The Vatican City, often called the Holy See, has the smallest population worldwide, with only *** inhabitants. It is also the smallest country in the world by size. The islands Niue, Tuvalu, and Nauru followed in the next three positions. On the other hand, India is the most populous country in the world, with over *** billion inhabitants.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Person, household, and dwelling

UNITS IDENTIFIED: - Dwellings: Yes - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: A dwelling is a building or independent building unit that is built, adapted or converted so that it may be inhabited by one or more people, either permanently or temporarily. It should have direct or independent access from the street or through public-use spaces, like hallways, patios, or stairs. It is normally separated by walls and a roof so that the people who live in it may separate themselves from others for cooking and eating, sleeping, and protection from the environment. - Households: A household is a person or group of persons, related or not, who occupy all or part of a dwelling. They share at least the main meals and provide for their other basic needs from a common budget - Group quarters: A collective dwelling is intended for habitation by persons, usually without family ties, who are subject to administrative rules and who live together for reasons of education, health, religion, work, or tourism, among others. Among collective dwellings there are 2 varieties: institutional and non-institutional.

Universe

All persons residing in the country.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics and Computing

SAMPLE DESIGN: Systematic sample was drawn from the 15% stratified sample developed by the statistical office.

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 2,745,895

Mode of data collection

Face-to-face [f2f]

Research instrument

A single form with three sections for the dwelling, household, and individuals

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

A dataset listing New York counties by population for 2024.

A dataset listing Louisiana cities by population for 2024.

California was the state with the highest resident population in the United States in 2024, with 39.43 million people. Wyoming had the lowest population with about 590,000 residents. Living the American Dream Ever since the opening of the West in the United States, California has represented the American Dream for both Americans and immigrants to the U.S. The warm weather, appeal of Hollywood and Silicon Valley, as well as cities that stick in the imagination such as San Francisco and Los Angeles, help to encourage people to move to California. Californian demographics California is an extremely diverse state, as no one ethnicity is in the majority. Additionally, it has the highest percentage of foreign-born residents in the United States. By 2040, the population of California is expected to increase by almost 10 million residents, which goes to show that its appeal, both in reality and the imagination, is going nowhere fast.

A dataset listing Washington counties by population for 2024.

A dataset listing Montana cities by population for 2024.

India is the most populous country in the world with one-sixth of the world's population. According to official estimates in 2022, India's population stood at over 1.42 billion.

This dataset contains the population distribution by state, gender, sex & region.

The file is in .csv format thus it is accessible everywhere.

A dataset listing Massachusetts cities by population for 2024.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwellings, households and persons

UNITS IDENTIFIED: - Dwellings: Not available in microdata sample - Vacant units: no - Households: Not available in microdata sample - Individuals: yes - Group quarters: Not available in microdata sample - Special populations: no

UNIT DESCRIPTIONS: - Dwellings: A structurally separate and independent place or building that has been constructed, built, converted, or made available as a permanent or temporary place of lodging. This includes any kind of shelter, fixed or mobile, occupied as a place of lodging at the time of the census. - Households: A private census household is made up of all of the occupants of a private dwelling. It can be made up of one person who is the only occupant of the dwelling. In cases where there is more than one occupant in the dwelling, the private census household is made up of the relatives, guests, renters, and domestic employees of the person considered to be the head of the family, as well as by all other occupants. - Group quarters: A place of lodging for a group of persons who are usually not related and who generally live together for reasons of discipline, health, education, religious life, military training, work, etc. Examples include: reformatories, military bases, jails, hospitals, sanatoriums, nursing homes for the elderly, boarding schools, convents, orphanages, worker?s camps, hotels, hostels, hospices, and other similar places of lodging.

Universe

All persons who spent the night of August 6th to August 7th, 1960 in the dwelling. Usual residents who were absent the night of August 6th to August 7th, 1960 due to work, or due to accidental reasons (a party, wake, etc.) were also enumerated. Foreign diplomats and their families were not enumerated.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Centro Latinoamericano de Demografia (CELADE)

SAMPLE UNIT: Individuals

SAMPLE FRACTION: 6.6%

SAMPLE SIZE (person records): 201,556

Mode of data collection

Face-to-face [f2f]

Research instrument

Single enumeration form that requested information on dwellings, households, and individuals.

