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 prolonged development arc in Sub-Saharan Africa.

This is a repository of global and regional human population data collected from: the databases of scenarios assessed by the Intergovernmental Panel on Climate Change (Sixth Assessment Report, Special Report on 1.5 C; Fifth Assessment Report), multi-national databases of population projections (World Bank, International Database, United Nation population projections), and other very long-term population projections (Resources for the Future). More specifically, it contains: - in other_pop_data folder files from World Bank, the International Database from the US Census, and from IHME - in the SSP folder, the Shared Socioeconomic Pathways, downloaded from IIASA - in the UN folder, the demographic projections from UN - IAMstat.xlsx, an overview file of the metadata accompanying the scenarios present in the IPCC databases - RFF.csv, an overview file containing the population projections obtained by Resources For the Future '- the remaining .csv files with names AR6#, AR5#, IAMC15# contain the IPCC scenarios assessed by the IPCC for preparing the IPCC assessment reports. They can be downloaded from AR5, SR 1.5, and AR6 This data should be used downloaded for use together with the package downloadable here.

Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

The total population in the United States was estimated at 341.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - United States Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This Excel based tool enables users to query the raw single year of age data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Each year the GLA demography team produce sets of population projections. The full raw data by single year of age (SYA) and gender are available as Datastore packages at the links below. How to use the tool Simply select the lower and upper age range for both males and females (starting in cell C3) and the spreadsheet will return the total population for the range. Find out more about GLA population projections on the GLA Demographic Projections page Click here for an archive of population projections from previous years that have since been superseded. 2019-based projections (published November 2020) Central range (upper bound) Central range (lower bound) Low population variant High population variant 2016-based projections (published July 2017) Housing-linked projection incorporating data from the 2016 SHLAA Ward-level projections consistent with the borough housing-led model Ethnic group projections consistent with the borough housing-led model (50MB file)

The total population in World was estimated at 8142.1 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset includes a chart with historical data for World Population.

Data Source: CA Department of Finance, Demographic Research Unit

Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.

This data biography shares the how, who, what, where, when, and why about this dataset. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org.

Data dashboard featuring this data: Napa County Demographics https://data.countyofnapa.org/stories/s/bu3n-fytj

How was the data collected? Population projections use the following demographic balancing equation: Current Population = Previous Population + (Births - Deaths) +Net Migration

Previous Population: the starting point for the population projection estimates is the 2020 US Census, informed by the Population Estimates Program data.

Births and Deaths: birth and death totals came from the California Department of Public Health, Vital Statistics Branch, which maintains birth and death records for California.

Net Migration: multiple sources of administrative records were used to estimate net migration, including driver’s license address changes, IRS tax return data, Medicare and Medi-Cal enrollment, federal immigration reports, elementary school enrollments, and group quarters population.

Who was included and excluded from the data? Previous Population: The goal of the US Census is to reflect all populations residing in a given geographic area. Results of two analyses done by the US Census Bureau showed that the 2020 Census total population counts were consistent with recent counts despite the challenges added by the pandemic. However, some populations were undercounted (the Black or African American population, the American Indian or Alaska Native population living on a reservation, the Hispanic or Latino population, and people who reported being of Some Other Race), and some were overcounted (the Non-Hispanic White population and the Asian population). Children, especially children younger than 4, were also undercounted.

Births and Deaths: Birth records include all people who are born in California as well as births to California residents that happened out of state. Death records include people who died while in California, as well as deaths of California residents that occurred out of state. Because birth and death record data comes from a registration process, the demographic information provided may not be accurate or complete.

Net Migration: each of the multiple sources of administrative records that were used to estimate net migration include and exclude different groups. For details about methodology, see https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf.

Where was the data collected?  Data is collected throughout California. This subset of data includes Napa County.

When was the data collected? This subset of Napa County data is from Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.

These 2019 baseline projections incorporate the latest historical population, birth, death, and migration data available as of July 1, 2020. Historical trends from 1990 through 2020 for births, deaths, and migration are examined. County populations by age, sex, and race/ethnicity are projected to 2060.

Why was the data collected?  The population projections were prepared under the mandate of the California Government Code (Cal. Gov't Code § 13073, 13073.5).

