Context

Some friends at kaggle said them would like to see more dataset about China. I'm just uncertain what kind of data do you want. So I simply collect a population dataset as a beginning. I'm very glad that our international friends can know more about the real China, my great motherland from these datasets.

Content

注：1981年及以前人口数据为户籍统计数；1982、1990、2000、2010、2020年数据为当年人口普查数据推算数；其余年份数据为年度人口抽样调查推算数据。总人口和按性别分人口中包括现役军人，按城乡分人口中现役军人计入城镇人口。 数据来源：国家统计局

Note: Population data in 1981 and before are household registration statistics; The data of 1982, 1990, 2000, 2010 and 2020 are calculated from the census data of the same year. Data for the remaining years were derived from annual population sampling surveys. Active servicemen are included in the total population and population by sex, and active servicemen are included in the urban population by urban and rural population. Source: National Bureau of Statistics.

Tips: what you should notice is that all the numbers with the counting unit (10 thousand) or we said in Chinese ‘万’, as a very usual counting unit rather than 'thousand' in English.

Inspiration

By this dataset, you can see the progress of China's urbanization.

Time series data for the statistic Rural land area (sq. km) and country China. Indicator Definition:Rural land area in square kilometers, derived from urban extent grids which distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.

Projections of future income distributions at subnational levels are becoming increasingly important for a variety of analyses and evaluations. However, relevant datasets are currently limited. This study presents a methodological framework that introduces machine learning algorithms to a top-down approach used for generating income distribution datasets. We project per capita disposable income and income inequality for 31 Chinese provinces from 2020 to 2100, considering different scenarios based on China’s local circumstances, and then estimate income distributions based on these. After accounting for necessary consistency between provincial, urban, and rural income datasets, we further generate the same data products at the urban and rural level for each province. We validate our projection results drawing on data from 2007-2023 for China’s disposable income, data from 2007 to 2019 for provincial income inequality in China, as well as national income inequality data for the past 20 to 60 years from select developed countries. The proposed methodology provides flexibility to generate similar data products according to a user’s specific needs. Our resulting datasets have several potential applications and can serve as inputs for research on drivers and impacts across social, economic, and environmental domains.

This dataset is derived from the article: Huang, M., Wang, Z.C., Pan, X.H., Gong, B.H., Tu, M.Z., & Liu, Z.F. (2022). Delimiting China's urban growth boundaries under localized shared socioeconomic pathways and various urban expansion modes. Earth's Future, 10, e2021EF002572. The dataset shows the urban expansion and urban growth boundaries of China in 2021-2100 under different socioeconomic scenarios and diverse urban expansion modes. To produce this dataset, the patch-based LUSD-urban model was used to simulate the urban expansion with 11 modes under the localized shared socioeconomic pathways, and the morphology approach was used to delimit urban growth boundaries according to the maximum extent of urban expansion. Using this dataset, the authors quantified the impacts of future urban expansion on ecosystem services under different scenarios and diverse modes, as well as the pressure of urban shrinkage, which is helpful to the Chinese government to demarcate urban development boundaries.

This dataset derives from the articles: (1) He, C., Liu, Z., Tian, J., & Ma, Q., (2014). Urban expansion dynamics and natural habitat loss in China: a multiscale landscape perspective. Global change biology, 20(9), 2886-2902.（2）Xu, M., He, C., Liu, Z., Dou, Y. (2016). How Did Urban Land Expand in China between 1992 and 2015? A Multi-Scale Landscape Analysis. PLoS ONE 11 (5): e0154839. To produce this dataset, the nighttime light data, vegetation index data, and land surface temperature data were preprocessed to obtain the multi-source remote sensing data in China from 1992 to 2020, and the economic regionalization, selection of samples, support vector machine classification, and inter-annual correction were used to extract the dynamic information of urban built-up area. According to the accuracy assessment based on Landsat TM/ETM+ data, Kappa coefficient is 0.60, overall accuracy is 92.62% This dataset has been used to assess the impacts of urban expansion on natural habitats and cropland, and can provide data support for understanding China’s urban expansion and its effects.