Response rate

COVERAGE: 92.2%

Population prediction could provide effective data support for social and economic planning and decision-making, especially for the sub-national population forecasting accurately. In addition to realizing efficient smart population management, this research focuses primarily on the combination model for forecasting demographic data based on machine learning. As to the higher error of population forecasts due to high population density and mobility, a dynamic monitoring method based on mobile communication big data such as mobile phone signals is proposed, combined with more structurally stable traditional statistical data, it forms a multi-source dataset that possesses both accuracy and real-time characteristics. In the study, the Extreme Gradient Boosting tree (XGBoost) model is used to identify the base model to create a reliable predictive model for population dynamic monitoring. The sparrow search algorithm (SSA) is investigated to obtain more reasonable parameters of XGBoost to improve forecast accuracy. The combination model is verified based on the data of the 6th and 7th national population census and mobile phone signal data in Hebei Province, obtained the predicted data for mortality and migration, categorized by age and gender, for the following year. Subsequently, the research compared the performance of different metaheuristic algorithms and various gradient-boosting machine-learning models on the dataset. The SSA-XGBoost model demonstrates a better prediction performance in the demographic data forecast with better R2 0.9984 and a lower mean absolute error of 0.0002 and a mean squared error of 6.9184. The results of the comparative experiments and cross-validation show that the proposed predictive model can effectively forecast the demographic data for sub-national regions to realize smart population management.

In 2025, approximately 23 million people lived in the São Paulo metropolitan area, making it the biggest in Latin America and the Caribbean and the sixth most populated in the world. The homonymous state of São Paulo was also the most populous federal entity in the country. The second place for the region was Mexico City with 22.75 million inhabitants. Brazil's cities Brazil is home to two large metropolises, only counting the population within the city limits, São Paulo had approximately 11.45 million inhabitants, and Rio de Janeiro around 6.21 million inhabitants. It also contains a number of smaller, but well known cities such as Brasília, Salvador, Belo Horizonte and many others, which report between 2 and 3 million inhabitants each. As a result, the country's population is primarily urban, with nearly 88 percent of inhabitants living in cities. Mexico City Mexico City's metropolitan area ranks sevenths in the ranking of most populated cities in the world. Founded over the Aztec city of Tenochtitlan in 1521 after the Spanish conquest as the capital of the Viceroyalty of New Spain, the city still stands as one of the most important in Latin America. Nevertheless, the preeminent economic, political, and cultural position of Mexico City has not prevented the metropolis from suffering the problems affecting the rest of the country, namely, inequality and violence. Only in 2023, the city registered a crime incidence of 52,723 reported cases for every 100,000 inhabitants and around 24 percent of the population lived under the poverty line.

The statistic shows the 30 largest countries in the world by area. Russia is the largest country by far, with a total area of about 17 million square kilometers.

Population of Russia

Despite its large area, Russia - nowadays the largest country in the world - has a relatively small total population. However, its population is still rather large in numbers in comparison to those of other countries. In mid-2014, it was ranked ninth on a list of countries with the largest population, a ranking led by China with a population of over 1.37 billion people. In 2015, the estimated total population of Russia amounted to around 146 million people. The aforementioned low population density in Russia is a result of its vast landmass; in 2014, there were only around 8.78 inhabitants per square kilometer living in the country. Most of the Russian population lives in the nation’s capital and largest city, Moscow: In 2015, over 12 million people lived in the metropolis.

Abstract

ABSTRACT OF ECONOMIC CENSUS IN INDIA

1. GENESIS

A reliable and robust database is the foundation of organized and proper planning. TheCentral Statistics Office (CSO), since its inception, has been instrumental in creation of database forvarious sectors of the economy and its periodic updation so as to meet the requirements of the plannersfor sound and systematic planning both at the macro as well as micro levels. While data requirementsmay be enormous in various sectors, the judicious collection and maintenance of data for varioussectors within the available resource is a challenge. Our economy can broadly be classified into twosectors, namely, Agricultural and Non-Agricultural sectors. Fairly reasonable database exists forAgricultural Sector whereas such data base for Non-Agricultural sector is much desired. Keeping inview the importance of the non-agricultural sector in the economy and non-availability of basic framefor adoption in various sampling techniques for collection of data and estimation of various parameters,conducting Economic Census was felt necessary. With this background, the CSO started EconomicCensus for preparing frame of establishments, particularly the ‘area frame’ which could be used forvarious surveys for collection of detailed data, mainly on non-agricultural sector of the economy.

