning techniques are used to identify buildings from commercially available satellite images then general population estimates are overlaid based on publicly available census data and other population statistics. The resulting maps are the most detailed and actionable tools available for aid and research organizations.

Our Population Density Grid Dataset for Eastern Africa offers detailed, grid-based insights into the distribution of population across cities, towns, and rural areas. Free to explore and visualize, this dataset provides an invaluable resource for businesses and researchers looking to understand demographic patterns and optimize their location-based strategies.

By creating an account, you gain access to advanced tools for leveraging this data in geomarketing applications. Perfect for OOH advertising, retail planning, and more, our platform allows you to integrate population insights with your business intelligence, enabling you to make data-driven decisions for your marketing and expansion strategies.

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.

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).

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Zimbabwe: (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 African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change

 This documentation describes the third version of a database of
 administrative units with associated population figures for
 Africa. The first version was compiled for UNEP's Global
 Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while
 the second version represented an update and expansion of this first
 product (Deichmann 1994, WRI 1995). The work discussed in the
 following paragraphs is also related to NCGIA activities to produce a
 global database of subnational population estimates (Tobler et
 al. 1995), and an improved database for the Asian continent (Deichmann
 1996a). The new version for Africa provides considerably more detail:
 more than 4700 administrative units, compared to about 800 in the
 first and 2200 in the second version. In addition, for each of these
 units a population estimate was compiled for 1960, 70, 80 and 90 which
 provides an indication of past population dynamics in Africa.

South Africa ZA: Population Density: People per Square Km data was reported at 46.754 Person/sq km in 2017. This records an increase from the previous number of 46.176 Person/sq km for 2016. South Africa ZA: Population Density: People per Square Km data is updated yearly, averaging 30.287 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 46.754 Person/sq km in 2017 and a record low of 14.773 Person/sq km in 1961. South Africa ZA: Population Density: People per Square Km 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.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

This map features the World Population Density Estimate 2016 layer for the Caribbean region. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: https://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

The Latin America population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

 This documentation describes the Latin American Population Database, a
 collaborative effort between the International Center for Tropical
 Agriculture (CIAT), the United Nations Environment Program (UNEP-GRID,
 Sioux Falls) and the World Resources Institute (WRI). This work is
 intended to provide a population database that compliments previous
 work carried out for Asia and Africa. This data set is more detailed
 than the Africa and Asia data sets. Population estimates for 1960,
 1970, 1980, 1990 and 2000 are also provided. The work discussed in the
 following paragraphs is also related to NCGIA activities to produce a
 global database of subnational population estimates (Tobler et
 al. 1995), and an improved database for the Asian continent (Deichmann
 1996a).

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.

From the AfriPop website..."High resolution, contemporary data on human population distributions are a prerequisite for the accurate measurement of the impacts of population growth, for monitoring changes and for planning interventions. The AfriPop project was initiated in July 2009 with an aim of producing detailed and freely-available population distribution maps for the whole of Africa. Based on the approaches outlined in detail here and here, and summarized on the methods page, fine resolution satellite imagery-derived settlement maps are combined with land cover maps to reallocate contemporary census-based spatial population count data. Assessments have shown that the resultant maps are more accurate than existing population map products, as well as the simple gridding of census data. Moreover, the 100m spatial resolution represents a finer mapping detail than has ever before been produced at national extents. The approaches used in AfriPop dataset production are designed with operational application in mind, using simple and semi-automated methods to produce easily updatable maps. Given the speed with which population growth and urbanisation are occurring across much of Africa, and the impacts these are having on the economies, environments and health of nations, such features are a necessity for both research and operational applications."Data Source: AfriPop.org

V1.5 The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Kenya: (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).

Abstract

The health and demography of the South African population has been undergoing substantial changes as a result of the rapidly progressing HIV epidemic. Researchers at the University of KwaZulu-Natal and the South African Medical Research Council established The Africa Health Research Studies in 1997 funded by a core grant from The Wellcome Trust, UK. Given the urgent need for high quality longitudinal data with which to monitor these changes, and with which to evaluate interventions to mitigate impact, a demographic surveillance system (DSS) was established in a rural South African population facing a rapid and severe HIV epidemic. The DSS, referred to as the Africa Health Research Institute Demographic Information System (ACDIS), started in 2000.

