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

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

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

A better understanding of malaria persistence in highly seasonal environments such as highlands and desert fringes requires identifying the factors behind the spatial reservoir of the pathogen in the low season. In these ‘unstable’ malaria regions, such reservoirs play a critical role by allowing persistence during the low transmission season and therefore, between seasonal outbreaks. In the highlands of East Africa, the most populated epidemic regions in Africa, temperature is expected to be intimately connected to where in space the disease is able to persist because of pronounced altitudinal gradients. Here, we explore other environmental and demographic factors that may contribute to malaria's highland reservoir. We use an extensive spatio-temporal dataset of confirmed monthly Plasmodium falciparum cases from 1995 to 2005 that finely resolves space in an Ethiopian highland. With a Bayesian approach for parameter estimation and a generalized linear mixed model that includes a spatially structured random effect, we demonstrate that population density is important to disease persistence during the low transmission season. This population effect is not accounted for in typical models for the transmission dynamics of the disease, but is consistent in part with a more complex functional form of the force of infection proposed by theory for vector-borne infections, only during the low season as we discuss. As malaria risk usually decreases in more urban environments with increased human densities, the opposite counterintuitive finding identifies novel control targets during the low transmission season in African highlands.

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

I studied the spatial distribution of South African avian species richness from the viewpoint that humans are a substantial modifying force on earth, and have also modified the historical spatial distribution of species richness. The main aim of the thesis is to investigate the way in which humans have modified avian species richness patterns in South Africa at the quarter-degree square (QDS) resolution, which is a phenomenon that has been either overlooked, or not completely clarified, in many previous studies of the same region and data at the same resolution. In particular, I investigated hypotheses that were proposed to explain the maintenance of a positive relationship between native species richness and human population density in the face of negative human impacts. Further, I investigated which of the possible anthropogenic and natural environmental factors determine spatial distribution in exotic bird species. Highlighted from these studies are that substantial positive and negative human influences on bird species richness distribution patterns are observable at the QDS resolution, that there are differences between common native birds and rare native birds with regard to their relationships with anthropogenic environmental conditions and exotic bird species, and that the particular combination of environmental covariates that is important for the spatial distributions of exotic species is taxon- and scale-dependent. Even though these results have contributed much towards our understanding on how human modifications have affected species richness patterns, this thesis leaves some unanswered questions. Finer resolution studies and temporal studies are needed to examine many of these questions. Further, an interdisciplinary approach incorporating politics and economics into ecological studies is needed to enhance our understanding of the factors that modify the distribution of humans and their associated threats and benefits to species richness.

VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Nigeria: (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](https://dataforgood.fb.com/docs/methodology-high-resolution-population-density-maps-demographic-estimates/

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

In 2021, there were around *** million children aged 0-4 years in Africa. In total, the population aged 17 years and younger amounted to approximately *** million. In contrast, only approximately ** million individuals were aged 65 years and older as of the same year. The youngest continent in the world Africa is the continent with the youngest population worldwide. As of 2023, around ** percent of the population was aged 15 years and younger, compared to a global average of 25 percent. Although the median age on the continent has been increasing annually, it remains low at around ***years. There are several reasons behind the low median age. One factor is the low life expectancy at birth: On average, the male and female population in Africa live between 61 and 65 years, respectively. In addition, poor healthcare on the continent leads to high mortality, also among children and newborns, while the high fertility rate contributes to lowering the median age. Cross-country demographic differences Africa’s demographic characteristics are not uniform across the continent. The age structure of the population differs significantly from one country to another. For instance, Niger and Uganda have the lowest median age in Africa, at **** and **** years, respectively. Not surprisingly, these countries also register a high crude birth rate. On the other hand, North Africa is the region recording the highest life expectancy at birth, with Algeria leading the ranking in 2023.