1. EARLIER ATTEMPTS

Broadly the entire planning period may be divided into two: prior to conduct of the FirstEconomic Census i.e. prior to 1977 and thereafter i.e. after the economic census was carried outperiodically. Efforts to fill up the data gaps for the non-agricultural sector were made right from thebeginning of the First Five Year Plan. The first National Sample Survey (NSS) round (1950-51)covered non-agricultural household establishments as one of its subject themes. Such establishmentswere covered regularly up to the tenth NSS round (1955-56). Subsequently, selected activities weretaken up for survey intermittently in different rounds (14th, 23 rd & 29th rounds). Establishmentschedules were canvassed in 1971 population census. The census of unorganized industrial units wascarried out during 1971 -73. Census of the units falling within the purview of Development Commissioner, Small Scale Industries, was carried out during 1973-74 and a survey on distributivetrade was conducted by some of the States during the Fourth Five-Year Plan period (1969-74). Allsuch efforts made prior to 1977 to collect data on non-agricultural establishments have been partial andsporadic. Area sampling with probability proportional to population were mostly used even to captureestablishments. For a survey of establishments such sample design is not only inefficient but alsoresults in under coverage of desired number of establishments and low reliability of the estimatesderived. The prolonged efforts of statisticians and planners in finding a way out for collection ofinformation on amorphous areas of activity resulted in a decisive breakthrough with the advent ofconduct of Economic Census.

1. ECONOMIC CENSUSES CONDUCTED IN THE PAST

The Economic Enquiry Committee set up in 1925 under the Chairmanship of Dr.Visweswarayya and more importantly the Bowley-Robertson Committee set up later in 1934, were mainly responsible for the government’s decision to set up an Inter-Departmental Committee with theEconomic Adviser to the Government of India as the chairman. The Inter-Departmental Committeerecommended the formation of a Central Statistical Office for coordination, institution of a statisticalcadre, establishment of State Bureaus at State Head Quarters and maintenance of important statisticsfor the entire country. Bowley and Robertson Committee also commissioned a study to explore thepossibility of conducting economic censuses in India. The first coordinated approach was made by theerstwhile Central Statistical Organisation (CSO), Government of India, by launching a plan scheme'Economic Census and Surveys' in 1976. The scheme envisaged organising countrywide census of alleconomic activities (excluding those engaged in crop production and plantation) followed by detailedsample surveys of unorganised segments of different sectors of non-agricultural economy in a phasedmanner during the intervening period of two successive economic censuses.The basic purpose of conducting the economic census (EC) was to prepare a frame for followup surveys intended to collect more detailed sector specific information between two economiccensuses. In view of the rapid changes that occur in the unorganised sectors of non-agriculturaleconomy due to high mobility or morbidity of smaller units and also on account of births of new units,the scheme envisaged conducting the economic census periodically in order to update the frame fromtime to time.

1. First Economic Census (EC -1977) and Follow Up Surveys

The First Economic Census was conducted throughout the country, except Lakshadweep,during 1977 in collaboration with the Directorate of Economics & Statistics (DES) in the States/UnionTerritories (UT). The coverage was restricted to only non-agricultural establishments employing atleast one hired worker on a fairly regular basis. Data on items such as description of activity, number ofpersons usually working, type of ownership, etc. were collected.Reports based on the data of EC-1977 at State/UT level and at all India level were published.Tables giving the activity group-wise distribution of establishments with selected characteristics andwith rural and urban break up were generated. State-wise details for major activities and size-class ofemployment in different establishments, inter-alia, were also presented in tables.Based on the frame provided by the First Economic Census, detailed sample surveys werecarried out during 1978-79 and 1979-80 covering the establishments engaged in manufacturing, trade,hotels & restaurants, transport, storage & warehousing and services. While the smaller establishments(employing less than six workers) and own account establishments were covered by National SampleSurvey Organisation (NSSO) as a part of its 33rd and 34th rounds, the larger establishments were covered through separate surveys by the CSO. Detailed information on employment, emoluments,capital structure, quantity & value of input, output, etc. were collected and reports giving all importantcharacteristics on each of the concerned subjects were published.

