Mauritius had the highest population density level in Africa as of 2023, with nearly *** inhabitants per square kilometer. The country has also one of the smallest territories on the continent, which contributes to the high density. As a matter of fact, the majority of African countries with the largest concentration of people per square kilometer have the smallest geographical area as well. The exception is Nigeria, which ranks among the largest territorial countries in Africa and is very densely populated at the same time. After all, Nigeria has also the largest population on the continent.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

The average for 2021 based on 47 countries was 119 people per square km. The highest value was in Mauritius: 634 people per square km and the lowest value was in Namibia: 3 people per square km. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

In 2023, the population density in Africa was 50.1 inhabitants per square kilometer. From 2000 onwards, the density of the population on the continent has increased annually. Moreover, the average number of people living within a square kilometer was expected to increase to around 58.5 by 2030. Mauritius, Rwanda, and Burundi were the African countries with the highest population density as of 2023.

Cairo, in Egypt, ranked as the most populated city in Africa as of 2025, with an estimated population of over 23 million inhabitants living in Greater Cairo. Kinshasa, in Congo, and Lagos, in Nigeria, followed with some 17.8 million and 17.2 million, respectively. Among the 15 largest cities in the continent, another one, Kano, was located in Nigeria, the most populous country in Africa. Population density trends in Africa As of 2023, Africa exhibited a population density of 50.1 individuals per square kilometer. Since 2000, the population density across the continent has been experiencing a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 58.5 by the year 2030. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 627 individuals per square kilometre. Mauritius possesses one of the most compact territories on the continent, a factor that significantly influences its high population density. Urbanization dynamics in Africa The urbanization rate in Africa was anticipated to reach close to 45.5 percent in 2024. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating only around a third of the total population then. This trajectory is projected to continue its rise in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2024, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. As of the same year, Africa's population was estimated to expand by 2.27 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.3 percent, reaching its pinnacle at 2.63 percent in 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

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

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

Central African Republic CF: Population Density: People per Square Km data was reported at 8.183 Person/sq km in 2022. This records a decrease from the previous number of 8.206 Person/sq km for 2021. Central African Republic CF: Population Density: People per Square Km data is updated yearly, averaging 4.833 Person/sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 8.206 Person/sq km in 2021 and a record low of 2.784 Person/sq km in 1961. Central African Republic CF: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

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.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.

Surgo Ventures' Africa CCVI ranks 756 regions across 48 African countries on their vulnerability—or their ability to mitigate, treat, and delay transmission of the coronavirus. Vulnerability is assessed based on many factors grouped into seven themes: socioeconomic status, population density, access to transportation and housing; epidemiological factors; health system factors; fragility; and age. The index reflects risk factors for COVID-19, both in terms of clinical outcomes and socioeconomic impact.

The Africa CCVI is the only index to measure vulnerability to COVID-19 within most countries in Africa at this level of detail. The index is modular to reflect the reality that vulnerability is a multi-dimensional construct, and two regions could be vulnerable for very different reasons. This allows stakeholders to customize pandemic responses informed by vulnerability on each dimension. For example, policymakers can identify areas for scaling up COVID-19 testing that are more vulnerable on theme two - population density - or direct community health workers or mobile health units to areas that are vulnerable due to weak health systems infrastructure. The modularity of the Africa CCVI can help governments design lean and precise responses for subnational regions during each phase of the pandemic.

Data files:

Africa_CCVI_subnational_zenodo.csv: Africa CCVI and seven themes' scores for 756 administrative level-1 regions across 48 countries

Africa_CCVI_country_zenodo.csv: Africa CCVI and seven themes scores across 36 countries (12 countries excluded as country-specific data sources were used for them)

DHS_raw_indicators_Zenodo.csv: this CSV contains indicator data for 36 countries, data was primarily sourced from Demographic and Health Surveys (DHS) in addition to other sources (listed in accvi-data-sources.xlsx)

non_DHS_raw_indicators_Zenodo.csv: 12 countries that did not have a recent DHS, so we used country-specific surveys, MICS UNICEF, and other sources (listed in accvi-data-sources.xlsx)

accvi-data-sources.xlsx: data sources used for ACCVI indicators

zenodo_data_dictionary.csv: names and definitions of variables used in data files