According to the forecast, Africa's total population would reach nearly 2.5 billion by 2050. In 2023, the continent had around 1.36 billion inhabitants, with Nigeria, Ethiopia, and Egypt as the most populous countries. In the coming years, Africa will experience significant population growth and will close the gap significantly with the Asian population by 2100. Rapid population growth The population of Africa has been increasing annually in recent years, growing from around 818 million to over 1.39 billion between 2000 and 2021, respectively. In the same period, the annual growth rate of the population has been constantly set at roughly 2.5 percent, with a peak of 2.62 percent in 2014. The reasons behind this rapid growth are various. One factor is the high fertility rate registered in African countries. In 2021, a woman in Niger had an average of over 6.8 children in her reproductive years, the highest rate on the continent. High fertility resulted in a large young population and partly compensated for the high mortality rate in Africa, leading to fast-paced population growth. High poverty levels Africa’s population is concerned with widespread poverty. In 2024, over 429 million people on the continent are extremely poor and live with less than 2.15 U.S. dollars per day. Globally, Africa is the continent hosting the highest poverty rate. In 2024, the countries of Nigeria and the Democratic Republic of the Congo account for around 21 percent of the world's population living in extreme poverty. Nevertheless, poverty in Africa is forecast to decrease in the coming years.

VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Nigeria: (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

Abstract

The Future of African Remittances (FAR) team conducted research on remittance flows to measure and understand the remittance process in sub-Saharan Africa. This ambitious and important research is initially focused on three countries in East Africa - Ethiopia, Kenya and Uganda.

In order to glean insights into the remittance process in the three designated countries, the World Bank designed a two-phase survey process. Phase 1 involved conducting a national survey in each of the three countries. The purpose of the first phase of research was to collect a large representative sample of the adult population in each country. The national surveys provide important baseline data about international remittance flows including: an estimate of the percent of the total adult population that regularly receives remittances, the average amount of each remittance received, most common methods of receipt and top sending countries. Additionally, through the analysis of the national survey results, World Bank was able to identify areas of each country that have high concentrations of international remittance recipients. This important piece of information guided Phase 2 of the research - surveys of remittance receivers in each country. Whereas the national surveys aimed to collect general data about the remittance process, the surveys of remittance recipients allowed for the collection of more detailed data about the remittance process itself, how remittances are used, the relationship between sender and receiver, and interest in various financial products.

The results of this research will not only provide estimates of total annual amounts of remittances for each country, but also will tell us the percentage of the population in each country that is involved in the international remittance process. Furthermore, it will offer insights as to the degree to which Ethiopians, Kenyans and Ugandans depend on international remittances and how the money is used, saved and/or invested. Results will also measure interest in financial products that, if utilized, can significantly impact the financial well-being of the population and the overall economic stability of each country.

Geographic coverage

National Coverage

Analysis unit

Households Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

General:

The total samples were compiled utilizing multi-stage stratified random sampling through respondent selection. Multi-stage random sampling ensured that a random sample of adults was collected in each country. First, after stratifying the population of each country by region and population density, sampling points (SPs) were determined. SPs were then randomly selected within each stratum. At each SP, respondents were randomly selected to participate in the survey.

Phase 1:

The first phase consisted of national surveys of the adult population of each country. The three survey samples were designed to be representative of the adult populations of these three countries. World Bank coordinated and oversaw all aspects of the sampling and interviewing process. A team of local field experts was hired in each country to conduct the actual interviews. All interviewers were professionally trained and supervised by research personnel. In this phase of the research, a total of 2022 Kenyan adults were interviewed.

Phase 2:

Once the national surveys were completed, the results were analyzed to determine the areas of concentration of the remittance recipient population, after which the second phase of the project was conducted. This phase of the project included a targeted survey of the remittance recipient population of each of the three East African countries. Sampling Points were established based on the analysis of the national survey data and the identification of areas within each country that showed the highest concentrations of remittances received from relatives abroad in proportion to the sample size of all areas surveyed. Once again, local field experts were hired in each country to conduct the interviews, training and supervision of field operations. Languages of interviews were the same as those employed in Phase 1 and, again, all interviews were conducted in person using the PAPI method. A total of 400 interviews with regular international remittance recipients were completed in each country during August and September of 2010. The margin of error for all three surveys is approximately ±5 percentage points and the 95 percent level of confidence.

