Our Population Density Grid Dataset for Northern 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.

This dataset is about countries in Northern Africa. It has 6 rows. It features 2 columns including rural population.

This dataset is about books. It has 4 rows and is filtered where the book subjects is Africa, North-Population. It features 9 columns including author, publication date, language, and book publisher.

North African Berbers dataset

This dataset is about countries in Northern Africa. It has 6 rows. It features 3 columns: continent, and population.

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA "rapid" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA "slow" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

This dataset is about cities in Northern Africa. It has 214 rows. It features 7 columns including country, population, latitude, and longitude.

This dataset is about countries in Northern Africa. It has 6 rows. It features 3 columns: individuals using the Internet, and population.

Abstract

The South African Population Research Infrastructure Network (SAPRIN) is a national research infrastructure funded through the Department of Science and Technology and hosted by the South African Medical Research Council. One of SAPRIN’s initial goals has been to harmonise the legacy 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 116 000 people; the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, established in 1996, with a current population of 100 000; and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, established in 2000, with a current population of 125 000.

SAPRIN data are processed for longitudinal analysis by organising the demographic data into residence episodes at a geographical location, and membership episodes within a household. Start events include enumeration, birth, in-migration and relocating into a household from within the study population; exit events include death (by cause), out-migration, and relocating to another location in the study population. Variables routinely updated at individual level include health care utilisation, marital status, labour status, education status, as well as recording household asset status. Anticipated outcomes of SAPRIN include: (i) regular releases of up-to-date, longitudinal data, representative of South Africa’s fast-changing poorer communities for research, interpretation and calibration of national datasets; (ii) national statistics triangulation, whereby longitudinal SAPRIN data are triangulated with National Census data for calibration of national statistics and studying the mechanisms driving the national statistics; (iii) An interdisciplinary research platform for conducting observational and interventional research at population level; (iv) policy engagement to provide evidence to underpin policy-making for cost evaluation and targeting intervention programmes, thereby improving the accuracy and efficiency of pro-poor, health and wellbeing interventions; (v) scientific education through training at related universities; and (vi) community engagement, whereby coordinated engagement with communities will enable two-way learning between researchers and community members, and enabling research site communities and service providers to have access to and make effective use of research results.

Geographic coverage

The Agincourt HDSS covers an area of approximately 420km2 and is located in Bushbuckridge District, Mpumalanga in the rural north-east of South Africa close to the Mozambique border. DIMAMO is located in the Capricorn district, Limpopo Province approximately 40 km from Polokwane, the capital city of Limpopo Province and 15-50 km from the University of Limpopo (Turfloop Campus). The site covers an area of approximately 200 km2. 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 strandles the highway) and in the north by the Inyalazi river for portions of the boundary. The area is 438km2.

Analysis unit

Exposure episodes

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 the household will be enumerated. A resident household member is an individual that intends to sleep the majority of time at the dwelling occupied by the household over a four-month period. Households will include resident and non-resident members. An individual is a non-resident member if they have close ties to the household, but do not physically reside with the household most of the time. They can also be called temporary migrants and they are enumerated within the household list. Because household membership is not tied to physical residency, an individual may be a member of more than one household.

Kind of data

Event/transaction data

Sampling procedure

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

South Africa Population: 15 to 64 Years: North West data was reported at 2,566.773 Person th in Sep 2018. This records an increase from the previous number of 2,555.849 Person th for Jun 2018. South Africa Population: 15 to 64 Years: North West data is updated quarterly, averaging 2,334.501 Person th from Mar 2008 (Median) to Sep 2018, with 43 observations. The data reached an all-time high of 2,566.773 Person th in Sep 2018 and a record low of 2,129.546 Person th in Mar 2008. South Africa Population: 15 to 64 Years: North West 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.

This dataset is about countries in Northern Africa. It has 6 rows. It features 3 columns: urban land area, and female population.

This dataset shows different breakdowns of London's resident population by their country of birth. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Four files are available for download: Country of Birth - Borough: Shows country of birth estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Country of Birth - London: Shows country of birth estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole Demography Update 09-2015: A GLA Demography report that uses APS data to analyse the trends in London for the period 2004 to 2014. A supporting data file is also provided. Country of Birth Borough 2004-2016 Analysis Tool: A tool produced by GLA Demography that allows users to explore different breakdowns of country of birth data. An accompanying Tableau visualisation tool has also been produced which maps data from 2004 to 2015. 2011 Census Country of Birth data can be found here: https://data.london.gov.uk/census/themes/diversity/ Nationality data can be found here: https://data.london.gov.uk/dataset/nationality Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

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.