ACDIS was established to ‘describe the demographic, social and health impact of the HIV epidemic in a population going through the health transition’ and to monitor the impact of intervention strategies on the epidemic. South Africa’s political and economic history has resulted in highly mobile urban and rural populations, coupled with complex, fluid households. In order to successfully monitor the epidemic, it was necessary to collect longitudinal demographic data (e.g. mortality, fertility, migration) on the population and to mirror this complex social reality within the design of the demographic information system. To this end, three primary subjects are observed longitudinally in ACDIS: physical structures (e.g. homesteads, clinics and schools), households and individuals. The information about these subjects, and all related information, is stored in a single MSSQL Server database, in a truly longitudinal way—i.e. not as a series of cross-sections.

The surveillance area is located near the market town of Mtubatuba in the Umkanyakude district of KwaZulu-Natal. The area is 438 square kilometers in size and includes a population of approximately 85 000 people who are members of approximately 11 000 households. The population is almost exclusively Zulu-speaking. The area is typical of many rural areas of South Africa in that while predominantly rural, it contains an urban township and informal peri-urban settlements. The area is characterized by large variations in population densities (20–3000 people/km2). In the rural areas, homesteads are scattered rather than grouped. Most households are multi-generational and range with an average size of 7.9 (SD:4.7) members. Despite being a predominantly rural area, the principle source of income for most households is waged employment and state pensions rather than agriculture. In 2006, approximately 77% of households in the surveillance area had access to piped water and toilet facilities.

To fulfil the eligibility criteria for the ACDIS cohort, individuals must be a member of a household within the surveillance area but not necessarily resident within it. Crucially, this means that ACDIS collects information on resident and non-resident members of households and makes a distinction between membership (self-defined on the basis of links to other household members) and residency (residing at a physical structure within the surveillance area at a particular point in time). Individuals can be members of more than one household at any point in time (e.g. polygamously married men whose wives maintain separate households). As of June 2006, there were 85 855 people under surveillance of whom 33% were not resident within the surveillance area. Obtaining information on non-resident members is vital for a number of reasons. Most importantly, understanding patterns of HIV transmission within rural areas requires knowledge about patterns of circulation and about sexual contacts between residents and their non-resident partners. To be consistent with similar datasets from other INDEPTH Member centres, this data set contains data from resident members only.

During data collection, households are visited by fieldworkers and information supplied by a single key informant. All births, deaths and migrations of household members are recorded. If household members have moved internally within the surveillance area, such moves are reconciled and the internal migrant retains the original identfier associated with him/her.

Geographic coverage

Demographic surveillance area situated in the south-east portion of the uMkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the South by the Umfolozi river, on the East by the N2 highway (except form portions where the Kwamsane township strandles the highway) and in the North by the Inyalazi river for portions of the boundary. The area is 438 square kilometers.

Analysis unit

Individual

Universe

Resident household members of households resident within the demographic surveillance area. Inmigrants are defined by intention to become resident, but actual residence episodes of less than 180 days are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than 180 days are censored. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever resident during the study period (1 Jan 2000 to 31 Dec 2015).

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 1 to 37 of demographic surveillance data covering the period from 1 Jan 2000 to 31 December 2015. Two rounds of data collection took place annually except in 2002 when three surveillance rounds were conducted. From 1 Jan 2015 onwards there are three surveillance rounds per annum.

Sampling procedure

This dataset is not based on a sample but contains information from the complete demographic surveillance area.

Reponse units (households) by year: Year Households 2000 11856
2001 12321
2002 12981
2003 12165
2004 11841
2005 11312
2006 12065
2007 12165
2008 11790
2009 12145
2010 12485
2011 12455
2012 12087 2013 11988 2014 11778 2015 11938

In 2006 the number of response units increased due to the addition of a new village into the demographic surveillance area.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Bounded structure registration (BSR) or update (BSU) form: - Used to register characteristics of the BS - Updates characteristics of the BS - Information as at previous round is preprinted

Household registration (HHR) or update (HHU) form: - Used to register characteristics of the HH - Used to update information about the composition of the household - Information preprinted of composition and all registered households as at previous

Household Membership Registration (HMR) or update (HMU): - Used to link individuals to households - Used to update information about the household memberships and member status observations - Information preprinted of member status observations as at previous