Detail:

The total sample was compiled utilizing multi-stage stratified random sampling through respondent selection. This sampling method enabled B&A to ensure that a representative random sample of Kenyan adults was collected. There are three stages to this type of sampling methodology. First, after stratifying the Kenyan population by region and population density, sampling points (SPs) were determined. SPs were then randomly selected within each stratum. In the second stage, using the random route method, dwellings were selected within each SP. The random route method involves selecting an address in each SP at random as a starting point. Each interviewer was given instructions to identify additional dwellings by taking alternate left and right turns and stopping at every Nth dwelling. The third and final stage involved selecting actual participants - for each selected dwelling, individual respondents were chosen using a Kish grid. In a Kish grid, prior to beginning the interview, the interviewer first asks for the ages and genders of every household member (only persons aged 18 or older were eligible for selection). The individual to be interviewed was then chosen based on a random number in the grid.

Once the national survey was completed, B&A analyzed the results to determine the areas of concentration of the remittance recipient population, after which the second phase of the project was conducted. This phase included a targeted survey of the remittance recipient population in Kenya. Sampling Points were established based on B&A's analysis of the national survey data and the identification of areas of the country that showed concentrations of international remittance receivers in proportion to the sample size of all areas surveyed. Once again, local field experts were hired to conduct the interviews and B&A conducted all training and supervision of field operations. Interviews were conducted in English or Swahili depending on respondent preference and all interviews were conducted in person using the PAPI method. A total of 401 interviews with regular international remittance recipients were conducted in Kenya during August and September of 2010. The margin of error for the surveys is approximately ±5 percentage points and the 95 percent level of confidence.

Mode of data collection

Face-to-face [f2f]

Research instrument

Phase 1:

This survey consisted of 12 questions that were aimed at helping to identify some of the basic characteristics of the remittance recipient population in each country. Some of the variables included in this survey were - location, age, gender, amount of money received, method of receipt, origin of remittance, etc.

Phase 2:

The survey instrument for Phase 2 consisted of approximately 35 questions and included a number of variables aimed at obtaining greater detail about the remittance receiving process including costs, amounts received, information about the sender and the relationship between sender and receiver. Additionally, the survey measured interest in various financial products.

Response rate

Every effort was made to achieve the maximum possible coverage, taking cost, timing and other factors into account. A coverage rate of 85% was achieved in the national survey and the 15% of the country that was not covered consisted of areas that were either very remote (and difficult to travel to) or that had extremely small populations.

Sampling error estimates

The margin of error is approximately ±5 percentage points and the 95 percent level of confidence.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

Abstract

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

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

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

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

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

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

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

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

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

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

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

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

7. Particulars of the institution

8. Availability of piped water for the institution

9. Main source of water for domestic use

10. Main type of toilet facility

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

12. Disposal of refuse or rubbish

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

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

15. The Post Enumeration Survey Questionnaire (English)

These questionnaires are provided as external resources.

Cleaning operations

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

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

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

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

Data appraisal

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

VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Togo: (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

Within carnivore guilds, dominant competitors (e.g., lions, Panthera leo) are limited primarily by the density of prey, while subordinate competitors (e.g., African wild dogs, Lycaon pictus) have been limited by the density of dominant competitors. Historically, the fitness and population density of subordinate competitors have not been tightly linked to prey density. However, populations of large herbivores have declined substantially across sub-Saharan Africa due to human impacts, and where prey depletion is severe, fitness costs for competitive subordinates may begin to outweigh the benefits of competitive release. Using long-term intensive monitoring of African wild dogs in Zambia's Luangwa Valley Ecosystem (LVE), we tested the effects of prey depletion on survival and reproduction. Our study area included four contiguous regions that varied in protection, prey density, and lion density. We fit Bayesian Cormack-Jolly-Seber and closed-capture models to estimate effects on survival and population density, and generalized linear models to estimate effects on reproductive success. We found that the LVE is a stronghold for wild dogs, with an estimated median density of 4.0 individuals/100 km². Despite this high density, survival and reproduction differed among regions, and both components of fitness were substantially reduced in the region with the lowest prey density. Anthropogenic prey depletion is becoming an important limiting factor for African wild dogs. If prey depletion (or any other form of habitat degradation) becomes severe enough that its fitness costs outweigh the benefits of competitive release, such changes can fundamentally alter the balance between limiting factors for competitively subordinate species.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Cote d'Ivoire: (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