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

SETTLEMENT EXTENTS:The Mozambique Settlement Extents Version 01.01. database are polygons representing areas where there is likely a human settlement based on the presence of buildings detected in satellite imagery. Settlement extents are not meant to represent the boundaries of an administrative unit or locality. A single settlement extent may be made up of multiple localities, especially in urban areas. Each settlement extent has an associated population estimate. Provided is information on the common operational boundary that the extent fully resides within along with their associated place codes (PCodes)The data are in geodatabase format and consist of a single-feature class combining built-up areas (BUA), small settlement areas (SSA), and hamlets (hamlets). A built-up area (BUA) is generally an area of urbanization with moderately-to-densely-spaced buildings and a visible grid of streets and blocks. Built up areas are characterized as polygons containing 13 or more buildings across an area greater than or equal to 400,000 square meters. A small settlement (SSA) is a settled area of permanently inhabited structures and compounds of roughly a few hundred to a few thousand inhabitants. The housing pattern in SSAs is an assemblage of family compounds adjoining other similar habitations. Small settlement areas are characterized as polygons containing 50 or more buildings across an area less than 400,000 square meters. A hamlet is a collection of several compounds or sleeping houses in isolation from small settlements or urban areas. Hamlets are characterized as polygons containing between 1 and 49 buildings across an area less than 400,000 square meters. Extent: The country's Admin Level 0 Boundaries. The overall extent of the layer is limited to the overall extent of the building footprint data set and may not reflect the extent of official administrative boundaries. Coordinate system: GCS WGS 1984.For full methodological details please explore data release statement available for download here. POPULATION ATTRIBUTES:The associated population estimates for the Settlement Extents datasets are derived from two WorldPop high resolution data sources. (1) The WorldPop Top-down constrained population estimates 2020 (Population) uses, for each country, the highest admin level official population totals of the 2000 and 2010 census rounds, that are publicly available and can be mapped to associated boundaries, and projects them to 2020. These projected values then disaggregated statistically to 100x100m resolution using a set of detailed geospatial datasets to disaggregate them to grid cell-based counts. The estimates are constrained to settlements based on the satellite-derived building footprint data from Maxar/ecopia for the 51 African countries, and based on a built settlement growth model of WorldPop for the remaining countries.(2) The Population Counts / Constrained Individual countries 2020 UN adjusted (100m resolution) population estimates (Pop_UN_adj) recognises that the United Nations produce their own estimates of national population totals. WorldPop, in order to provide flexibility to users, adjusted the number of people per pixel of its top-down constrained population estimates nationally to match the corresponding official United Nations population estimates (i.e. 2019 Revision of World Population Prospects).For more information about WorldPop’s methods, see: https://www.worldpop.org/methods/populations and https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained"Population Counts / Constrained Individual countries 2020 (100m resolution)" & "Population Counts / Constrained Individual countries 2020 UN adjusted (100m resolution)" derived from WorldPop.org(3) The GRID3 population estimates (pop_wp1_2) are based on the most recent and best available data of that country and were calculated using the random forest-based dasymetric mapping approach, using the random forest algorithm, to produce a gridded population density data set. This weighting layer was subsequently used to dasymetrically disaggregate population counts from district level totals into grid cells, based on those grid cells which were deemed “settled”. District level population totals from the 2017 Census were downloaded from INE’s website (https://www.ine.gov.mz/iv-rgph-2017). Please, read the relevant data documentation on the WorldPop open Data Repository (https://wopr.worldpop.org/?MOZ/Population).