Individual registration form (IDR): - Used to uniquely identify each individual - Mainly to ensure members with multiple household memberships are appropriately captured

Migration notification form (MGN): - Used to record change in the BS of residency of individuals or households _ Migrants are tracked and updated in the database

Pregnancy history form (PGH) & pregnancy outcome notification form (PON): - Records details of pregnancies and their outcomes - Only if woman is a new member - Only if woman has never completed WHL or WGH

Death notification form (DTN): - Records all deaths that have recently occurred - Iincludes information about time, place, circumstances and possible cause of death

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an

Abstract

The ‘South African Population Research Infrastructure Network’ (SAPRIN) is a national research infrastructure funded through the Department of Science and Innovation and hosted by the South African Medical Research Council. One of SAPRIN’s initial goals has been to harmonise and share the longitudinal data from the three current Health and Demographic Surveillance System (HDSS) Nodes. These long-standing nodes are the MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, established in 1993, with a population of 113 113 people; the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, established in 1996, with a current population of 38 479; and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, established in 2000, with a current population of 139 250.

This dataset represents a snapshot of the continually evolving data in the underlying longitudinal databases maintained by the SAPRIN nodes. In these databases the rightmost extend of the individual's surveillance episode is indicated by the data collection date of the last time the individual's membership of a household under surveillance has been confirmed. Each dataset has a right censor date (31 December 2017 for the current version of the dataset) and individual surveillance episodes are terminated at that point if the individual is still under surveillance beyond the cut-off date.

Each individual surveillance episode is associated with a physical location, for internal residency episodes it is the actual place of residence of the individual, for external residence episodes (periods of temporary migration) it is the place of residence of the individual's household. If an individual change their place of residency from one location within the surveillance area to another location still within the surveillance area, the episode at the original location is terminated with a location exit event, and a new episode starts with a location entry event at the destination location. It is also possible for the household the individual is a member of, to change their place of residency in the surveillance area, whilst the individual is externally resident (is a temporary migrant), in which case the individual's external resident episode will also be split with a location exit-entry pair of events.

At every household visit written consent is obtained from the household respondent for continued participation in the surveillance and such consent can be withdrawn. When this happens all household members' surveillance episodes are terminated with a refusal event. It is possible for households to again provide consent to participate in the surveillance after some time, in such cases surveillance events are restarted with a permission event.

As mentioned previously, surveillance episodes are continually extended by the last data collection event if the individual remains under surveillance. In certain cases, individuals may be lost to follow-up and surveillance episodes where the date of last data collection is more than one year prior to the right censor data are terminated as lost to follow up at that last data collection date. Individuals with data collection dates within a year of the right censor date is considered still to be under surveillance up to this last data collection date.

Each surveillance episode contains the identifier of the household the individual is a member of during that episode. Under relatively rare circumstances it is possible for an individual to change household membership whilst still resident at the same location, or to change membership whilst externally resident, in these cases the surveillance episode will be split with a pair of membership end and membership start events. More commonly membership start and end events coincide with location exit and entry events or in- and out-migration events. Memberships also obviously start at birth or enumeration and end at death, refusal to participate or lost to follow-up.

In about half of the cases, individuals have a single episode from first enumeration, birth or in-migration, to their eventual death, out-migration or currently still under surveillance. In the remaining cases, individuals transition from internal residency to external residency via out-migration, or from one location to another via internal migration with a location exit and entry event, or some other rarer form of transition involving membership change, refusal or lost to follow-up. Usually these series of surveillance episodes are continuous in time, with no gaps between episodes, but gaps can form, e.g. when an individual out-migrates and end membership with the household and so is no longer under surveillance, only to return via in-migration at some future date and take up membership with same or different household.

The SAPRIN Individual Surveillance Episodes 2020 Datasets consists of three types of the Demographic surveillance datasets: 1.SAPRIN Individual Surveillance Episodes 2020: Basic Dataset. This dataset contains only the internal and external residency episodes for an individual. 2.SAPRIN Individual Surveillance Episodes 2020: Age-Year-Delivery Dataset. This dataset splits the basic surveillance episodes at calendar year end and at the date when the age in years (birth-day) of an individual changes. In the case of women who have given births, episodes are split at the time of delivery as well. 3.SAPRIN Individual Surveillance Episodes 2020: Detailed Dataset. This dataset adds to the dataset 2 time-varying attributes such as education, employment, marital status and socio-economic status.

