The number of Reddit users in Africa was forecast to continuously increase between 2024 and 2028 by in total 4.7 million users (+66.67 percent). After the eighth consecutive increasing year, the Reddit user base is estimated to reach 11.78 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Reddit users in countries like North America and Asia.

The number of snapchat users in Africa was forecast to continuously increase between 2024 and 2028 by in total 40.3 million users (+52.07 percent). After the ninth consecutive increasing year, the snapchat user base is estimated to reach 117.68 million users and therefore a new peak in 2028. Notably, the number of snapchat users of was continuously increasing over the past years.The user numbers, depicted here regarding the platform Snapchat, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of snapchat users in countries like Asia and Australia & Oceania.

The number of Pinterest users in Africa was forecast to continuously increase between 2024 and 2028 by in total 15 million users (+68.81 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 36.81 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Pinterest users in countries like Europe and Australia & Oceania.

South Africa Population: Mid Year: African data was reported at 46,682,896.000 Person in 2018. This records an increase from the previous number of 45,656,400.796 Person for 2017. South Africa Population: Mid Year: African data is updated yearly, averaging 40,044,937.103 Person from Jun 2001 (Median) to 2018, with 18 observations. The data reached an all-time high of 46,682,896.000 Person in 2018 and a record low of 34,932,157.000 Person in 2001. South Africa Population: Mid Year: African data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.G003: Population: Mid Year: by Group, Age and Sex.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market!

Veraset Movement (GPS Mobility Data) offers unparalleled insights into footfall traffic patterns across nearly four dozen countries in Africa.

Covering 46+ countries, Veraset's Mobility Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement.

Ideal for ad tech, planning, retail, and transportation logistics, Veraset's Movement data (Mobility data) helps shape strategy and make impactful data-driven decisions.

Veraset’s Africa Movement Panel includes the following countries: - algeria-DZ - angola-AO - benin-BJ - botswana-BW - burkina faso-BF - burundi-BI - cameroon-CM - central african republic-CF - chad-TD - comoros-KM - congo-brazzaville-CG - congo-kinshasa-CD - djibouti-DJ - egypt-EG - eritrea-ER - ethiopia-ET - gabon-GA - gambia-GM - ghana-GH - guinea-bissau-GW - kenya-KE - lesotho-LS - liberia-LR - libya-LY - madagascar-MG - malawi-MW - mali-ML - mauritius-MU - morocco-MA - mozambique-MZ - namibia-NA - nigeria-NG - rwanda-RW - senegal-SN - seychelles-SC - sierra leone-SL - somalia-SO - south africa-ZA - south sudan-SS - tanzania-TZ - togo-TG - tunisia-TN - uganda-UG - zambia-ZM - zimbabwe-ZW

Companies use Veraset's Mobility Data for: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Central African Republic CF: Number of Deaths Ages 5-14 Years data was reported at 2,048.000 Person in 2018. This records a decrease from the previous number of 2,192.000 Person for 2015. Central African Republic CF: Number of Deaths Ages 5-14 Years data is updated yearly, averaging 2,371.000 Person from Dec 1990 (Median) to 2018, with 5 observations. The data reached an all-time high of 2,452.000 Person in 1990 and a record low of 2,048.000 Person in 2018. Central African Republic CF: Number of Deaths Ages 5-14 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Social: Health Statistics. Number of deaths of children ages 5-14 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

South Africa ZA: Population: Male: Ages 15-19: % of Male Population data was reported at 9.294 % in 2017. This records a decrease from the previous number of 9.405 % for 2016. South Africa ZA: Population: Male: Ages 15-19: % of Male Population data is updated yearly, averaging 10.409 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 11.077 % in 1999 and a record low of 9.244 % in 1964. South Africa ZA: Population: Male: Ages 15-19: % of Male Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Population and Urbanization Statistics. Male population between the ages 15 to 19 as a percentage of the total male population.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; ;

Success.ai's B2B and B2C Contact Data for African Professionals and Companies provides an extensive database of verified emails and phone numbers, specifically designed to enhance your marketing efforts and business outreach in Africa. This dataset encompasses a wide range of industries, offering direct access to key decision-makers and influencers within the African market.