IntroductionThe Healthy China Initiative emphasizes family health. Education is an upstream determinant of health, which can both achieve upward mobility and cause class solidification.MethodsUsing nationwide large-scale data collected in 2021, the present study explored the relationship between education and family health in the urban-rural dual society via Oaxaca-Blinder decomposition and propensity score matching.ResultsOur data revealed disparities in family health, educational attainment, household income, healthcare coverage, and job type between urban and rural China. An inverted U-shaped relationship existed between increasing years of education and family health. The upper limit was 17.1 years for urban residents and 13.7 years for rural residents, with limited health benefits from higher education obtained by rural residents. Mediated by work-family conflict, highly-educated people received gradually diminishing health returns. The results of the Oaxaca-Blinder decomposition showed that 25.8% of the urban-rural gap in family health could be explained by the disparity in education. Urban residents could translate cultural capital and economic capital into health capital to a greater extent. After propensity score matching, a robust, inverted U-shaped relationship was found between education and family health. The inverted U-shaped relationship was found to replace family health with self-rated health and quality of life.DiscussionFamily-centered public health and education programs, policies, and goals should be developed to break urban-rural dual structure barriers and advance social equity in China.

Although digital inclusion is globally recognized as a crucial pathway to equitable development, rigorous empirical evidence on its specific mechanisms and regionally varying effects in driving rural-urban integration within developing countries remains scarce. To address this gap, this study adopts fixed-effects models and robustness checks to rigorously analyze the impact of digital inclusion on rural-urban integration, drawing on evidence from China. The findings demonstrate that digital inclusion enhances rural-urban integration by mitigating information asymmetry and improving access to educational resources, healthcare, and industrial convergence. However, there is a significant positive effect in eastern and central China, but an insignificant effect in the western region. These results challenge the conventional assumption of uniformly positive impacts from digital policies and underscore the necessity for spatially differentiated interventions. By integrating mechanism analysis with regional disparity diagnostics, this research deepens the theoretical understanding of digital inclusion's societal effects and provides crucial empirical support and policy insights for developing countries seeking to formulate regionally differentiated digital inclusion strategies.

Income disparity, spatial inequality, and urban-rural inequality are three fundamental issues affecting human social equality. The Chinese government considers addressing these three inequalities as crucial for achieving common prosperity. Therefore, in response to these inequalities, the income Gini coefficient, the population-weighted CV of nighttime light, and the urban-rural income ratio were integrated to construct a social equality index (SEI). This index reflects the overall degree of social inequality in China.The specific calculation method can be found in the related paper.

The China County-Level Data on Population (Census) and Agriculture, Keyed To 1:1M GIS Map consists of census, agricultural economic, and boundary data for the administrative regions of China for 1990. The census data includes urban and rural residency, age and sex distribution, educational attainment, illiteracy, marital status, childbirth, mortality, immigration (since 1985), industrial/economic activity, occupation, and ethnicity. The agricultural economic data encompasses rural population, labor force, forestry, livestock and fishery, commodities, equipment, utilities, irrigation, and output value. The boundary data are at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of California-Davis China in Time and Space (CITAS) project, and the Center for International Earth Science Information Network (CIESIN).

The purpose of this project was to measure and estimate the distribution of personal income and related economic factors in both rural and urban areas of the People's Republic of China. The principal investigators based their definition of income on cash payments and on a broad range of additional components. Data were collected through a series of questionnaire-based interviews conducted in rural and urban areas at the end of 2002. There are ten separate datasets. The first four datasets were derived from the urban questionnaire. The first contains data about individuals living in urban areas. The second contains data about urban households. The third contains individual-level economic variables copied from the initial urban interview form. The fourth contains household-level economic variables copied from the initial urban interview form. The fifth dataset contains village-level data, which was obtained by interviewing village leaders. The sixth contains data about individuals living in rural areas. The seventh contains data about rural households, as well as most of the data from a social network questionnaire which was presented to rural households. The eighth contains the rest of the data from the social network questionnaire and is specifically about the activities of rural school-age children. The ninth dataset contains data about individuals who have migrated from rural to urban areas, and the tenth dataset contains data about rural-urban migrant households. Dataset 1 contains 151 variables and 20,632 cases (individual urban household members). Dataset 2 contains 88 variables and 6,835 cases (urban households). Dataset 3 contains 44 variables and 27,818 cases, at least 6,835 of which are empty cases used to separate households in the file. The remaining cases from dataset 3 match those in dataset 1. Dataset 4 contains 212 variables and 6,835 cases, which match those in dataset 2. Dataset 5 contains 259 variables and 961 cases (villages). Dataset 6 contains 84 variables and 37,969 cases (individual rural household members). Dataset 7 contains 449 variables and 9,200 cases (rural households). Dataset 8 contains 38 variables and 8,121 cases (individual school-age children). Dataset 9 contains 76 variables and 5,327 cases (individual rural-urban migrant household members). Dataset 10 contains 129 variables and 2,000 cases (rural-urban migrant households). The Chinese Household Income Project collected data in 1988, 1995, 2002, and 2007. ICPSR holds data from the first three collections, and information about these can be found on the series description page. Data collected in 2007 are available through the China Institute for Income Distribution.

