• 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.

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.

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

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

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

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

References:

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

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

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

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

Context

As the world is fighting against this invisible enemy a lot of data-driven students like me want to study it as well as we can. There is an enormous number of data set available on covid19 today but as a beginner, in this field, I wanted to find some more simple data. So here I come up with this covid19 data set which I scrapped from "https://www.worldometers.info/coronavirus". It is my way of learning by doing. This data is till 5/17/2020. I will keep on updating it.

Content

The dataset contains 194 rows and 12 columns which are described below:-

Country: Contains the name of all Countries. Total_Cases: It contains the total number of cases the country has till 5/17/2020. Total_Deaths: Total number of deaths in that country till 5/17/2020. Total_Recovered: Total number of individuals recovered from covid19. Active_Cases: Total active cases in the country on 5/17/2020. Critical_Cases: Number of patients in critical condition. Cases/Million_Population: Number of cases per million population of that country. Deaths/Million_Population: Number of deaths per million population of that country. Total_Tests: Total number of tests performed 5/17/2020 Tests/Million_Population: Number of tests performed per million population. Population: Population of the country Continent: Continent in which the country lies.

Acknowledgements

"https://www.worldometers.info/coronavirus/"

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

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

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

This restriction site associated DNA sequencing (RAD-seq) dataset for Antarctic krill (Euphausia superba) includes raw sequence data and summaries for 148 krill from 5 Southern Ocean sites. A detailed README.pdf file is provided to describe components of the dataset. DNA library preparation was carried out in two separate batches by Floragenex (Eugene, Oregon, USA). RAD fragment libraries (SbfI) were sequenced on an Illumina HiSeq 2000 using single-end 100 bp chemistry. As there is no reference genome for Antarctic krill, a set of unique 90 bp sequences (RAD tags) was assembled from 17.3 million single-end reads from an individual krill. We obtained over a billion raw reads from the 148 krill in our study (a mean of 6.8 million reads per sample). The reference assembly contained 239,441 distinct RAD tags. The core genotype dataset exported for downstream data filtering included just those SNPs with genotype calls in at least 80% of the krill samples and contained 12,114 SNPs on 816 RAD tags.

Sample collection table (comma separated):

Southern Ocean Location, Sample Size, Austral Summer, Latitude, Longitude, ID

East Antarctica (Casey), 21, 2010/2011, 64S, 100E, Cas East Antarctica (Mawson), 22, 2011/2012. 66S, 70E, Maw Lazarev Sea, 38, 2004/2005 and 2007/2008, 66S, 0E, Laz Western Antarctic Peninsula, 16, 2010/2011, 69S, 76W, WAP Ross Sea, 23, 2012/2013, 68S, 178E, Ross

Ecologists are increasingly turning to historical abundance data to understand past changes in animal abundance and more broadly the ecosystems in which animals occur. However, developing reliable ecological or management interpretations from temporal abundance data can be difficult because most population counts are subject to measurement or estimation error.

There is now widespread recognition that counts of animal populations are often subject to detection bias. This recognition has led to the development of a general framework for abundance estimation that explicitly accounts for detection bias and its uncertainty, new methods for estimating detection bias, and calls for ecologists to estimate and account for bias and uncertainty when estimating animal abundance. While these methodological developments are now being increasingly accepted and used, there is a wealth of historical population count data in the literature that were collected before these developments. These historical abundance data may, in their original published form, have inherent unrecognised and therefore unaccounted biases and uncertainties that could confound reliable interpretation. Developing approaches to improve interpretation of historical data may therefore allow a more reliable assessment of extremely valuable long-term abundance data.

This dataset contains details of over 200 historical estimates of Adelie penguin breeding populations across the Australian Antarctic Territory (AAT) that have been published in the scientific literature. The details include attributes of the population count (date and year of count, count value, count object, count precision) and the published estimate of the breeding population derived from those attributes, expressed as the number of breeding pairs. In addition, the dataset contains revised population estimates that have been re-constructed using new estimation methods to account for detection bias as described in the associated publication. All population data used in this study were sourced from existing publications.

description: The Global Rural-Urban Mapping Project (GRUMP), Alpha Version consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and associated datasets dated circa 2000. The data products include population count grids (raw counts), population density grids (per square km), land area grids (actual area net of ice and water), mean geographic unit area grids, urban extents grids, centroids, a national identifier grid, national boundaries, coastlines, and settlement points. These products vary in GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to assign population values to grid cells. Additional global grids are created from the 30 arc-second grid at 1/4, 1/2, and 1 degree resolutions. The Spatial Reference metadata section information applies only to global extent, 30 arc-second resolution. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT). (Suggested Usage: To allow analysis of urban and rural population figures based on a consistent global dataset.); abstract: The Global Rural-Urban Mapping Project (GRUMP), Alpha Version consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and associated datasets dated circa 2000. The data products include population count grids (raw counts), population density grids (per square km), land area grids (actual area net of ice and water), mean geographic unit area grids, urban extents grids, centroids, a national identifier grid, national boundaries, coastlines, and settlement points. These products vary in GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to assign population values to grid cells. Additional global grids are created from the 30 arc-second grid at 1/4, 1/2, and 1 degree resolutions. The Spatial Reference metadata section information applies only to global extent, 30 arc-second resolution. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT). (Suggested Usage: To allow analysis of urban and rural population figures based on a consistent global dataset.)