Why Choose Success.ai’s B2C Contact Data?

Extensive Coverage: Tap into a rich resource of B2C contact information for professionals across Africa, including emerging and established markets. High-Quality Data: Each entry is rigorously validated for accuracy and completeness, using AI-driven processes to ensure the reliability of emails and phone numbers. Targeted Marketing: Ideal for businesses looking to engage with African professionals for B2C campaigns, event promotions, and market entry strategies. Data Points Included:

Verified Emails: Reach professionals directly with validated email addresses that enhance your digital marketing efforts. Phone Numbers: Immediate access to mobile and direct line numbers for telemarketing and SMS campaigns. Professional Details: Insights into job titles, industries, and company affiliations, enabling targeted and personalized outreach. Customizable and Flexible Data Solutions:

Tailored Filters: Segment the data based on specific criteria such as geographic location, industry, or professional level to align perfectly with your campaign needs. Multiple Format Options: Available in CSV, Excel, or via API for seamless integration into your existing CRM or marketing automation platforms. Best Price Guarantee:

Our commitment to providing the most competitive prices ensures that you get maximum value from your investment without compromising on the quality or depth of data. Applications and Use Cases:

Direct Marketing: Utilize accurate contact details for email campaigns, direct mail, and cold calling. Market Research: Conduct surveys and studies to understand market needs and consumer behavior within various African regions. Event Promotion: Drive attendance to webinars, workshops, and conferences by reaching out directly to interested professionals. Customer Profiling: Enhance your understanding of your customer base by integrating detailed B2C data into your analytics tools. Compliance and Ethics:

Data Privacy Compliance: Adheres to local and international data protection regulations, ensuring that your use of contact data remains compliant. Ethical Data Collection: Our data collection methods are transparent and ethical, respecting the privacy and rights of professionals. Support and Consultation:

Dedicated Support Team: Our experts are available to help you navigate the complexities of African markets and optimize the use of our data. Custom Consultation: Work with our team to refine your data strategy and ensure you are targeting the right audience with the right message. Start Expanding Your Reach in Africa: With Success.ai’s B2C Contact Data for African Professionals, you are equipped to execute powerful marketing strategies and build meaningful connections that drive growth and engagement. Contact us today to discover how our data can transform your business outreach in Africa.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Ouaka, Mbomou, Bangui, Nana-Mambéré, Ouham, Sangha-Mbaéré, Ombella M'Poko, Mambéré-Kadéï, Vakaga, Ouham Pendé, Lobaye, Haute-Kotto, Kémo, Nana-Gribizi, Bamingui-Bangoran, Haut-Mbomou, Market Average

Abstract

This cumulative dataset contains statistics on mortality and causes of death in South Africa covering the period 1997-2017. The mortality and causes of death dataset is part of a regular series published by Stats SA, based on data collected through the civil registration system. This dataset is the most recent cumulative round in the series which began with the separately available dataset Recorded Deaths 1996.

The main objective of this dataset is to outline emerging trends and differentials in mortality by selected socio-demographic and geographic characteristics for deaths that occurred in the registered year and over time. Reliable mortality statistics, are the cornerstone of national health information systems, and are necessary for population health assessment, health policy and service planning; and programme evaluation. They are essential for studying the occurrence and distribution of health-related events, their determinants and management of related health problems. These data are particularly critical for monitoring the Sustainable Development Goals (SDGs) and Agenda 2063 which share the same goal for a high standard of living and quality of life, sound health and well-being for all and at all ages. Mortality statistics are also required for assessing the impact of non-communicable diseases (NCD's), emerging infectious diseases, injuries and natural disasters.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

This dataset is based on information on mortality and causes of death from the South African civil registration system. It covers all death notification forms from the Department of Home Affairs for deaths that occurred in 1997-2017, that reached Stats SA during the 2018/2019 processing phase.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

The registration of deaths is captured using two instruments: form BI-1663 and form DHA-1663 (Notification/Register of death/stillbirth).