Where can I learn more about this data? https://dof.ca.gov/Forecasting/Demographics/Projections/ https://dof.ca.gov/wp-content/uploads/sites/352/Forecasting/Demographics/Documents/P3_Dictionary.txt https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf

The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

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Excel age range creator for GLA Projections data

This Excel based tool enables users to query the raw single year of age data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Each year the GLA demography team produce sets of population projections. On this page each of these datasets since 2009 can be accessed, though please remember that the older versions have been superceded. From 2012, data includes population estimates and projections between 2001 and 2041 for each borough plus Central London (Camden, City of London, Kensington & Chelsea, and Westminster), Rest of Inner Boroughs, Inner London, Outer London and Greater London.

The full raw data by single year of age (SYA) and gender are available as Datastore packages at the links below.

How to use the tool: Simply select the lower and upper age range for both males and females (starting in cell C3) and the spreadsheet will return the total population for the range.

Tip: You can copy and paste the boroughs you are interested in to another worksheet by clicking: Edit then Go To (or Control + G), then Special, and Visible cells only. Then simply copy and 'paste values' of the cells to a new location.

Warning: The ethnic group and ward files are large (around 35MB), and may take some time to download depending on your bandwidth.

Find out more about GLA population projections on the GLA Demographic Projections page

BOROUGH PROJECTIONS

GLA 2009 Round London Plan Population Projections (January 2010) (SUPERSEDED)

GLA 2009 Round (revised) London Plan Population Projections (August 2010) (SUPERCEDED)

GLA 2009 Round (revised) SHLAA Population Projections (August 2010) (SUPERCEDED)

GLA 2010 Round SHLAA Population Projections (February 2011) (SUPERCEDED)

GLA 2011 Round SHLAA Population Projections, High Fertility (December 2011) (SUPERCEDED)

GLA 2011 Round SHLAA Population Projections, Standard Fertility (January 2012) (SUPERCEDED)

GLA 2012 Round SHLAA Population Projections, (December 2012)(SUPERCEDED)

GLA 2012 Round Trend Based Population Projections, (December 2012) (SUPERCEDED)

GLA 2012 Round SHLAA Borough Projections incorporating DCLG 2011 household formation rates, (June 2013) (SUPERCEDED)

GLA 2013 Round Trend Based Population Projections - High (December 2013) (SUPERCEDED)

GLA 2013 Round Trend Based Population Projections - Central (December 2013) (SUPERCEDED)

GLA 2013 Round Trend Based Population Projections - Low (December 2013) (SUPERCEDED)

GLA 2013 Round SHLAA Based Population Projections (February 2014) (SUPERCEDED) Spreadsheet now includes national comparator data from ONS.

GLA 2013 Round SHLAA Based Capped Population Projections (March 2014) (SUPERCEDED) Spreadsheet includes national comparator data from ONS.

GLA 2014 Round Trend-based, Short-Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS.

GLA 2014 Round Trend-based, Long-Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS.

GLA 2014 Round SHLAA DCLG Based Long Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS.

GLA 2014 Round SHLAA Capped Household Size Model Short Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS. This is the recommended file to use.

WARD PROJECTIONS

GLA 2008 round (High) Ward Projections (March 2009) (SUPERSEDED)

GLA 2009 round (revised) London Plan Ward Projections (August 2010) (SUPERCEDED)

GLA 2010 round SHLAA Ward Projections (February 2011) (SUPERCEDED)

GLA 2011 round SHLAA Standard Ward Projections (January 2012) (SUPERCEDED)

GLA 2011 round SHLAA High Ward Projections (January 2012) (SUPERCEDED)

GLA 2012 round SHLAA based Ward Projections (March 2013) (XLS) (SUPERCEDED)

GLA 2012 round SHLAA Ward Projections (March 2013) (XLS) (SUPERCEDED)

GLA 2013 round SHLAA Ward Projections (March 2014) (SUPERCEDED)

GLA 2013 round SHLAA Capped Ward Projections (March 2014) (SUPERCEDED)

GLA 2014 round SHLAA Capped Household Size Model Short Term Migration Scenario Ward Projections (April 2015) This is the recommended file to use.