The African waste management market, valued at $21.72 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 4.98% from 2025 to 2033. This expansion is driven by several key factors. Rapid urbanization across the continent is leading to increased waste generation, particularly in major cities like Lagos, Cairo, and Johannesburg. Simultaneously, rising environmental awareness among governments and citizens is fueling demand for improved waste management infrastructure and sustainable disposal methods. The increasing adoption of recycling and waste-to-energy technologies also contributes to market growth. Furthermore, stricter environmental regulations and government initiatives aimed at promoting waste management are creating a conducive environment for market players. However, challenges remain, including insufficient funding for waste management projects in many African countries, a lack of advanced waste management technologies in certain regions, and inconsistent waste collection services in some areas. Despite these challenges, significant opportunities exist. The diverse waste streams – industrial, municipal solid, hazardous, e-waste, plastic, and biomedical – present various avenues for investment and innovation. The market is segmented by disposal methods, including landfills, incineration, dismantling, and recycling, each presenting specific growth potentials. The presence of established players like Averda, Enviroserv, and Interwaste, alongside emerging local companies and social enterprises, signifies a dynamic and competitive landscape. Focusing on sustainable solutions, leveraging technology, and partnering with local communities are crucial for companies seeking success in this growing market. The growth trajectory is expected to be particularly strong in countries with higher population density and economic growth, such as Nigeria, South Africa, and Egypt. Addressing the existing infrastructure gaps and promoting public-private partnerships will be key to unlocking the full potential of the African waste management sector. This comprehensive report provides a detailed analysis of the burgeoning Africa waste management industry, encompassing the historical period (2019-2024), base year (2025), and forecast period (2025-2033). With a focus on key market segments and influential players, this report is an invaluable resource for businesses, investors, and policymakers seeking to understand and capitalize on the opportunities within this rapidly evolving sector. The study covers a market valued at billions, projecting significant growth fueled by increasing urbanization, rising environmental awareness, and supportive government regulations. Search terms like "African waste management market," "waste recycling Africa," "municipal solid waste Africa," and "e-waste management Africa" are strategically incorporated for maximum search engine visibility. Recent developments include: October 2022- In line with the conditions stated on June 9, 2022, SUEZ, Royal Bafokeng Holdings (RBH), and African Infrastructure Investment Managers (AIIM) finalized the acquisition of EnviroServ Proprietary Holdings Ltd and its subsidiaries (collectively, "EnviroServ") after receiving permission from the regional antitrust authorities. By this purchase, SUEZ will be able to solidify both its presence in Africa and its position as a global leader in the treatment of municipal and industrial waste., May 2022- Innovative trash solutions are being introduced to developing markets in the Middle East and Africa thanks to a historic new deal between IFC and Averda International, one of the largest privately held integrated waste management firms in those regions. The UAE-based firm will be able to continue its planned development in South Africa, Oman, and Morocco with the help of a USD 30 million loan from IFC, which will help it become more resilient after the epidemic. With integrated waste management services driven by the private sector, the historic agreement will contribute to delivering advantages for the environment. with this deal, IFC makes its first investment in the continent of Africa and the Middle East's private waste management market.. Notable trends are: Increasing Awareness towards the Waste Management.

Aquatic ecosystems globally have been invaded by molluscs. Tarebia granifera is a highly successful invader, often becoming the dominant aquatic invertebrate species in an invaded ecosystem. Resultingly, it has been suggested that T. granifera may have severe negative impacts on these invaded ecosystems. Limited information is available regarding the population structures and densities of T. granifera, particularly in invaded countries such as South Africa, and information on this could assist in developing management and control strategies for this invasive species. The aim of the present study was to assess the current distribution, densities, and population structures of T. granifera in invaded habitats on the Limpopo and Phongolo River systems, South Africa. This was accomplished by collecting aquatic benthic molluscs from sites across these systems. Water quality parameters were measured at each site and water samples collected for chemical nutrient analyses. The density of snails ..., Nineteen sites were selected for sampling in this study based on known distributions of aquatic snails (invasive and native) and accessibility to sites (Figure 1). Four sites were sampled on the lower Phongolo River in northern KwaZulu-Natal (August – September 2017), South Africa, and a total of 15 sites were sampled on the Limpopo River and its tributaries (Limpopo River system), in the Mpumalanga and Limpopo provinces of South Africa (April – May 2021). Two sites in the Limpopo River system on the Olifants River (OLIF-1, OLIF-2) were sampled in consecutive years (2020 and 2021). Mollusc sampling and identification Each of the 19 selected sites was sampled for aquatic benthic molluscs (including snails and bivalves/clams), using a metal-frame square benthic sampler (30 x 30 cm, 2 mm mesh size). The sampler was used to scoop up sediment and sieve through the benthic zone of the river. Random samples, each comprising three replicates consisting of ten scoops per replicate (30 sampler s..., , # Tarebia granifera and other benthic snails abundances and associated Water Quality at selected sites of easterly flowing rivers in South Africa