1. Second Economic Census (EC-1980) and Follow Up Surveys

The Second Economic Census was conducted in 1980 along with the house-listing operations ofPopulation Census 1981. This was done with a view to economizing resources, manpower, time andmoney. The scope and coverage were enlarged. This time all establishments engaged in economicactivities - both agricultural and non-agricultural whether employing any hired worker or not werecovered, except those engaged in crop production and plantation. All States/UTs were covered withthe sole exception of Assam, where Population Census 1981 was not conducted.The information on location of establishment, description of economic activity carried out,nature of operation, type of ownership, social group of owner, use of power/fuel, total number ofworkers usually engaged with its hired component and break-up of male and female workers werecollected. The items on which information were collected in Second Economic Census were more orless the same as those collected in the First Economic Census. However, based on experience gained inthe First Economic Census certain items viz. years of operation, value of annualoutput/turnover/receipt, mixed activity or not, registered/ licensed/recognised and act or authority, ifregistered were dropped.The field work was done by the field staff consisting of enumerators and supervisors employedin the Directorate of Census Operations of each State/UT. The State Directorates of Economics &Statistics (DES) were also associated in the supervision of fieldwork. Data processing and preparationof State level reports of economic census and their publication were carried out by the DES.Based on the frame thrown up by EC-1980, three follow-up surveys were carried out, one in1983-84 on hotels & restaurants, transport, storage & warehousing and services, second in 1984-85 onunorganised manufacturing and third in 1985- 86 on wholesale and retail trade.The economic census scheduled for 1986 could not be carried out due to resource constraints.However, the EC- 1980 frame was updated during 1987-88 in 64 cities (12 cities having more than 10lakh population and 52 other class-I cities) which had problems of identification of enumerationblocks and changes due to rapid urbanization. On the basis of the updated frame, four follow-upsurveys were conducted during 1988-89, 1989-90, 1990-91 and 1991-92 covering the subjects ofhotels & restaurants and transport, unorganized manufacturing, wholesale & retail trade and medical,educational, cultural & other services respectively.

1. Third Economic Census (EC-1990) and Follow Up Surveys

The Third Economic Census was synchronized with the house listing operations of the Population Census 1991 on the same pattern as EC- 1980. The coverage was similar to that of EC-1980. All States/UTs except Jammu & Kashmir, where Population Census 1991 was not undertaken,were covered.Based on the frame thrown up by EC-1990 four follow up surveys were carried out:(i) Establishment Survey covering sectors of mining & quarrying, storage & warehousingin 1992-93;(ii) Establishment Survey covering sectors of hotels & restaurants and transport in 1993-94;(iii) NSS 51 st round covering directory, non-directory and own account establishments inunregistered manufacturing sector in 1994-95; and(iv) Directory Trade Establishments Survey in 1996-97. NSS 53 rd round covered theresidual part of the unorganised trade sector in 1997.

1. Fourth Economic Census (EC-1998) and Follow up Surveys

Population prediction could provide effective data support for social and economic planning and decision-making, especially for the sub-national population forecasting accurately. In addition to realizing efficient smart population management, this research focuses primarily on the combination model for forecasting demographic data based on machine learning. As to the higher error of population forecasts due to high population density and mobility, a dynamic monitoring method based on mobile communication big data such as mobile phone signals is proposed, combined with more structurally stable traditional statistical data, it forms a multi-source dataset that possesses both accuracy and real-time characteristics. In the study, the Extreme Gradient Boosting tree (XGBoost) model is used to identify the base model to create a reliable predictive model for population dynamic monitoring. The sparrow search algorithm (SSA) is investigated to obtain more reasonable parameters of XGBoost to improve forecast accuracy. The combination model is verified based on the data of the 6th and 7th national population census and mobile phone signal data in Hebei Province, obtained the predicted data for mortality and migration, categorized by age and gender, for the following year. Subsequently, the research compared the performance of different metaheuristic algorithms and various gradient-boosting machine-learning models on the dataset. The SSA-XGBoost model demonstrates a better prediction performance in the demographic data forecast with better R2 0.9984 and a lower mean absolute error of 0.0002 and a mean squared error of 6.9184. The results of the comparative experiments and cross-validation show that the proposed predictive model can effectively forecast the demographic data for sub-national regions to realize smart population management.