ETHNIC GROUP PROJECTIONS FOR LOCAL AUTHORITIES

GLA 2012 Round SHLAA Ethnic Group Borough Projections - Interim (May 2013) (SUPERCEDED)

GLA 2012 Round Trend Based Ethnic Group Borough Projections - Interim (May 2013) (SUPERCEDED)

GLA 2012 Round SHLAA Based Ethnic Group Borough Projections - Final (Nov 2013) (SUPERCEDED)

GLA 2012 Round Trend Based Ethnic Group Borough Projections - Final (Nov 2013) (SUPERCEDED)

GLA 2013 Round SHLAA Capped Ethnic Group Borough Projections (August 2014)

The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

Original and derived data products referenced in the original manuscript are provided in the data package.

Description of the data and file structure

Original data:

Table_1_source_papers.csv: Papers that met review criteria and which are summarized in Table 1 of the manuscript.

1. ID: The paper identification number
2. Topic: The broad topic (i.e., each row of Table 1)
3. Authors: The names of the authors of the paper
4. Article Title: The title of the paper
5. Source Title: The name of the journal in which the paper was published
6. Abstract: The paper's abstract, retrieved from the Web of Science search
7. study_type: Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.
8. pathogen_broad: Broad classification of the type of pathogen discussed in the paper.
9. transmission_type: Categorization of indirect, direct, sexual, vector, or other transmission modes.
10. pathogen_type: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.
11. country: Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).

Derived data:

change_livestock_country.csv: A dataframe containing values used to generate Figure 4a in the manuscript.

1. County Name: The name of the county in Kenya
2. Sheep and goats 1980: The estimated number of sheep and goats in 1980
3. Sheep and goats 2016: The estimated number of sheep and goats in 2016
4. pct_change_shoat: The percent change in sheep and goat numbers from 1980 to 2016
5. Cattle 1980: The estimated number of cattle in 1980
6. Cattle 2016: The estimated number of cattle in 2016
7. pct_change_cattle: The percent change in cattle numbers from 1980 to 2016
8. Camel 1980: The estimated number of camels in 1980
9. Camel 2016: The estimated number of camels in 2016
10. pct_change_camel: The percent change in camel numbers from 1980 to 2016
11. human_pop 1980: The estimated human population in the county in 1980
12. human_pop 2016: The estimated human population in the county in 1980
13. pct_change_human: The percent change in the human population from 1980 to 2016
14. area_sq_km: The land area of the county
15. change_ind_per_sq_km_shoat: Absolute change in number of sheep and goats from 1980 to 2016
16. change_ind_per_sq_km_cattle: Absolute change in number of cattle from 1980 to 2016
17. change_ind_per_sq_km_camel: Absolute change in number of camels from 1980 to 2016

country_avg_schist_wormy_world.csv: A dataframe containing values used to generate Figure 3 in the manuscript.

• Country: The country in which the schistosome prevalence studies were performed.
• Latitude: The latitute in decimal degrees
• Longitude: The longitute in decimal degrees
• Maximum.prevalence: The mean maximum schistosomiasis prevalence of studies conducted within each country.

kenya_precip_change_1951_2020.csv: A dataframe containing values used to generate Figure 4b in the manuscript.

• Precipitation (mm): Binned annual precipitation values
• 1951-1980: The density of observations for each annual precipitation value for the 1951-1980 period
• 1971-2000: The density of observations for each annual precipitation value for the 1971-2000 period
• 1991-2020: The density of observations for each annual precipitation value for the 1991-2020 period

Sharing/Access information

Data were derived from the following sources:

• Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PloS ONE, 11(9), e0163249. https://doi.org/10.1371/journal.pone.0163249

• London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global Atlas of Helminth Infections: STH and Schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0

• World Bank Group. (2023). Climate Data & Projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections

The Global Human Footprint Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Interrupted Goode Homolosine Projection (IGHP) is produced by the Wildlife Conservation Society (WCS) and Columbia University Center for International Earth Science Information Network (CIESIN). To provide an updated map of anthropogenic impacts on the environment in Interrupted Goode Homolosine Projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions.

