In 2023, approximately 127.1 million people lived in Guangdong province in China. That same year, only about 3.65 million people lived in the sparsely populated highlands of Tibet. Regional differences in China China is the world’s most populous country, with an exceptional economic growth momentum. The country can be roughly divided into three regions: Western, Eastern, and Central China. Western China covers the most remote regions from the sea. It also has the highest proportion of minority population and the lowest levels of economic output. Eastern China, on the other hand, enjoys a high level of economic development and international corporations. Central China lags behind in comparison to the booming coastal regions. In order to accelerate the economic development of Western and Central Chinese regions, the PRC government has ramped up several incentive plans such as ‘Rise of Central China’ and ‘China Western Development’. Economic power of different provinces When observed individually, some provinces could stand an international comparison. Jiangxi province, for example, a medium-sized Chinese province, had a population size comparable to Argentina or Spain in 2023. That year, the GDP of Zhejiang, an eastern coastal province, even exceeded the economic output of the Netherlands. In terms of per capita annual income, the municipality of Shanghai reached a level close to that of the Czech Republik. Nevertheless, as shown by the Gini Index, China’s economic spur leaves millions of people in dust. Among the various kinds of economic inequality in China, regional or the so-called coast-inland disparity is one of the most significant. Posing as evidence for the rather large income gap in China, the poorest province Heilongjiang had a per capita income similar to that of Sri Lanka that year.

China is a vast and diverse country and population density in different regions varies greatly. In 2023, the estimated population density of the administrative area of Shanghai municipality reached about 3,922 inhabitants per square kilometer, whereas statistically only around three people were living on one square kilometer in Tibet. Population distribution in China China's population is unevenly distributed across the country: while most people are living in the southeastern half of the country, the northwestern half – which includes the provinces and autonomous regions of Tibet, Xinjiang, Qinghai, Gansu, and Inner Mongolia – is only sparsely populated. Even the inhabitants of a single province might be unequally distributed within its borders. This is significantly influenced by the geography of each region, and is especially the case in the Guangdong, Fujian, or Sichuan provinces due to their mountain ranges. The Chinese provinces with the largest absolute population size are Guangdong in the south, Shandong in the east and Henan in Central China. Urbanization and city population Urbanization is one of the main factors which have been reshaping China over the last four decades. However, when comparing the size of cities and urban population density, one has to bear in mind that data often refers to the administrative area of cities or urban units, which might be much larger than the contiguous built-up area of that city. The administrative area of Beijing municipality, for example, includes large rural districts, where only around 200 inhabitants are living per square kilometer on average, while roughly 20,000 residents per square kilometer are living in the two central city districts. This is the main reason for the huge difference in population density between the four Chinese municipalities Beijing, Tianjin, Shanghai, and Chongqing shown in many population statistics.

In 2023, the ratio of urban to rural population varied greatly in different provinces of China. While Guangdong province had an urban population of around 95.8 million and a rural population of 31.2 million, Tibet had an urban population of only 1.4 million, but a rural population of around 2.2 million.

Motivation

The data has been collected for COVID-19 analysis. You can use the data for getting the population of each province.

Data has been collected from: http://population.city/ manually.

In 2023, the natural growth rate of the population across China varied between 7.96 people per 1,000 inhabitants (per mille) in Tibet and -6.92 per mille in Heilongjiang province. The national total population growth rate turned negative in 2022 and ranged at -1.48 per mille in 2023. Regional disparities in population growth The natural growth rate is the difference between the birth rate and the death rate of a certain region. In China, natural population growth reached the highest values in the western regions of the country. These areas have a younger population and higher fertility rates. Although the natural growth rate does not include the direct effects of migration, migrants are often young people in their reproductive years, and their movement may therefore indirectly affect the birth rates of their home and host region. This is one of the reasons why Guangdong province, which received a lot of immigrants over the last decades, has a comparatively high population growth rate. At the same time, Jilin, Liaoning, and Heilongjiang province, all located in northeast China, suffer not only from low fertility, but also from emigration of young people searching for better jobs elsewhere. The impact of uneven population growth The current distribution of natural population growth rates across China is most likely to remain in the near future, while overall population decline is expected to accelerate. Regions with less favorable economic opportunities will lose their inhabitants faster. The western regions with their high fertility rates, however, have only small total populations, which limits their effect on China’s overall population size.