Data appraisal

This cumulative dataset is part of a regular series published by Stats SA and includes all previous rounds in the series (excluding Recorded Deaths 1996). Stats SA only includes one variable to classify the occupation group of the deceased (OccupationGrp) in the current round (1997-2017). Prior to 2016, Stats SA included both occupation group (OccupationGrp) and industry classification (Industry) in all previous rounds. Therefore, DataFirst has made the 1997-2015 cumulative round available as a separately downloadable dataset which includes both occupation group and industry classification of the deceased spanning the years 1997-2015.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

As of February 2025, Morocco had an internet penetration of over 92 percent, making it the country with the highest internet penetration in Africa. Libya ranked second, with 88.5 percent, followed by Seychelles with over 87 percent. On the other hand, The Central African Republic, Chad, and Burundi had the lowest prevalence of internet among their population. Varying but growing levels of internet adoption Although internet usage varies significantly across African countries, the overall number of internet users on the continent jumped to around 646 million from close to 181 million in 2014. Of those, almost a third lived in Nigeria and Egypt only, two of the three most populous countries on the continent. Furthermore, internet users are expected to surge, reaching over 1.1 billion users by 2029. Mobile devices dominate web traffic Most internet adoptions on the continent occurred recently. This is among the reasons mobile phones increasingly play a significant role in connecting African populations. As of early January 2024, around 74 percent of the web traffic in Africa was via mobile phones, over 14 percentage points higher than the world average. Furthermore, almost all African countries have a higher web usage on mobile devices compared to other devices, with rates as high as 92 percent in Sudan. This is partly due to mobile connections being cheaper and not requiring the infrastructure needed for traditional desktop PCs with fixed-line internet connections.

South Africa Population: Mid Year: African: Male: 30 to 34 Years data was reported at 2,281,671.000 Person in 2018. This records an increase from the previous number of 2,208,498.111 Person for 2017. South Africa Population: Mid Year: African: Male: 30 to 34 Years data is updated yearly, averaging 1,583,319.067 Person from Jun 2001 (Median) to 2018, with 18 observations. The data reached an all-time high of 2,281,671.000 Person in 2018 and a record low of 1,114,709.000 Person in 2001. South Africa Population: Mid Year: African: Male: 30 to 34 Years data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.G003: Population: Mid Year: by Group, Age and Sex.

This dataset provides values for TOURIST ARRIVALS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Source data assessment of statistical capacity (scale 0 - 100) in Central African Republic was reported at 10 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Central African Republic - Source data assessment of statistical capacity (scale 0 - 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Techsalerator’s Business Technographic Data for Africa is an invaluable resource designed to provide businesses, market analysts, and technology vendors with a comprehensive understanding of the technological landscape across Africa. This dataset offers an in-depth examination of the technology ecosystems within companies operating in the region, offering a granular view into their technology stacks, digital tools, and IT infrastructure.

Key Features of the Dataset: Technology Stacks:

Detailed information on the technology stacks used by companies, including software, hardware, and platforms. This encompasses data on programming languages, frameworks, databases, cloud services, and enterprise applications that companies rely on. Digital Tools:

Insight into the digital tools and services adopted by businesses, such as customer relationship management (CRM) systems, enterprise resource planning (ERP) solutions, collaboration tools, and marketing automation platforms. IT Infrastructure:

Data on the IT infrastructure of companies, including their network setups, server environments, data storage solutions, and cybersecurity measures. This also covers information on on-premises versus cloud-based infrastructure. Technological Trends:

Analysis of emerging technological trends and innovations being adopted across different sectors and regions. This helps in identifying shifts in technology usage and areas of growth within the African market. Sector and Regional Breakdown:

Segmentation of data by industry sectors and geographic regions, providing insights into how technology adoption varies across different industries and African countries.