Description of the data and file structure

The data for this paper is all within a single Excel file. Each tab in the file is associated with a different metric across the 19 sites. These include Abundance, density and percentage contribution of size classes to a population and/or species to a community. Site names correspond to those used in the article.

Site-Specific Information - Relevant to all data sheets in the Excel file

1.) Numbers in parentheses refer to the year the site was sampled.

2.) Four letter site code refers to the specific river: PHON = Phongolo River, OLIF = Olifants River, LIMP = Limpopo River, MOGA = Mogalakwena River, LUVU = Luvuvhu River, G-LETA = Groot Letaba River, LETA = Letaba River, SHIN = Shingwedzi River.

3.) Number following the site code indicates upstream to downstream. i.e. 1 is the most ups...

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.

As of 2020, Botswana had the highest new business density in Africa. The country recorded nearly ** new companies per 1,000 people. Cabo Verde had the second-highest new business density on the continent, with ** new registrations per 1,000 population. South Africa followed, with the proportion standing at **** new businesses per thousand people.

The “infrastructure index” describes the degree of development of physical facilities and networks in a certain area in 2010. The quality of infrastructure is an important measure of the relative adaptive capacity of a region. Regions with developed infrastructure systems are presumed to be better able to adapt to climatic stresses. Improved infrastructure may reduce transactions costs, and strengthen the links between labor and product markets. Moreover, improved infrastructure should encourage the formation of non-farm enterprises as a source of diversification in the short run and, eventually, a transition out of agriculture. The index results from the second cluster of the Principal Component Analysis preformed among 10 potential variables. The analysis identifies three dominant variables, namely “road density”, “road availability” and “infrastructure poverty”, assigning weights of 0.47, 0.36 and 0.17, respectively. Before to perform the analysis all variables were log transformed to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1) in order to be comparable. A shapefile of road network was published by the Center for International Earth Science Information Network of Columbia University in 2013. The “road density” was computed by calculating the Kilometers of road per cell (size 0.5 arc-minute) and then running a focal statistic (radius of about 30 km to spread the effect of a transportation network in a neighborhood). The “road availability” is the road density divided by the logarithm of population. The 0.5 arc-minute grid “infrastructure poverty” is based on the average lights per pixel in 2010, which was produced by NOAA National Geophysical Data Center, divided by the logarithm of population. The original data was highly fragmented and at fine resolution may have contained fine-scale artifacts at urban edges due to data mismatch between the population and night-lights datasets. Thus focal statistics ran within 20 Km to calculate an average values and represents some of the extend influence of the infrastructure network for local people. The density and availability of road is a normally accepted indicator of infrastructure development degree. Moreover, developed road network facilitate the diffusion of rural products to large markets enhancing the income of rural population and sharing the risk of crisis among larger area. The average night light density per capita represents the diffusion of electricity among population and here is considered a proxy of diffusion of developed infrastructural network. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-05-15

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

Infrastructure index (2010)

