According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 17 percent of the overall global population in 2024. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

The total population in China was estimated at 1409.7 million people in 2023, according to the latest census figures and projections from Trading Economics. This dataset provides - China Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

As of 2023, the bulk of the Chinese population was aged between 25 and 59 years, amounting to around half of the population. A breakdown of the population by broad age groups reveals that around 61.3 percent of the total population was in working age between 16 and 59 years in 2023. Age cohorts below 25 years were considerably smaller, although there was a slight growth trend in recent years. Population development in China Population development in China over the past decades has been strongly influenced by political and economic factors. After a time of high fertility rates during the Maoist regime, China introduced birth-control measures in the 1970s, including the so-called one-child policy. The fertility rate dropped accordingly from around six children per woman in the 1960s to below two at the end of the 20th century. At the same time, life expectancy increased consistently. In the face of a rapidly aging society, the government gradually lifted the one-child policy after 2012, finally arriving at a three-child policy in 2021. However, like in most other developed countries nowadays, people in China are reluctant to have more than one or two children due to high costs of living and education, as well as changed social norms and private values. China’s top-heavy age pyramid The above-mentioned developments are clearly reflected in the Chinese age pyramid. The age cohorts between 30 and 39 years are the last two larger age cohorts. The cohorts between 15 and 24, which now enter childbearing age, are decisively smaller, which will have a negative effect on the number of births in the coming decade. When looking at a gender distribution of the population pyramid, a considerable gender gap among the younger age cohorts becomes visible, leaving even less room for growth in birth figures.

About this dataset

Context

The dataset tabulates the data for the China, TX population pyramid, which represents the China population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for China, TX, is 25.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for China, TX, is 39.1.
• Total dependency ratio for China, TX is 64.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for China, TX is 2.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the China population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the China for the selected age group is shown in the following column.
• Population (Female): The female population in the China for the selected age group is shown in the following column.
• Total Population: The total population of the China for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the China, Maine population pyramid, which represents the China town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for China, Maine, is 26.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for China, Maine, is 21.7.
• Total dependency ratio for China, Maine is 48.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for China, Maine is 4.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the China town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the China town for the selected age group is shown in the following column.
• Population (Female): The female population in the China town for the selected age group is shown in the following column.
• Total Population: The total population of the China town for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China town Population by Age. You can refer the same here

Introduction: Gridded population datasets are instrumental for modeling the interactions between human and the environment at fine spatial scales. Here, we introduce an Age-Stratified Population Estimation from the 2020 China Census by Township (ASPECT), estimating total population and population by age groups (0-14, 15-59, 60-64, ≥65 years old) at 100m spatial resolution as of year 2020. We used a dasymetric mapping approach to downscale township-level population data (n=40,718 townships) from the 2020 China Census to 100 by 100 m grid cells. The study area is mainland China.Data records: ASPECT includes GEOTIFF files on population density (persons per hectare) at 100 m spatial resolution. No data areas indicate townships missing the 2020 Census data (n=590) and areas fall outside our study area, mainland China. The files are:population_total_pop: gridded estimates on total populationpopulation_age0_14: gridded estimates on population between 0 and 14 years oldpopulation_age15_59: gridded estimates on population between 15 and 59 years oldpopulation_age60_64: gridded estimates on population between 60 and 64 years oldpopulation_age65above: gridded estimates on population ≥65 years oldpopulation_total_pop_sum: gridded estimates on total population, summed with age-group specific estimates (file 2-5). This file is used to calculate the proportion of each age group.ASPECT also includes GEOTIFF files on the proportion (%) of population by age groups at 100 m spatial resolution. No data areas indicate townships missing the 2020 Census data, places fell outside our study area, and places with zero population. Proportion of a population age group is calculated by dividing its population counts by the sum of population from all age groups (i.e., grid cell values from the “population_total_pop_sum.zip” file). The files are:proportion_age0_14: the proportion of population between 0 and 14 years oldproportion_age15_59: the proportion of population between 15 and 59 years oldproportion_age60_64: the proportion of population between 60 and 64 years oldproportion_age65above: the proportion of population ≥65 years oldAll GEOTIFF files are projected with WGS 1984 geographic coordinate system (EPSG: 4326). The files are compressed in zip format. Once uncompressed, the GEOTIFF files can processed by GIS software such as ArcGIS, and by programing language packages such as Rasterio in Python.Reference: 100-m resolution Age-Stratified Population Estimation from the 2020 China Census by Township (ASPECT), published by Scientific Data [link]Citation: Ju, Y., Liang, Y., Kong, J., Wang, X., Wen, S., Shang, H., & Wang, X. (2025). 100-m resolution Age-Stratified Population Estimation from the 2020 China Census by Township (ASPECT). Scientific Data, 12(1), 1058. https://doi.org/10.1038/s41597-025-05401-1

In 1800, the population of the territory that makes up present-day Thailand was approximately 4.7 million people. As part of the kingdom of Siam, the population of Thailand would grow gradually through the 19 th century, with much of the population growth being driven by Chinese emigration from southern Qing China into Siam, in search of work and refuge from instability in their home country. This migrant influx would continue throughout the century, with estimates suggesting that the Chinese population in Siam grew from 230,000 in 1825, to over 792,000 in 1910; by 1932, over 12 percent of the population in modern-day Thailand was ethnically Chinese. Migration from China would see another surge under the reign of Vajiravudh, as the "Warlord era" in China, after the fall of the Qing dynasty, would see entire families of Chinese immigrants arriving in Thailand. While immigration would slow in later years, Chinese-Thai would remain a significant demographic in Thailand’s population, both as one of the largest overseas Chinese populations, and accounting for an estimated 11-14 percent of the total Thailand population in 2012.