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Maize (Zea mays), also known as corn, is a crop of world wide importance. Originally domesticated in what is now Mexico, its tolerance of diverse climates has lead to its widespread cultivation. Globally, it is tied with rice as the second most widely grown crop. Only wheat is more widely grown. In Africa it is grown throughout the agricultural regions of the continent from the Nile Delta in the north to the country of South Africa in the south. In sub-Saharan Africa it is relied on as a staple crop for 50% of the population. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofmaize harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of maize as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Cassava Groundnut (Peanut) Millet Potato Rice Sorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started follow these links: Landscape Layers - a reintroductionLiving Atlas Discussion Group

For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Occupation data for 2021 and 2022
The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022

APS Well-Being Datasets
From 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.

APS disability variables
Over time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage.

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address

The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Abstract

The Ouagadougou Health and Demographic Surveillance System (Ouagadougou HDSS), located in five neighborhoods at the northern periphery of the capital of Burkina Faso, was established in 2008. Data on vital events (births, deaths, unions, migration events) are collected during household visits that have taken place every 10 months.

The areas were selected to contrast informal neighborhoods (40,000 residents) with formal areas (40,000 residents), with the aims of understanding the problems of the urban poor, and testing innovative programs that promote the well-being of this population. People living in informal areas tend to be marginalized in several ways: they are younger, poorer, less educated, farther from public services and more often migrants. Half of the residents live in the Sanitary District of Kossodo and the other half in the District of Sig-Nonghin.

The Ouaga HDSS has been used to study health inequalities, conduct a surveillance of typhoid fever, measure water quality in informal areas, study the link between fertility and school investments, test a non-governmental organization (NGO)-led program of poverty alleviation and test a community-led targeting of the poor eligible for benefits in the urban context. Key informants help maintain a good rapport with the community.

The areas researchers follow consist of 55 census tracks divided into 494 blocks. Researchers mapped all the census tracks and blocks using fieldworkers with handheld global positioning system (GPS) receivers and ArcGIS. During a first census (October 2008 to March 2009), the demographic surveillance system was explained to every head of household and a consent form was signed; during subsequent censuses, new households were enrolled in the same way.

Geographic coverage

Ouagadougou is the capital city of Burkina Faso and lies at the centre of this country, located in the middle of West Africa (128 North of the Equator and 18 West of the Prime Meridian).

Analysis unit

Individual

Universe

Resident household members of households resident within the demographic surveillance area. Inmigrants (visitors) are defined by intention to become resident, but actual residence episodes of less than six months (180 days) are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than six months (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 residents during the study period (03 Oct. 2009 to 31 Dec. 2014).

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 0 to 7 of demographic surveillance data covering the period from 07 Oct. 2008 to 31 December 2014.

Sampling procedure

This dataset is not based on a sample, it contains information from the complete demographic surveillance area of Ouagadougou in Burkina Faso.

Reponse units (households) by Round: Round Households
2008 4941
2009 19159 2010 21168
2011 12548 2012 24174 2013 22326

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

List of questionnaires:

Collective Housing Unit (UCH) Survey Form - Used to register characteristics of the house - Use to register Sanitation installations - All registered house as at previous round are uploaded behind the PDA or tablet.

Household registration (HHR) or update (HHU) Form - Used to register characteristics of the HH - Used to update information about the composition of the household - All registered households as at previous rounds are uploaded behind the PDA or tablet.

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 - All member status observations as at previous rounds are uploaded behind the PDA or tablet.

Presences registration form (PDR) - Used to uniquely identify the presence of each individual in the household and to identify the new individual in the household - Mainly to ensure members with multiple household memberships are appropriately captured - All presences observations as at previous rounds are uploaded behind the PDA or tablet.

Visitor registration form (VDR) - Used register the characteristics of the new individual in the household - Used to capt the internal migration - Use matching form to facilitate pairing migration

Out Migration notification form (MGN) - Used to record change in the status 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 - All member pregnancy without pregnancy outcome as at previous rounds are uploaded behind the PDA or tablet.

