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.

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

Dataset population: All usual residents aged 16 and over in employment the week before the census

Location of where people live when working

The location in which an individual lives when they are working.

Place of work

The location in which an individual works.

Geographies of origin areas:

• UK Local Authorities (merged) 2011

Geographies of destination areas:

• UK Local Authorities (merged) 2011
• 2011 UK SWS additional workplace codes

For the area in which people live while they are working, if that address is a work-related second address that is outside of the UK then this is signified by code OD0000005.

*The following codes are used for area of workplace that is not an LAD geographic code:

OD0000001 = Mainly work at or from home

OD0000002 = Offshore installation

OD0000003 = No fixed place

OD0000004 = Outside UK*

This map features a global estimate of human population for 2016 with a focus on the Caribbean region . Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: https://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones.

Number of People living in an Area by sub-county

Worldwide, around 5.8 billion people were living in countries classified as autocracies in 2024. Parts of this is down to the fact that the world's two most populous countries, China and India, who both count around 1.4 billion inhabitants, are classified as a closed autocracy and electoral autocracy, respectively. Similarly, the large increase in people living in autocracies in 2017 is because India was downgraded from an electoral democracy to an electoral autocracy that year.

Estimated number of persons on July 1, by 5-year age groups and gender, and median age, for Canada, provinces and territories.

This statistic shows the international migrant stock worldwide as a percentage of the global population from 1990 to 2015. In 2015, the international migrant stock accounted fro *** of the world population.

In 2019, people from most ethnic minority groups were more likely than White British people to live in the most deprived neighbourhoods.

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: Yes - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: All places defined by walls and roofs where one or more people live regularly, that is where they sleep, cook and protect themselves from the elements. Also people can enter and leave the mentioned without passing through another house, having direct access from the street, passage, path or passing through common areas such as patios, hallways, corridors or stairs. - Households: Group of people who live as a family - Group quarters: This is a place or building where a group of people without family ties resides and share the space for reasons of lodging, health, education, military, religion, old age, orphanhood, etc. This includes hotels, boarding houses, guest houses, hospitals, homes for the elderly, internment schools, hospices, jails, etc.

Universe

All people who live in the country and all households nationally. Homeless

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: General Directorate of Statistics and Censuses

SAMPLE SIZE (person records): 510760.

SAMPLE DESIGN: Stratified systematic sample. Homeless

Mode of data collection

Face-to-face [f2f]

Research instrument

Census questionnaire containing questions on demographic and socio-economic characteristics of the population, dwelling unit characteristics, emigration, and mortality.

This dataset provides annual numbers for each state in the United States for 2013-2018. Includes the following data: total population, median income, and number of people living at or below the poverty level.

Helpful information on using U.S. Census data is found at https://censusreporter.org/

Estimated number of persons by quarter of a year and by year, Canada, provinces and territories.

This map features the World Population Density Estimate 2016 layer for the Caribbean region. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: https://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

Table contains count and percentage of county residents living alone. Data are presented for people of all ages and those 65 years and older. The measure is summarized at county, city, zip code and census tract. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B09019, B09020; data accessed on July 20, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.MATEDATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (String): Geography IDNAME (String): Name of geographyt_pop (Numeric): Total populationt_pop_livingalone (Numeric): Number of people (all ages) living alonepct_pop_livingalone (Numeric): Percent of people (all ages) living alonet_p65plus (Numeric): People ages 65 and oldert_pop65_livingalone (Numeric): Number of people ages 65 and older living alonepct_pop65_livingalone (Numeric): Percent of people ages 65 and older living alone

In 2024, the population of the Los Angeles-Long Beach-Anaheim metropolitan area in the United States was about 12.93 million people. This is a slight increase from the 12.88 million people who lived there the previous year.

Analysis unit

Persons and households

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: The dwelling is a building or collection of buildings used as the living unit of the household. - Households: A household is a set of people who live together. An ordinary household is as an individual or a group of people live together to collectively meet their food and other vital needs. It is made up of a group of people, related or not, who recognize the authority of an individual called the "head of household", live under the same roof or in the same compound and take their meals together. - Group quarters: A collective household is made up of a group of people, without a priori family tie, who live together in the same institution for health, study, work, travel, disciplinary or other reasons. Examples of collective households are boarding schools, barracks and military boarding schools, prisons, hotels, hospitals, convents, orphanages, boarding schools, etc.

Universe

The resident population, defined as all people who lived for at least six months in the households where they were enumerated or who intend to stay there for at least six months.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics

SAMPLE SIZE (person records): 1050916.

SAMPLE DESIGN: Systematic Sample of every 10th dwelling with a random start, drawn by IPUMS

Mode of data collection

Face-to-face [f2f]

Research instrument

The Ordinary Household Questionnaire

Gender, age, language, income, poverty status, indigenous identity, immigrant status, visible minority, education and labour force status of the population residing in resource-based communities (census subdivisions where a relatively high proportion of employment income comes from fishing, forestry, or agriculture), for 2021.

This table contains 1392 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years), and was last released on 2007-01-29. This table contains data described by the following dimensions (Not all combinations are available): Geography (29 items: Austria; Belgium (French speaking); Canada; Belgium (Flemish speaking) ...), Sex (2 items: Males; Females ...), Age groups (3 items: 11 years; 13 years;15 years ...), Student response (2 items: Yes; No ...), Family member (4 items: Mother; Father; Stepfather; Stepmother ...).

Tuvalu TV: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 13.900 % in 2010. Tuvalu TV: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 13.900 % from Dec 2010 (Median) to 2010, with 1 observations. The data reached an all-time high of 13.900 % in 2010 and a record low of 13.900 % in 2010. Tuvalu TV: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tuvalu – Table TV.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Poland PL: International Migrant Stock: % of Population data was reported at 1.604 % in 2015. This records a decrease from the previous number of 1.665 % for 2010. Poland PL: International Migrant Stock: % of Population data is updated yearly, averaging 2.011 % from Dec 1990 (Median) to 2015, with 6 observations. The data reached an all-time high of 2.953 % in 1990 and a record low of 1.604 % in 2015. Poland PL: International Migrant Stock: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Poland – Table PL.World Bank.WDI: Population and Urbanization Statistics. International migrant stock is the number of people born in a country other than that in which they live. It also includes refugees. The data used to estimate the international migrant stock at a particular time are obtained mainly from population censuses. The estimates are derived from the data on foreign-born population--people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population--that is, people who are citizens of a country other than the country in which they reside--are used as estimates. After the breakup of the Soviet Union in 1991 people living in one of the newly independent countries who were born in another were classified as international migrants. Estimates of migrant stock in the newly independent states from 1990 on are based on the 1989 census of the Soviet Union. For countries with information on the international migrant stock for at least two points in time, interpolation or extrapolation was used to estimate the international migrant stock on July 1 of the reference years. For countries with only one observation, estimates for the reference years were derived using rates of change in the migrant stock in the years preceding or following the single observation available. A model was used to estimate migrants for countries that had no data.; ; United Nations Population Division, Trends in Total Migrant Stock: 2008 Revision.; Weighted average;