Effective population size (Ne) is a particularly useful metric for conservation as it affects genetic drift, inbreeding and adaptive potential within populations. Current guidelines recommend a minimum Ne of 50 and 500 to avoid short-term inbreeding and to preserve long-term adaptive potential, respectively. However, the extent to which wild populations reach these thresholds globally has not been investigated, nor has the relationship between Ne and human activities. Through a quantitative review, we generated a dataset with 4610 georeferenced Ne estimates from 3829 unique populations, extracted from 723 articles. These data show that certain taxonomic groups are less likely to meet 50/500 thresholds and are disproportionately impacted by human activities; plant, mammal, and amphibian populations had a <54% probability of reaching = 50 and a <9% probability of reaching = 500. Populations listed as being of conservation concern according to the IUCN Red List had a smaller median than unlisted populations, and this was consistent across all taxonomic groups. was reduced in areas with a greater Global Human Footprint, especially for amphibians, birds, and mammals, however relationships varied between taxa. We also highlight several considerations for future works, including the role that gene flow and subpopulation structure plays in the estimation of in wild populations, and the need for finer-scale taxonomic analyses. Our findings provide guidance for more specific thresholds based on Ne and help prioritize assessment of populations from taxa most at risk of failing to meet conservation thresholds. Methods Literature search, screening, and data extraction A primary literature search was conducted using ISI Web of Science Core Collection and any articles that referenced two popular single-sample Ne estimation software packages: LDNe (Waples & Do, 2008), and NeEstimator v2 (Do et al., 2014). The initial search included 4513 articles published up to the search date of May 26, 2020. Articles were screened for relevance in two steps, first based on title and abstract, and then based on the full text. For each step, a consistency check was performed using 100 articles to ensure they were screened consistently between reviewers (n = 6). We required a kappa score (Collaboration for Environmental Evidence, 2020) of ³ 0.6 in order to proceed with screening of the remaining articles. Articles were screened based on three criteria: (1) Is an estimate of Ne or Nb reported; (2) for a wild animal or plant population; (3) using a single-sample genetic estimation method. Further details on the literature search and article screening are found in the Supplementary Material (Fig. S1). We extracted data from all studies retained after both screening steps (title and abstract; full text). Each line of data entered in the database represents a single estimate from a population. Some populations had multiple estimates over several years, or from different estimation methods (see Table S1), and each of these was entered on a unique row in the database. Data on N̂e, N̂b, or N̂c were extracted from tables and figures using WebPlotDigitizer software version 4.3 (Rohatgi, 2020). A full list of data extracted is found in Table S2. Data Filtering After the initial data collation, correction, and organization, there was a total of 8971 Ne estimates (Fig. S1). We used regression analyses to compare Ne estimates on the same populations, using different estimation methods (LD, Sibship, and Bayesian), and found that the R2 values were very low (R2 values of <0.1; Fig. S2 and Fig. S3). Given this inconsistency, and the fact that LD is the most frequently used method in the literature (74% of our database), we proceeded with only using the LD estimates for our analyses. We further filtered the data to remove estimates where no sample size was reported or no bias correction (Waples, 2006) was applied (see Fig. S6 for more details). Ne is sometimes estimated to be infinity or negative within a population, which may reflect that a population is very large (i.e., where the drift signal-to-noise ratio is very low), and/or that there is low precision with the data due to small sample size or limited genetic marker resolution (Gilbert & Whitlock, 2015; Waples & Do, 2008; Waples & Do, 2010) We retained infinite and negative estimates only if they reported a positive lower confidence interval (LCI), and we used the LCI in place of a point estimate of Ne or Nb. We chose to use the LCI as a conservative proxy for in cases where a point estimate could not be generated, given its relevance for conservation (Fraser et al., 2007; Hare et al., 2011; Waples & Do 2008; Waples 2023). We also compared results using the LCI to a dataset where infinite or negative values were all assumed to reflect very large populations and replaced the estimate with an arbitrary large value of 9,999 (for reference in the LCI dataset only 51 estimates, or 0.9%, had an or > 9999). Using this 9999 dataset, we found that the main conclusions from the analyses remained the same as when using the LCI dataset, with the exception of the HFI analysis (see discussion in supplementary material; Table S3, Table S4 Fig. S4, S5). We also note that point estimates with an upper confidence interval of infinity (n = 1358) were larger on average (mean = 1380.82, compared to 689.44 and 571.64, for estimates with no CIs or with an upper boundary, respectively). Nevertheless, we chose to retain point estimates with an upper confidence interval of infinity because accounting for them in the analyses did not alter the main conclusions of our study and would have significantly decreased our sample size (Fig. S7, Table S5). We also retained estimates from populations that were reintroduced or translocated from a wild source (n = 309), whereas those from captive sources were excluded during article screening (see above). In exploratory analyses, the removal of these data did not influence our results, and many of these populations are relevant to real-world conservation efforts, as reintroductions and translocations are used to re-establish or support small, at-risk populations. We removed estimates based on duplication of markers (keeping estimates generated from SNPs when studies used both SNPs and microsatellites), and duplication of software (keeping estimates from NeEstimator v2 when studies used it alongside LDNe). Spatial and temporal replication were addressed with two separate datasets (see Table S6 for more information): the full dataset included spatially and temporally replicated samples, while these two types of replication were removed from the non-replicated dataset. Finally, for all populations included in our final datasets, we manually extracted their protection status according to the IUCN Red List of Threatened Species. Taxa were categorized as “Threatened” (Vulnerable, Endangered, Critically Endangered), “Nonthreatened” (Least Concern, Near Threatened), or “N/A” (Data Deficient, Not Evaluated). Mapping and Human Footprint Index (HFI) All populations were mapped in QGIS using the coordinates extracted from articles. The maps were created using a World Behrmann equal area projection. For the summary maps, estimates were grouped into grid cells with an area of 250,000 km2 (roughly 500 km x 500 km, but the dimensions of each cell vary due to distortions from the projection). Within each cell, we generated the count and median of Ne. We used the Global Human Footprint dataset (WCS & CIESIN, 2005) to generate a value of human influence (HFI) for each population at its geographic coordinates. The footprint ranges from zero (no human influence) to 100 (maximum human influence). Values were available in 1 km x 1 km grid cell size and were projected over the point estimates to assign a value of human footprint to each population. The human footprint values were extracted from the map into a spreadsheet to be used for statistical analyses. Not all geographic coordinates had a human footprint value associated with them (i.e., in the oceans and other large bodies of water), therefore marine fishes were not included in our HFI analysis. Overall, 3610 Ne estimates in our final dataset had an associated footprint value.