Common murres are an abundant marine predator in the California Current System yet knowledge gaps exist in colony-associated movements, habitat-use of non-breeding individuals, and post-breeding season movements. We captured 24 common murres at sea, immediately adjacent to Yaquina Head, the largest murre colony in Oregon, USA. Murres were captured in May of 2015-2017 and in August of 2016 and 2017. Murres were fitted with 17 g battery-powered Platform Terminal Transmitters (PTTs; Telonics Inc.) in 2015 and 5 g solar-rechargeable PTTs (Microwave Telemetry Inc.) in the latter years. The data files show raw Argos tracking data with a low-pass filter applied as well as improved processed locations derived from fitting a state-space model. In addition, we present dive data collected and processed (number of dives and mean dive durations per hour) during 2015 only. We assessed the first and last 3 days of tracks (both in daily distance traveled and number of dives) for anomalous activity, and if present, excluded these portions of each track from the dataset. The deployment data file contains morphological information of tagged birds.

The “agriculture resources sensitivity” represents the agriculture potential in 2010. This potential is measured by the availability of land and food production per capita, and the main thread to agriculture land, represented by desertification risk. The index results from the second cluster of the Principal Component Analysis preformed among 16 potential variables. The analysis identify four dominant variables, namely “potential rain-fed food production per capita”, “cropland crowding”, “desertification index” and “topographic resources availability”, assigning respectively the weights of 0.29, 0.29, 0.27 and 0.15. Before to perform the analysis all the 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; all variables with inverse method except “desertification index”) in order to be comparable. The 5 arc-minute grid “potential rain-fed food production per capita” of 2007 was gathered from FAO GeoNetwork and then sampled at 0.5 arc-minutes. It was multiplied by crop land occurrence dataset, extracted from the FAO Global Land Cover-SHARE dataset of 2014 and divided by population grid in order to compute the per capita values. The 0.5 arc-minute grid “cropland crowding” of 2010 was produced dividing the crop land occurrence (FAO Global Land Cover-SHARE) by the population. The 0.5 arc-minute grid “desertification index” of 2000 was measured in terms of number of months recording values less than 0.75 of the ratio between precipitation (current monthly average) and potential evapo-transpiration (PET, current monthly average). Data of precipitation and PET were gathered from Worldclim and from CGIAR Consortium for Spatial Information, respectively. Finally the 0.5 arc-minute grid “topographic resources availability” was produced within the ClimAfrica project based on SRTM DEM of NASA. The “potential rain-fed food production per capita” measures the availability of food in a certain area produced with subsistence techniques. Cells with low food production are sensitive to climate change impacts because the low input agriculture (dominant in Africa) may not produced sufficient food quantities to support the local populations. The “cropland crowding” is an indicator that assess the availability of crop land hectares per 1,000 people. Sensitive areas are where few crop lands are shared by large population. The “desertification index” assesses the climatological risk of a certain area to be subjected to desertification due to lack of rainfall. Such areas are more sensitive to lost crop land and thus food production quantities due to climate change impacts. The “topographic resources availability” is the percentage of each cell with slopes equal to or lower than 15 %. Landscapes strongly dissected contain less land with agriculture values than plain landscapes. The scarcity of agriculture land may increase the fragility of a system because unable to increase to crop surface to cope with climate change impacts. 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: 2014-09-01

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:

Agriculture resources sensitivity 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

Population connectivity and gene flow in near shore Antarctic Echinoids (Sterechinus neumayeri, Abatus nimrodi and Abatus ingens) was investigated in East Antarctica. This data set consists of microsatellite genotype data from 11 novel loci and mitochondrial DNA sequences from two gene region, COI and 16S. In addition, to determine if changes in temperature and salinity impacted fertilisation success in S. neumayeri, and to determine the appropriate sperm to egg ratio for this type of experiment, a fertilisation experiment was completed using various combinations of temperature, salinity and sperm to egg ratio. Samples were collected near two Australian Antarctic research stations, Casey and Davis, during the 08/09 and 09/10 summer field seasons.