Population growth would slow somewhat in the 1930s, as several rebellions and coups, paired with a rise in anti-Chinese sentiment in the country, would result in a sharp decline in immigration to the country. In the years following the Second World War, the population of Thailand would begin to grow rapidly, following a wave of urbanization and a significant increase in standard of living throughout the country. As a result, the population of Thailand would rise from approximately 20 million in 1950, to just under 63 million by the turn of the century just 50 years later. This population growth would slow somewhat as the country would continue to modernize in the 2000s, and in 2020, it is estimated that just under 70 million people live in Thailand.

A 100-m gridded population dataset of China’s seventh census in 2020 was generated by stacking ensemble learning and geospatial big data. The county-level and town-level data of China’s seventh census and ten related covariates at the 100-m resolution were first collected as the input datasets. Three popular machine learning algorithms (i.e., random forest, XGBoost, and LightGBM) were chosen as base models to create and train the stacking ensemble learning to generate gridded population dataset for China.The estimated gridded population dataset (R2=0.8936) is more accurate than existing WorldPop (R2=0.7427) and LandScan (R2=0.7165) products assessed by the town-level test census data. The dataset is associated with the paper of "Yuehong Chen, Congcong Xu, Yong Ge, Xiaoxiang Zhang and Ya'nan Zhou. A 100-m gridded population dataset of China’s seventh census using ensemble learning and big geospatial data, 2024" published in the journal of Earth System Science Data.

About this dataset

Context

The dataset tabulates the data for the China Grove, TX population pyramid, which represents the China Grove population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for China Grove, TX, is 18.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for China Grove, TX, is 31.7.
• Total dependency ratio for China Grove, TX is 50.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for China Grove, TX is 3.2.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the China Grove population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the China Grove for the selected age group is shown in the following column.
• Population (Female): The female population in the China Grove for the selected age group is shown in the following column.
• Total Population: The total population of the China Grove for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China Grove Population by Age. You can refer the same here

China Population Census: Sex Ratio(Female=100) data was reported at 105.070 NA in 12-01-2020. This records a decrease from the previous number of 105.200 NA for 12-01-2010. China Population Census: Sex Ratio(Female=100) data is updated decadal, averaging 106.300 NA from Dec 1953 (Median) to 12-01-2020, with 7 observations. The data reached an all-time high of 107.560 NA in 12-01-1953 and a record low of 105.070 NA in 12-01-2020. China Population Census: Sex Ratio(Female=100) data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: National Population Census.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.


Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country. They can also be visualised and explored through the woprVision App.
The remaining datasets in the links below are produced using the "top-down" method, with either the unconstrained or constrained top-down disaggregation method used. Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively):

- Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019)
-Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019).
-Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets.
-Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020.
-Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national population estimates (UN 2019).

Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

The economic factors present in this dataset include data items of gross domestic product (GDP) (100 million), per-capita GDP (yuan/people), primary industry (100 million), secondary industry (100 million), tertiary industry (100 million) and total investment in fixed assets (100 million). Time serial data from 1949 to 2013 of whole China and all the provinces are included. All of data were collected from the China Statistical Yearbook from 1981 to 2014 and China Compendium of Statistics from 1949 to 2008.These data are not intended for demarcation.

Tuberculosis (TB) incidence has been in steady decline in China over the last few decades. However, ongoing demographic transition, fueled by aging, and massive internal migration could have important implications for TB control in the future. We collated data on TB notification, demography, and drug resistance between 2004 and 2017 across seven cities in Shandong, the second most populous province in China. Using these data, and age-period-cohort models, we (i) quantified heterogeneities in TB incidence across cities, by age, sex, resident status, and occupation and (ii) projected future trends in TB incidence, including drug-resistant TB (DR-TB). Between 2006 and 2017, we observed (i) substantial variability in the rates of annual change in TB incidence across cities, from -4.84 to 1.52%; (ii) heterogeneities in the increments in the proportion of patients over 60 among reported TB cases differs from 2 to 13%, and from 0 to 17% for women; (iii) huge differences across cities in the annual growths in TB notification rates among migrant population between 2007 and 2017, from 2.81 cases per 100K migrants per year in Jinan to 22.11 cases per 100K migrants per year in Liaocheng, with drastically increasing burden of TB cases from farmers; and (iv) moderate and stable increase in the notification rates of DR-TB in the province. All of these trends were projected to continue over the next decade, increasing heterogeneities in TB incidence across cities and between populations. To sustain declines in TB incidence and to prevent an increase in Multiple DR-TB (MDR-TB) in the future in China, future TB control strategies may (i) need to be tailored to local demography, (ii) prioritize key populations, such as elderly and internal migrants, and (iii) enhance DR-TB surveillance.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

A description of the modelling methods used for age and gender structures can be found in "https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-11-11" target="_blank"> Tatem et al and Pezzulo et al. Details of the input population count datasets used can be found here, and age/gender structure proportion datasets here.
Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020.
The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping.
Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646

Access to electricity, rural (% of rural population) in China was reported at 100 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Access to electricity, rural (% of rural population) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

A 100 m grid population dataset of Shanghai was generated from 2010 to 2050 using the Spatial-Leslie Model. For individuals aged 65 and above, the relative root mean square error (%RMSE) is 0.31%. In the validation of projected population distributions for the period 2015-2050, the dataset achieved correlation coefficients exceeding 0.73 across all age groups, with the highest predictive accuracy observed in the 15-64 age group (R2 = 0.83). This dataset aims to provide scientific and technical support for aging- related research by delivering high-precision population distribution data, thereby offering a robust basis for informed decision-making to address future challenges associated with population aging.

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.