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.

this graph was created in R:

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driven primarily by high birth rates in developing countries and advancements in healthcare. According to the United Nations, the global population surpassed 8 billion in 2023, marking a critical milestone in human history. This growth, however, is unevenly distributed across continents and countries, leading to varied population densities and urban pressures.

Surface area and population density play vital roles in shaping the demographic and economic landscape of each country. For instance, countries with large land masses such as Russia, Canada, and Australia have low population densities despite their significant populations, as vast portions of their land are sparsely populated or uninhabitable. Conversely, nations like Bangladesh and South Korea exhibit extremely high population densities due to smaller land areas combined with large populations.

Population density, measured as the number of people per square kilometer, affects resource availability, environmental sustainability, and quality of life. High-density areas face greater challenges in housing, infrastructure, and environmental management, often experiencing increased pollution and resource strain. In contrast, low-density areas may struggle with underdeveloped infrastructure and limited access to services due to the dispersed population.

Urbanization trends are another important aspect of these dynamics. As people migrate to cities seeking better economic opportunities, urban areas grow more densely populated, amplifying the need for efficient land use and sustainable urban planning. The UN reports that over half of the world’s population currently resides in urban areas, with this figure expected to rise to nearly 70% by 2050. This shift requires nations to balance population growth and density with sustainable development strategies to ensure a higher quality of life and environmental stewardship for future generations.

Through an understanding of population size, surface area, and density, policymakers can better address challenges related to urban development, rural depopulation, and resource allocation, supporting a balanced approach to population management and economic development.

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Understanding why some species are at high risk of extinction, while others remain relatively safe, is central to the development of a predictive conservation science. Recent studies have shown that a species' extinction risk may be determined by two types of factors: intrinsic biological traits and exposure to external anthropogenic threats. However, little is known about the relative and interacting effects of intrinsic and external variables on extinction risk. Using phylogenetic comparative methods, we show that extinction risk in the mammal order Carnivora is predicted more strongly by biology than exposure to high-density human populations. However, biology interacts with human population density to determine extinction risk: biological traits explain 80% of variation in risk for carnivore species with high levels of exposure to human populations, compared to 45% for carnivores generally. The results suggest that biology will become a more critical determinant of risk as human populations expand. We demonstrate how a model predicting extinction risk from biology can be combined with projected human population density to identify species likely to move most rapidly towards extinction by the year 2030. African viverrid species are particularly likely to become threatened, even though most are currently considered relatively safe. We suggest that a preemptive approach to species conservation is needed to identify and protect species that may not be threatened at present but may become so in the near future.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name