Africa Countries Covered: Northern Africa: Algeria Bahrain Egypt Libya Mauritania Morocco Sudan Tunisia Sub-Saharan Africa: West Africa: Benin Burkina Faso Cape Verde Ivory Coast (Côte d'Ivoire) Gambia Ghana Guinea Guinea-Bissau Liberia Mali Niger Nigeria Senegal Sierra Leone Togo Central Africa: Angola Cameroon Central African Republic Chad Congo, Republic of the Congo, Democratic Republic of the Equatorial Guinea Gabon São Tomé and Príncipe East Africa: Burundi Comoros Djibouti Eritrea Eswatini (Swaziland) Ethiopia Kenya Lesotho Malawi Mauritius Rwanda Seychelles Somalia Tanzania Uganda Southern Africa: Botswana Lesotho Namibia South Africa Swaziland (Eswatini) Zimbabwe Benefits of the Dataset: Strategic Insights: Businesses can leverage the data to make informed decisions about technology investments, partnerships, and competitive strategies based on a thorough understanding of the technology landscape. Market Analysis: Market analysts can use the data to identify trends, benchmark companies, and assess the technological capabilities of different sectors and regions. Vendor Strategy: Technology vendors can gain insights into the technology stacks and tools being used by potential clients, allowing them to tailor their offerings and sales strategies effectively. Techsalerator’s Business Technographic Data for Africa equips stakeholders with essential information to navigate the complex technological environment of Africa businesses, enabling more strategic and data-driven decisions.

This dataset contains gridded 1-minute resolution terrain elevation data for the area around South Africa.

Abstract

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

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

Geographic coverage

Two provinces: Gauteng and Limpopo

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

Analysis unit

• Household
• Individual

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

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

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

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

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

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

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

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

South Africa Population: 15 to 64 Years: African data was reported at 30,549.747 Person th in Sep 2018. This records an increase from the previous number of 30,399.484 Person th for Jun 2018. South Africa Population: 15 to 64 Years: African data is updated quarterly, averaging 27,383.231 Person th from Mar 2008 (Median) to Sep 2018, with 43 observations. The data reached an all-time high of 30,549.747 Person th in Sep 2018 and a record low of 24,422.531 Person th in Mar 2008. South Africa Population: 15 to 64 Years: African data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.G001: Population.

Water Observations from Space (WOfS) is a service that draws on satellite imagery to provide historical surface water observations of the whole African continent. WOfS allows users to understand the location and movement of inland and coastal water present in the African landscape. It shows where water is usually present; where it is seldom observed; and where inundation of the surface has been observed by satellite. WOfS annual summary shows the frequency of a pixel being classified as wet over an annual period (calendar year). This is calculated by looking at:Total number of clear observations for each pixel: the number of observations that were clear (no cloud, cloud shadow or terrain shadow) for the selected time period. The classification algorithm then assigns these as either wet, or dry.Total number of wet observation for each pixel: the number of observations that were clear and wet for the selected time period.Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35° SouthTemporal Coverage: 1984 - 2022Spatial Resolution: 30 x 30 meterUpdate frequency: Annual from 1984 - 2022Number of Bands: 3 BandsParent Dataset: Landsat Collection 2 Level-2 Surface Reflectance; WOfS Feature LayerSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)

Available BandsBand IDDescriptionValue rangeData typeNo data valuecount_wetHow many times a pixel was wet0 - 32767int16-999count_clearHow many times a pixel was clear0 - 32767int16-999frequencyFrequency of water detection at a location0 - 1float32NaN

Interpreting WOfSThe WOfS service should be interpreted with caveats in the following situations:Mixed pixels: Discretion should be used where a single pixel covers both water and land. These areas tend to occur on the edges of lakes, and in wetlands where there is a mix of water and vegetation.Turbid or dark water: The WOfS algorithm is developed to identify a diverse range of waterbodies. However, the classifier may miss dark water surfaces or water with high concentration of sediments. In some cases, the impact can be mitigated by using a temporal summary of WOfS, such as the Annual Summary or All-Time Summary. A waterbody may be missed in a single observation, but over the course of the year it is mapped as water in other dates and therefore mapped as a waterbody in the summary products.Other environmental factors: Sediment, floating vegetation and similar obstructions change the colour of water and can obfuscate water detection by WOfS.Inaccurate input data: Inaccurate input surface reflectance may lead to false classification in WOfS. To maximize coverage, all pixels within a valid surface reflectance range (0-1) from Landsat Collection 2 are used to generate the WOFLs. When creating WOfS summaries, only WOFLs processed from Landsat Tier 1 data with good geometric accuracy are used.Note that WOfS is not intended for studying ocean. Validation has been centred around inland and near-coastal waterbodies.

More details on this dataset can be found here.