Geographic coverage

The South African Population Research Infrastructure Network (SAPRIN) currently represents a network of three Health and Demographic Surveillance System (HDSS) nodes located in rural South Africa, namely: 1) MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, which has collected data since 1993. The nodal website is: http://www.agincourt.co.za; 2) the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, which has collected data since 1996.The nodal website is: N/A; 3) and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, which has collected data since 2000.The nodal website is: http://www.ahri.org.

The Agincourt HDSS covers a surveillance area of approximately 420 square kilometres and is located in the Bushbuckridge District, Mpumalanga in the rural northeast of South Africa close to the Mozambique border. At baseline in 1992, 57 600 people were recorded in 8900 households in 20 villages; by 2006, the population had increased to about 70 000 people in 11 700 households. As of December 2017, there were 113 113 people under surveillance of whom 28% were not resident within the surveillance area, with a total of about 2m person years of observation. 33% of the population is under 15 years old. The population is almost exclusively Shangaan-speaking.The Agincourt HDSS has population density of over 200 persons per square kilometre. The Agincourt HDSS extends between latitudes 24° 50´ and 24° 56´S and longitudes 31°08´ and 31°´ 25´ E. The altitude is about 400-600m above sea level.

DIMAMO is located in the Capricorn district, Limpopo Province approximately 40 kilometres from Polokwane, the capital city of Limpopo Province and 15-50 kilometres from the University of Limpopo. The site covers an area of approximately 400 square kilometres . The initial total population observed was about 8 000 but the field site was expanded in 2010. As of December 2017, there were 38 479 people under surveillance, of whom 22% were not resident within the surveillance area, with about 400,000 person years of observation. 30% of the population is under 15 years old. The population is predominantly Sotho speaking. Most households have electricity. Some households have piped water either inside the house or in their yards, but most fetch water from taps situated at strategic points in the villages. Most households have a pit latrine in their yards. The area lies between latitudes and 23°65´ and 23°90´S and longitudes 29°65´ and 29°85´E. The HDSS is located on a high plateau area (approximately 1250 m above sea level) where communities typically consist of households clustered in villages, with access to local land for small-scale food production.

Africa Health Research Institute (AHRI) is situated in the south-east portion of the Umkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the south by the Umfolozi river, on the east by the N2 highway (except form portions where the Kwamsane township stradles the highway) and in the north by the Inyalazi river for portions of the boundary. The surveillance area is approximately 850 square kilometres. As of December 2017, there were 139 250 people under surveillance of whom 28% were not resident within the surveillance area, with about 1.7m person years of observation. 32% of the population is under 15 years old. The population is almost exclusively Zulu-speaking. The surveillance area is typical of many rural areas of South Africa in that while predominantly rural, it contains an urban township and informal peri-urban settlements. The area is characterized by large variations in population densities (20-3000 people per square kilometre). The area lies between latitudes -28°24' and 28°20'N and longitudes 32°10' and 31°58'E.

Analysis unit

Households and individuals

Universe

Households resident in dwellings within the study area will be eligible for inclusion in the household component of SAPRIN. All individuals identified by the household proxy informant as a member of

From the AfriPop website..."High resolution, contemporary data on human population distributions are a prerequisite for the accurate measurement of the impacts of population growth, for monitoring changes and for planning interventions. The AfriPop project was initiated in July 2009 with an aim of producing detailed and freely-available population distribution maps for the whole of Africa. Based on the approaches outlined in detail here and here, and summarized on the methods page, fine resolution satellite imagery-derived settlement maps are combined with land cover maps to reallocate contemporary census-based spatial population count data. Assessments have shown that the resultant maps are more accurate than existing population map products, as well as the simple gridding of census data. Moreover, the 100m spatial resolution represents a finer mapping detail than has ever before been produced at national extents. The approaches used in AfriPop dataset production are designed with operational application in mind, using simple and semi-automated methods to produce easily updatable maps. Given the speed with which population growth and urbanisation are occurring across much of Africa, and the impacts these are having on the economies, environments and health of nations, such features are a necessity for both research and operational applications."Data Source: AfriPop.org