To generate the microsatellite data set, a total of 545 adults, nuemayeri and 26 echinoplutei were collected. Spatial replication was achieved by comparing adult populations between two regions (Casey and Davis). These two regions are separated by approximately 1400 km. Sampling in the Casey region was done at two locations 9 km apart and in the Davis region at five locations separated by 5 - 30 km. Within each location 25-50 individuals were collected from up to three sites approximately 0.5 km apart. Within each site, all individuals were collected within an area less than 50 m2. Adult urchins were collected by dip nets, snorkel or scuba depending on location. Echinoplutei were collected from the water column in two locations in the Davis region using a purpose built plankton net. DNA was extracted using QiagenDNeasy Blood and Tissue extraction kits as per the manufacturer's protocols. PCR amplification was carried out in four multiplex reactions and analysis of the PCR product was carried out on a CEQ 8000 (Beckman Coulter) automated sequencer by capillary separation, and alleles scored as fragment size using CEQ 8000 Genetic Analysis System software (ver. 8.0).

Data available: Data consists of 571 individual genotypes at 11 loci in an excel spreadsheet following the GenAlEx v 6.41 layout. Sites from the Davis region are; Old Wallow 1 (OW1), Old Wallow 2 (OW2), Boyd Island (BO1), Ellis Fjord 1 (EL1), Ellis Fjord 2 (EL2), Ellis Fjord 3 (EL3), Trigwell Island 1 (TR1), Trigwell Island 2 (TR2), Trigwell Island 3 (TR3), Zappit Point 1 (ZP1), Zappit Point 2 (ZP2), Zappit Point 3 (ZP3). Sites from the Casey region are; Browning Peninsula 1 (CB1), Browning Peninsula 2 (CB2), Browning Peninsula 3 (CB3), Sparkes Bay 1 (CS1), Sparkes Bay 2 (CS2).Echinoplutei samples are Hawker Island (D1); Kazak Island 1 (K1); Kazak Island 2 (K2) Data is coded as fragment length, with a zero value representing no data.

To generate the mtDNA sequence data, a total of 24 S. neumayeri individuals were sequenced for the COI gene region with two haplotypes found. For the 16S gene region, 25 individuals were sequenced with three haplotypes founds. For Abatusingens, 51 individuals were sequenced with six CO1 haplotypes and five 16S haplotypes. For Abatus nimrodi (n = 48) there were two CO1 haplotypes and eight 16S haplotypes. In addition, eight A. shackeltoni, four A. philippii and one A. cavernosus sample were included from the Davis region.

Data available: data are available in four FASTA text format files, one for Abatus COI data, one forAbatus 16S data, one for Sterechinus COI data. Individuals are coded with the first two letters representing species (SN = S. neumayeri, AN = A. nimrodi, AI = A. ingens, AS = A. shackletoni, AC= A. cavernosus) the next two representing gene region (CO = COI, 16 = 16S) and either three or four more digits for Davis region samples or five digits beginning with 41 for Casey region samples.

To generate the fertilisation data set, S. neumayeri were collected from Ellis Fjord prior to ice breakout. A total of 12 individuals were screened for the fertilisation experiment, seven males and five females to ensure a suitable cross where greater than 90% fertilisation success was achievable. Sperm were activated with FSW at -1.8 degrees C and sperm concentration determined using a haemocytometer. Three temperature treatments, (-1.8 degrees C, 1 degrees C and 3 degrees C), three salinity treatments (35ppt, 30ppt and 25ppt), and five sperm to egg ratios (50:1, 100:1, 500:1, 1500:1 and 2500:1) were used during fertilisation, with four replicates at each temperature:salinity:sperm to egg ratio combination. After 30 min, three to five drops of 10% formalin were added to each vial to fix eggs and to prevent further fertilisation from occurring. To determine percentage fertilisation, the first 100 eggs encountered from each vial were scored as either fertilised or unfertilised based on the presence or absence of an elevated fertilisation membrane.

Data available: Data are available as an excel file, with three spreadsheets, one for each temperature treatment. Each spreadsheet consists of three tables, one for each salinity treatment. Each salinity treatment table con...

The dataset comprises records of crossings by Adelie penguins of a weighbridge and gateway established on Bechervaise Island. The weighbridge and gateway are positioned so that most or all of the penguins breeding in a set of sub-colonies on the island cross the weighbridge when they leave the colony to forage and when they return from foraging. The gateway records the time of each crossing, the dynamic weight of the penguin as it crosses, and the identity of penguins that have been sub-cutaneously tagged. The weighbridge and gateway operate continuously throughout the austral breeding season. The data are currently in an unprocessed form.

This dataset is for data collected between 2006 and 2018.

As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.

New-ID: NBI18

The Africa Major Infrastructure and Human Settlements Dataset

Files: TOWNS2.E00 Code: 100022-002 ROADS2.E00 100021-002

Vector Members: The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename

The Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent

References:

ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC.

Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC.

DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC.

Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago

Source: FAO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Points Format: Arc/Info export non-compressed Related Datasets: All UNEP/FAO/ESRI Datasets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments: There is no outline of Africa

New-ID: NBI16

Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10.

The Africa Agro-ecological Zones Dataset documentation

Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013

Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename.

The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot.

Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA

The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent

References:

ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC.

G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC.

FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division.

FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48.

Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53)