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

Updated Basic information Form (UBIF) - Use to change the individual basic information

Health questionnaire (adults, women, child, elder) - Family planning - Chronic illnesses - Violence and accident - Mental health - Nutrition, alcohol, tobacco - Access to health services - Anthropometric measures - Physical limitations - Self-rated health - Food security

Variability of climate and water accessibility - accessibility to water - child health outcomes - gender outcomes - data on rainfall, temperatures, water quality

Cleaning operations

The data collection system is composed by two databases: - A temporary database, which contains data collected and transferred each day during the round. - A reference database, which contains all data of Ouagadougou Health and Demographic Surveillance System, in which is transferred the data of the temporary database to the end of each round. The temporary database is emptied at the end of the round for a new round.

The data processing takes place in two ways:

1) When collecting data with PDAs or tablets and theirs transfers by Wi-Fi, data consistency and plausibility are controlled by verification rules in the mobile application and in the database. In addition to these verifications, the data from the temporary database undergo validation. This validation is performed each week and produces a validation report for the data collection team. After the validation, if the error is due to an error in the data collection, the field worker equipped with his PDA or tablet go back to the field to revisit and correct this error. At the end of this correction, the field worker makes again the transfer of data through the wireless access points on the server. If the error is due to data inconsistencies that might not be directly related to an error in data collection, the case is remanded to the scientific team of the main database that could resolve the inconsistency directly in the database or could with supervisors perform a thorough investigation in order to correct the error.

2) At the end of the round, the data from the temporary database are automatically transferred into the reference database by a transfer program. After the success of this transfer, further validation is performed on the data in the database to ensure data consistency and plausibility. This still produces a validation report for the data collection team. And the same process of error correction is taken.

Response rate

Household response rates are as follows (assuming that if a household has not responded for 2 years following the last recorded visit to that household, that the household is lost to follow-up and no longer part of the response rate denominator):

Year Response Rate
2008 100%
2009 100%
2010 100%
2011 98% 2012 100% 2013 95%

Sampling error estimates

Not applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate BF041 MicroDataCleaned Starts 151624 2017-05-16 13:36
BF041 MicroDataCleaned Transitions 0 314778 314778 0 2017-05-16 13:36
BF041 MicroDataCleaned Ends 151624 2017-05-16 13:36
BF041 MicroDataCleaned SexValues 314778 2017-05-16 13:36
BF041 MicroDataCleaned DoBValues 314778 2017-05-16 13:36

Population Data 2006. Population data at Province, District and Sub-district level. (Census 2006)

COVID19-Algeria-and-World-Dataset

A coronavirus dataset with 104 countries constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the COVID-19. The assumptions for the different factors are as follows:

• Geography: some continents/areas may be more affected by the disease
• Climate: cold temperatures may promote the spread of the virus
• Healthcare: lack of hospital beds/doctors may lead to more human losses
• Economy: weak economies (GDP) have fewer means to fight the disease
• Demography: older populations may be at higher risk of the disease

The last column represents the number of daily tests performed and the total number of cases and deaths reported each day.

Data description

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Countries in the dataset by geographic coordinates

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• Europe: 33 countries
• Asia: 28 countries
• Africa: 21 countries
• North America: 11 countries
• South America: 8 countries
• Oceania: 3 countries

Statistical description of the data

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Data distribution

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Download

The dataset is available in an encoded CSV form on GitHub.

Python code

The Python Jupyter Notebook to read and visualize the data is available on nbviewer.

Data update

The dataset is updated every month with the latest numbers of COVID-19 cases, deaths, and tests. The last update was on March 01, 2021.

Data construction

The dataset is constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the coronavirus. Note that we selected only the main factors for which we found data and that other factors can be used. All data were retrieved from the reliable Our World in Data website, except for data on:

• Continents: www.kaggle.com/statchaitya/country-to-continent
• Geographic-coordinates: www.kaggle.com/eidanch/counties-geographic-coordinates
• Temperatures: www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data
• Share of the population over 65 years old: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS
• GDP/Capita: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD

Citation

If you want to use the dataset please cite the following arXiv paper, more details about the data construction are provided in it.

@article{belkacem_covid-19_2020,
  title = {COVID-19 data analysis and forecasting: Algeria and the world},
  shorttitle = {COVID-19 data analysis and forecasting},
  journal = {arXiv preprint arXiv:2007.09755},
  author = {Belkacem, Sami},
  year = {2020}
}

Contact

If you have any question or suggestion, please contact me at this email address: s.belkacem@usthb.dz

This dataset is about countries in Northern Africa. It has 6 rows. It features 3 columns: male population, and rural population.