China has the longest terrestrial boundary and the highest number of neighboring countries in the world. At present, there is a lack of research on human activities at the grid scale in China's border areas. Using the land use/cover data, population density data, and night-time light data, we constructed the human activity intensity index (HAI). Taking the 50km buffer zones of China’s land boundary on each side as the study area, we calculated the HAI with a resolution of 1km in 1992, 2000, 2010, and 2020. This dataset is stored as the tif format and composed of four layers with a volume of 469 MB (compressed into one file, 4.37 MB).

China Area of Urban Zone data was reported at 2,371,372.710 sq km in 2022. This records an increase from the previous number of 2,368,544.490 sq km for 2021. China Area of Urban Zone data is updated yearly, averaging 1,986,564.870 sq km from Dec 2006 (Median) to 2022, with 17 observations. The data reached an all-time high of 2,371,372.710 sq km in 2022 and a record low of 1,899,921.300 sq km in 2010. China Area of Urban Zone data remains active status in CEIC and is reported by Ministry of Housing and Urban-Rural Development. The data is categorized under China Premium Database’s Land and Resources – Table CN.NLL: Urban Area.

Based on an investigation of urban forests across local transects from urban central areas to rural areas in Beijing and a regional transect from north to south in eastern China, we estabished a database on leaf and topsoil (0-10cm) Hg concentrations. The data are used for the study entitled "Geographical patterns of leaf and topsoil mercury in China’s urban forests" (Earth's Future).

The China GSS is an annual or biannual questionnaire survey of China's urban and rural households aiming to monitor systematically the changing relationship between social structure and quality of life in urban and rural China. The objectives of the China GSS are: (1) to gather longitudinal data on social trends; (2) to address issues of theoretical and practical significance; and (3) to serve as a global resource for the international scholarly community. Includes: labour force activity, demographic variables, household size and composition, ethnicity of R and parents, mobility, dwelling, income, expenditures and facilities, education, military service, etc. 1 data file (1,000 logical records) & accompanying documentation (5 pdf files) in both English and Chinese characters.

Leading with the principle of ‘people-oriented urbanization,’ the adaptation of rural migrants in urban China has attracted increasing concerns from policy-makers and scholars. Today, China has proceeded to a new stage of urbanization. Many rural migrants prefer moving to cities near their home villages rather than to large cities, reflecting the changes in migration patterns and expectations of rural migrants. Although migrant adaptation has been repeatedly investigated in academia, researchers tend to address the topic in one host setting, while migrant adaptation in diverse urban settings has rarely been compared. This paper seeks to fill this research gap via a survey conducted in two cities with different urban settings in Jiangsu. The rural migrant adaptation experiences in the two cities are systematically compared. Our statistical results show that economic structure and living costs, on the one hand, and local regulations and socio-cultural environments, on the other hand, determine rural migrant adaptation experiences in different urban settings. Despite abundant employment opportunities in more-developed cities, the high living costs, working pressure, and strict institutional schemes significantly hamper rural migrant adaptation. In less-developed cities, limited employment opportunities and conservative socio-cultural environments hinder rural migrants from adapting in host societies. Our findings suggest that the governments of different cities need to tailor strategies to assist rural migrants in adapting in urban communities.

Characteristics of urban and rural residence (in millions) in Zhejiang, China, 2012.

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.

Hong Kong HK: Rural Land Area data was reported at 231.171 sq km in 2010. This stayed constant from the previous number of 231.171 sq km for 2000. Hong Kong HK: Rural Land Area data is updated yearly, averaging 231.171 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 231.171 sq km in 2010 and a record low of 231.171 sq km in 2010. Hong Kong HK: Rural Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong SAR – Table HK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Rural land area in square kilometers, derived from urban extent grids which distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

Rural socio-economic data and spatial data of Chongqing Western region

This dataset is about countries per year in China. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, self-employed workers, and urban population.