In Sub-Saharan Africa and other developing regions, there has been very little systematic attempt to document the uses and perceived health benefits of urban green spaces in cities and the factors influencing usage. We therefore sought to establish the availability, accessibility and use of urban green spaces, and the perceived health benefits in an African population. We also ascertained the factors influencing use and development of green spaces at home. A population-based survey was conducted in Accra, the capital city of Ghana, spanning 11 Municipal and 3 Sub-Metropolitan areas. Multivariable binary logistic regression adjusting for potential confounders was used to establish the association between green space use and development at home, and socio-demographic, neighbourhood and health factors. Odds ratios and their corresponding 95% confidence intervals were estimated from the models. Several socio-demographic (gender, age, marital status, occupation, ethnicity, religion) and district-level (population density, income level, neighbourhood greenness) factors were associated with use of green spaces and development of green spaces at home in Accra. Residents who were worried about depletion of green spaces in their community were more likely to develop green spaces at home. In neighbourhoods with moderate and high level of greenness, residents were less likely to develop green spaces at home. Five-percent and 47% of green space users in Accra reported witnessing an improvement in their physical and mental health, respectively, from use of green spaces. The study findings can inform policy action for promoting use and development of green spaces in African cities and for mitigating depletion and degradation of the limited urban greenery.

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

There are approximately 8.16 billion people living in the world today, a figure that shows a dramatic increase since the beginning of the Common Era. Since the 1970s, the global population has also more than doubled in size. It is estimated that the world's population will reach and surpass 10 billion people by 2060 and plateau at around 10.3 billion in the 2080s, before it then begins to fall. Asia When it comes to number of inhabitants per continent, Asia is the most populous continent in the world by a significant margin, with roughly 60 percent of the world's population living there. Similar to other global regions, a quarter of inhabitants in Asia are under 15 years of age. The most populous nations in the world are India and China respectively; each inhabit more than three times the amount of people than the third-ranked United States. 10 of the 20 most populous countries in the world are found in Asia. Africa Interestingly, the top 20 countries with highest population growth rate are mainly countries in Africa. This is due to the present stage of Sub-Saharan Africa's demographic transition, where mortality rates are falling significantly, although fertility rates are yet to drop and match this. As much of Asia is nearing the end of its demographic transition, population growth is predicted to be much slower in this century than in the previous; in contrast, Africa's population is expected to reach almost four billion by the year 2100. Unlike demographic transitions in other continents, Africa's population development is being influenced by climate change on a scale unseen by most other global regions. Rising temperatures are exacerbating challenges such as poor sanitation, lack of infrastructure, and political instability, which have historically hindered societal progress. It remains to be seen how Africa and the world at large adapts to this crisis as it continues to cause drought, desertification, natural disasters, and climate migration across the region.

The distribution, spatial pattern and population dynamics of a species can be influenced by differences in the environment across its range. Spatial variation in climatic conditions can cause local populations to undergo disruptive selection and ultimately result in local adaptation. However, local adaptation can be constrained by gene flow and may favour resident individuals over migrants—both are factors critical to the assessment of invasion potential. The Natal fruit fly (Ceratitis rosa) is a major agricultural pest in Africa with a history of island invasions, although its range is largely restricted to south east Africa. Across Africa, C. rosa is genetically structured into two clusters (R1 and R2), with these clusters occurring sympatrically in the north of South Africa. The spatial distribution of these genotypic clusters remains unexamined despite their importance for understanding the pest's invasion potential. Here, C. rosa, sampled from 22 South African locations, were genotyped at 11 polymorphic microsatellite loci and assessed morphologically using geometric morphometric wing shape analyses to investigate patterns of population structure and determine connectedness of pest-occupied sites. Our results show little to no intraspecific (population) differentiation, high population connectivity, high effective population sizes and only one morphological type (R2) within South Africa. The absence of the R1 morphotype at sites where it was previously found may be a consequence of differences in thermal niches of the two morphotypes. Overall, our results suggest high invasion potential of this species, that area-wide pest management should be undertaken on a country-wide scale, and that border control is critical to preventing further invasions.