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

Description: This is aggregated data of individuals or households. The data originates from the South African censuses of 1991, 1996 and 2001, as well as the community survey of 2007. The geographical units were standardised to the 2005 municipal boundaries so that spatial measuring was consistent. The data therefore covers the whole country at a municipal level for different time periods. The major variables focus on employment status. The data set consists of 32 variables and 257 cases. It contains the same socio-economic variables for different time periods, namely 1991, 1996, 2001 and 2007. Combined ranking - municipalities were ranked for each year, i.e. 1991, 1996, 2001 and 2007, in terms of unemployment rate and assigned a rank value. There is also a combined unemployment rank value for all years and all municipalities. Population density - this was calculated by dividing the total population of a municipality in 1991 by the area and the answer is expressed as number of people per square kilometer. The linking of different census geographies was done by using areal interpolation to transfer data from one set of boundaries to another. The 2005 municipality boundaries were used as the common denominator and it is part of a spatial hierarchy developed by Statistics SA for the 2001 census. Abstract: Global unemployment has risen in the past few years and spatial data is required to address the problem effectively. South African unemployment literature focused mostly on a national level of spatial analysis. Some literature refers to spatial aspects that affect unemployment trends, but does not assign a location, e.g. a suburb or municipality. The research was conducted to obtain an understanding of geographical unemployment changes in South Africa over time. The data sets from the South African censuses of 1991, 1996 and 2001, as well as the community survey of 2007 were compared by spatial extent and associated attributes. The representation of change over time was explored and aggregation to a common boundary, such as municipalities was suggested to overcome modifiable areal unit problems. Census data is spatially more detailed than labour force survey data, and census data from pre-1991 might not reflect the post-apartheid labour trends effectively. To determine which unemployment data set is useful for a spatial understanding of unemployment in South Africa, the attributes of various datasets were compared, the completeness of the spatial data, as well as the geographic scale of presentation. South African census data represents employment statistics at the most detailed spatial level. Census data is collected every five to ten years. Initial data capture for censuses was usually at Enumerator Area (EA) level. Prior to 1991 the spatial data (EA and census district boundaries) were represented on hard copy maps only and no digital spatial data were captured. In the 1991 census, unemployment statistics were not directly calculated at EA level. To generate these statistics the number of employed people was subtracted from the economically active population. In the 1996 census, the number of unemployed, employed and economically active people per small area layer (SAL) was provided by Stats SA. The data were re-aggregated by the Human Sciences Research Council (HSRC), which could then be compared with EA data from other years. The 2001 census attribute data were not released at an EA level, and this consequently made comparisons with the previous two censuses very difficult. However, the spatial boundaries for the EAs were made available, and statistical modelling techniques were used by the HSRC to compute unemployment statistics for these boundaries. CS 2007 released statistics only at a municipality level. The linking of different census geographies was done by using areal interpolation to transfer data from one set of boundaries to another. The 2005 municipality boundaries were used as the common denominator and it is part of a spatial hierarchy developed by Statistics SA for the 2001 census. Municipalities were ranked for each year in terms of unemployment rate and assigned a rank value. There is also a combined unemployment rank value for all years and all municipalities. This resulted in a new data set of aggregated data of individuals or households. The geographical units were standardised to the 2005 municipal boundaries so that spatial measuring was consistent. The data therefore covers the whole country at a municipal level for different time periods. The major variables focus on employment status. All people in South Africa on the date of the census in 1991, 1996, and 2001 as well as the households at the time when the 2007 Community Survey was conducted. The South African Census 1996 covered every person present in South Africa on Census Night, 9-10 October 1996 (except foreign diplomats and their families). The South African Census 2001 covered every person present in South Africa on Census Night, 9-10 October 2001 including all de jure household members and residents of institutions. The South African Census 1991 was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night (i.e. at midnight between 7 and 8 March 1991) were therefore enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active were enumerated and included in the figures. The Diplomatic and Consular Corps of& foreign countries were not included. Crews and passengers of ships were also not enumerated, except those who were present at the harbours of the RSA on census night. Similarly, residents of the RSA who were absent from the night were not enumerated. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria). The South African Community Survey 2007 covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions. However, an approximation of the out-of-scope population was made from the 2001 Census and added to the final estimates of the CS 2007 results. Sampling is not applicable since the data used here refers to aggregated data of the universe.