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.

Data and Code for Prediction of the COVID-19 Epidemic Trends Based on SEIR and AI Models.Data include the number of confirmed cases of COVID-19, local population density, capital GDP, distance to Wuhan, average annual temperature, average annual rainfall of Chinese provinces (Except for Hong Kong, Macao and Taiwan) and migration population in Wuhan. Code include SEIR, DNN, RNN for prediction.

Population density population distribution is the main form and the main indicators to measure regional differences in population distribution. Town (Street) at the grass-roots administrative system in China, is China's smallest administrative unit of the public release of census data, population density in the township-level data can be objective and precise characterization of the spatial pattern of population distribution and trends in China, and for research on resources, environment and population issues is of great significance. Paper standardized processing has China Liaoning, and Jilin, and in Inner Mongolia (part area), and Beijing, and Tianjin, and Shanghai, and Hebei, and Henan, and Shaanxi, and Ningxia, and Shanxi, and Shandong, and Anhui, and Jiangsu, and Hunan, and Hubei, and Jiangxi, and Zhejiang, and Fujian, and Guangdong, and Hainan, and Yunnan, and Guizhou, and Qinghai, and Tibet, 25 a province (municipalities, and autonomous regions) Township (Street) level administrative line data and the fifth times census Township (Street) level population statistics data, guarantee Township border county (district) Territories consistent, and Spatial and census information for each township unit corresponds to one by one. On this basis, accurately matching the spatial extent of each township and census information, calculated the average population density of communes, form the data set.

This polygon shapefile represents provincial boundaries of China for 2010. This layer is part of the 2010 China Province Population Census Dataset. These data were primarily based on the "The Administrative Maps of the People's Republic of China, published by China Map Press.

Population: This dataset contains 65-years’ time serial data of whole China (unit: million persons), each provinces (unit: 10000 persons), and each county. The source data are originally collected from China Statistical Yearbook from 1949 to 2013. The county data covers 2000, 2006, 2007, and 2009. In addition, 4 years (1995, 2000, 2005, 2010) population distributions cover the whole land region in China are also included in this dataset. Such data is expressed as raster format with 1 km resolution and a projection of Albers. Attribute information mainly includes population density (unit: number of person per square kilometer). The source data are originally provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn) and Data Sharing Infrastructure of Earth System Science (http://www.geodata.cn).These data are not intended for demarcation.

The age structure of the population in China varies greatly across different regions. In 2023, only around 9.6 percent of the population in Shanghai municipality was aged 14 years or younger, while this share amounted to 24.4 percent in Tibet.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets

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

The GIS map layer include the province map with comparable variables from 2000 and 2010 population Census data.

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.

Social pull-push factors mainly fall into six categories: food, traffic, education, technology, health and medical conditions and human living conditions. Indicators of total grain product (Million tons), number of health agencies (units), number of beds in health care agencies (1000 beds), length of railways (10000 km), length of highways (10000 km), length of navigable inland waterways (10000 km), number of regular primary schools (units), number of higher education institutions (units), number of patent applications (units), per capita annual income of urban households (yuan), per capita annual income of rural households (yuan), Engel's coefficient of urban households (-), Engel's coefficient of rural households(-).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.

Annual CV values of 31 provinces (municipalities/autonomous regions) in Mainland China.