Population prediction could provide effective data support for social and economic planning and decision-making, especially for the sub-national population forecasting accurately. In addition to realizing efficient smart population management, this research focuses primarily on the combination model for forecasting demographic data based on machine learning. As to the higher error of population forecasts due to high population density and mobility, a dynamic monitoring method based on mobile communication big data such as mobile phone signals is proposed, combined with more structurally stable traditional statistical data, it forms a multi-source dataset that possesses both accuracy and real-time characteristics. In the study, the Extreme Gradient Boosting tree (XGBoost) model is used to identify the base model to create a reliable predictive model for population dynamic monitoring. The sparrow search algorithm (SSA) is investigated to obtain more reasonable parameters of XGBoost to improve forecast accuracy. The combination model is verified based on the data of the 6th and 7th national population census and mobile phone signal data in Hebei Province, obtained the predicted data for mortality and migration, categorized by age and gender, for the following year. Subsequently, the research compared the performance of different metaheuristic algorithms and various gradient-boosting machine-learning models on the dataset. The SSA-XGBoost model demonstrates a better prediction performance in the demographic data forecast with better R2 0.9984 and a lower mean absolute error of 0.0002 and a mean squared error of 6.9184. The results of the comparative experiments and cross-validation show that the proposed predictive model can effectively forecast the demographic data for sub-national regions to realize smart population management.

Notes from product: II. Notes on China 2000 and 2010 Population Census Data In order to guide you to use the data correctly, provide you some explanations as follows: (l) Census time: 0:00AM of November 1, 2000 and 2010 as the reference time for the census. (2) The 2000 and 2010 population census covered all persons who hold the nationality of, and have permanent residing place in the People's Republic of China. During the census, each person was enumerated in his/her permanent residing place. The following persons should be enumerated in their permanent residing place: a) Those who reside in the townships, towns and street communities and have their permanent household registration there. b) Those who have resided in the townships, towns and street communities for more than 6 months but the places of their permanent household registration are elsewhere. c) Those who have resided in the townships, towns and street communities for less than 6 months but have been away from the place of their permanent household registration for more than 6 months. d) Those who live in the townships, towns and street communities during the population census while the places of their household registration have not yet settled. e) Those who used to live in the townships, towns and street communities but are working or studying abroad during the census and have no Permanent household registration for the time being. (3) Two types of questionnaires (long form and short form) were used for the 2000 and 2010 population census. The short form contains items that reflect the basic situation of the population, while the long form include all short form items plus other items such as migration, education, economic activities, marriage and family, fertility , housing , etc. . According to the National Bureau of Statistics of China, the households for the Long Form survey were selected by a random sampling program. The data included in this product are from 100% Short Form survey.(4) Results in this publication are based on the processing of data directly from enumeration without any adjustment. It is therefore worthwhile to notice the following: a. Data in the publication do not include population not enumerated in the Census. b. Data in the publication do not include the servicemen of the People's Liberation Army. c. The post-enumeration sample survey indicates an undercount of 1.81% in 2000 Census and 0.12% in 2010 Census. III. Notes on the China Province GIS Maps for the 2000 and 2010 Population Census Data (1) The China Province GIS map were developed for the 2000 and 2010 population Census data, which covered all 31 municipalities, provinces and autonomous regions of China, except for Taiwan, Hong Kong and Macao. (2) The population data came from the 5th and 6th China Population Census surveyed in 2000 and 2010. The GIS data is based on the national digital map (1:1 million) developed by the National Geographic Information Center of China (NGCC), including rives, roads, residential area and administrative boundaries.(3) The China province GIS maps are developed for matching 2000 and 2010 China population Census data, which should only be used as references for research or education instead of used as official maps. The distributor is not responsible for the accuracy of the those maps if the maps are used for business or other purposes.

A dataset listing Idaho cities by population for 2024.

A dataset listing Georgia counties by population for 2024.