Here we used remote sensing data from multiple sources (time-series of Landsat and Sentinel images) to map the impervious surface area (ISA) at five-year intervals from 1990 to 2015, and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more, which were listed in the United Nations (UN) World Urbanization Prospects (WUP) database (including Mainland China, Hong Kong, Macao and Taiwan). We employed a range of spectral indices to generate the 1990–2015 ISA maps in urban areas based on remotely sensed data acquired from multiple sources. In this process, various types of auxiliary data were used to create the desired products for urban areas through manual segmentation of peri-urban and rural areas together with reference to several freely available products of urban extent derived from ISA data using automated urban–rural segmentation methods. After that, following the well-established rules adopted by the UN, we carried out the conversion to the standardized built-up area products from the 1990–2015 ISA maps in urban areas, which conformed to the definition of urban agglomeration area (UAA). Finally, we implemented data postprocessing to guarantee the spatial accuracy and temporal consistency of the final product.The standardized urban built-up area dataset (SUBAD–China) introduced here is the first product using the same definition of UAA adopted by the WUP database for 433 county and higher-level cities in China. The comparisons made with contemporary data produced by the National Bureau of Statistics of China, the World Bank and UN-habitat indicate that our results have a high spatial accuracy and good temporal consistency and thus can be used to characterize the process of urban expansion in China.The SUBAD–China contains 2,598 vector files in shapefile format containing data for all China's cities listed in the WUP database that have different urban sizes and income levels with populations over 300,000. Attached with it, we also provided the distribution of validation points for the 1990–2010 ISA products of these 433 Chinese cities in shapefile format and the confusion matrices between classified data and reference data during different time periods as a Microsoft Excel Open XML Spreadsheet (XLSX) file.Furthermore, The standardized built-up area products for such cities will be consistently updated and refined to ensure the quality of their spatiotemporal coverage and accuracy. The production of this dataset together with the usage of population counts derived from the WUP database will close some of the data gaps in the calculation of SDG11.3.1 and benefit other downstream applications relevant to a combined analysis of the spatial and socio-economic domains in urban areas.

V1 dataset:Under the global framework of Shared Socioeconomic Pathways (SSPs), based on localized population and economic parameters, a Population Development Environment (PDE) model is adopted to construct population grid data for SSPs from 2020 to 2100; Using the Cobb Douglas model, construct economic data for SSPs from 2020 to 2100.The v1 dataset includes:Population grid data of the world, The Belt and Road region, and China, with a spatial resolution of 0.5°GDP grid data of the world, The Belt and Road region, and China, with a spatial resolution of 0.5 °Grid data on the output value of three industries in the Chinese region, with a spatial resolution of 0.1 °V2 dataset:Based on the data from the 7th National Population Census of China, starting from 2020, the parameters such as fertility rate, mortality rate, migration rate, and education level in the Population Development Environment (PDE) model were updated. Under the Shared Socioeconomic Pathways (SSP1-5), a new version (v2) of the total population and age and gender specific population projection dataset for China and its provinces from 2020 to 2100 was created. Based on the data from the 7th National Population Census and the 4th Economic Census of China, with 2020 as the starting year, the parameters of total factor productivity, capital stock, labor input, and capital elasticity coefficient in the Cobb Douglas model were updated. Under the shared SSP1-5, a new version (v2) of China and its provincial GDP projectiondataset from 2020 to 2100 was created.The v2 (2024 version) dataset includes:Total Population Data of China and Provinces (2020-2100)Population data by age and gender in China (2020-2100)China and Provincial GDP Data (2020-2100)

This point shapefile represents the provincial capitals, with 2000 population census data, for the Hebei Sheng province of China for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.

China administrative level 0-2 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE: COD-AB source is unofficial. COD-AB reflects Chinese and UN recognition of 'Taiwan' as a Chinese ADM1 feature.

This COD-AB was most recently reviewed for accuracy and necessary changes in October 2024. The COD-AB does not require any update.

Sourced from publicly available online sources

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

There is no suitable population statistics dataset (COD-PS) for linkage to this COD-AB..

No edge-matched (COD-EM) version of this COD-AB has yet been prepared.

Please see the COD Portal.

Administrative level 1 contains 34 feature(s). The normal administrative level 1 feature type is ""currently not known"".

Administrative level 2 contains 361 feature(s). The normal administrative level 2 feature type is ""currently not known"".

"China administrative level 0-2 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE: COD-AB source is unofficial. COD-AB reflects Chinese and UN recognition of 'Taiwan' as a Chinese ADM1 feature.

This COD-AB was most recently reviewed for accuracy and necessary changes in October 2024. The COD-AB does not require any update.

Sourced from publicly available online sources

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

There is no suitable population statistics dataset (COD-PS) for linkage to this COD-AB..

No edge-matched (COD-EM) version of this COD-AB has yet been prepared.

Please see the COD Portal.

Administrative level 1 contains 34 feature(s). The normal administrative level 1 feature type is 'currently not known'.

Administrative level 2 contains 361 feature(s). The normal administrative level 2 feature type is 'currently not known'.

Recommended cartographic projection: Asia South Albers Equal Area Conic

This metadata was last updated on January 18, 2025.