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.

China Population: Age 0 to 4 data was reported at 57.537 Person th in 2023. This records a decrease from the previous number of 62.248 Person th for 2022. China Population: Age 0 to 4 data is updated yearly, averaging 68.380 Person th from Dec 1982 (Median) to 2023, with 36 observations. The data reached an all-time high of 116,438.419 Person th in 1990 and a record low of 57.537 Person th in 2023. China Population: Age 0 to 4 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: Sample Survey: By Age and Sex.

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)

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.

About this dataset

Context

This list ranks the 40 cities in the Blue Earth County, MN by Non-Hispanic Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Non-Hispanic Asian Population: This column displays the rank of cities in the Blue Earth County, MN by their Non-Hispanic Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Non-Hispanic Asian Population: The Non-Hispanic Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Non-Hispanic Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Blue Earth County Non-Hispanic Asian Population: This tells us how much of the entire Blue Earth County, MN Non-Hispanic Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Tibet was excluded from the sample. The excluded areas represent less than 1 percent of the total population of China.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for China is 3500.

Mode of data collection

Mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

The COFI database includes power-generation projects in Belt and Road Initiative (BRI) countries financed by Chinese corporations and banks that reached financial closure from 2000 to 2023. Types of financing include debt and equity investment, with the latter including greenfield foreign direct investments (FDI) and cross-border mergers and acquisitions (M&As). COFI is consolidated using nine source databases using both automated join method in R Studio, and manual joining by analysts. The database includes power plant characteristics data and investment detail data. It captures 575 power plants in 87 BRI countries, including 314 equity investment transactions and 341 debt investment transactions made by Chinese investors. Key data points for financial transactions in COFI include the financial instrument (equity or debt), investor name, amount, and financial close year. Key technical characteristics tracked for projects in COFI include name, installed capacity, commissioning year, country, and primary fuel type. This project is a collaboration among the Boston University Global Development Policy Center, the Inter-American Dialogue, the China-Africa Research Initiative at the Johns Hopkins University (CARI), and the World Resources Institute (WRI). The detailed methodology is given in the World Resources Institute publication “China Overseas Finance Inventory”. Cautions When analyzing debt investment amounts, users should be aware of the difference between loan commitment and actual disbursement. Our database records the loan commitment for a certain year and not actual disbursement. The investment amount should only provide a rough picture of where Chinese companies are investing and not how much their exact portion is. In this version of the database, all equity investment amounts are missing. This is because the equity amount is either missing or estimated in the source databases. Citation

Introduction

Welcome to the Mandarin Chinese General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Mandarin speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Mandarin Chinese communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Mandarin speech models that understand and respond to authentic Chinese accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Mandarin Chinese. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Mandarin speech and language AI applications:

Unemployment Rate in China remained unchanged at 5 percent in June. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Introduction

The Chinese Telecom Chat Dataset is a comprehensive collection of over 10,000 text-based conversations between telecom customers and call center agents. This dataset captures real-world service interactions and domain-specific language in Chinese, enabling the development of intelligent conversational AI and NLP systems tailored for the telecommunications sector.Participant & Chat Overview

Topic Diversity

This dataset spans a wide range of telecom customer service scenarios:

Language Nuance & Realism

The conversations reflect real-life telecom interactions in Chinese, incorporating:

Conversational Flow & Structure

Conversations follow the natural flow of telecom customer service exchanges, including:

Chinese Domestic Databases Market Outlook

The Chinese Domestic Databases market size is set for robust growth, projected to grow from USD 2 billion in 2023 to USD 6.5 billion by 2032, reflecting an impressive CAGR of 13.5%. This growth is driven by the increasing demand for data sovereignty, technological advancements, and regulatory support from the Chinese government. The market is primed for expansion, propelled by factors such as the burgeoning digital economy, increased cloud adoption, and the strategic focus on indigenous technological advancements.

One of the primary growth factors for the Chinese Domestic Databases market is the increasing emphasis on data sovereignty and security. With the Chinese government imposing stringent regulations on data storage and management, domestic companies are compelled to utilize local databases to ensure compliance. This has created a favorable environment for the growth of domestic database providers who are tailored to meet these unique requirements. Additionally, the rise in cyber threats has further driven the need for secure and reliable database solutions, contributing significantly to market growth.

Technological advancements and innovation within the database industry are also pivotal growth drivers. The rapid development of Artificial Intelligence (AI) and Machine Learning (ML) technologies has allowed for more efficient and intelligent database management systems. Innovations in data handling, processing speed, and storage capabilities provide a significant competitive edge to domestic databases over international counterparts. Furthermore, the integration of AI and ML with databases enables advanced analytics and insights, helping businesses make more informed decisions, thus driving the market forward.

The digital transformation across various sectors in China has also fueled the demand for robust database solutions. Sectors such as finance, healthcare, and retail are increasingly relying on digital platforms for their operations, necessitating sophisticated and reliable databases to manage vast amounts of data. The push towards a digital economy by the Chinese government, coupled with initiatives like the "New Infrastructure" program, which focuses on the development of digital infrastructure including big data centers, has significantly boosted the demand for domestic databases.

Regionally, East China dominates the market due to the presence of major economic hubs like Shanghai and Hangzhou, which are home to numerous technology companies and data centers. North China, with Beijing as its central hub, also plays a significant role in the market due to the concentration of governmental bodies and financial institutions that demand secure and compliant database solutions. South China, particularly Shenzhen, is another critical region, given its prominence as a technology and innovation hub. Central China and other regions are gradually catching up as investments in digital infrastructure spread across the country. Overall, the regional dynamics of the Chinese Domestic Databases market present a diverse and rapidly evolving landscape.

Type Analysis

The Chinese Domestic Databases market comprises various types, including Relational Databases, NoSQL Databases, NewSQL Databases, and others. Relational Databases have been the cornerstone of the database industry for decades, offering structured data storage and easy retrieval through SQL queries. Despite their age, they remain highly relevant due to their robustness, reliability, and the vast ecosystems that have developed around them. In China, relational databases continue to be widely adopted across various industries, particularly in sectors like finance and government, where data accuracy and consistency are paramount.

NoSQL Databases have gained significant traction in recent years due to their flexibility, scalability, and ability to handle unstructured data. Unlike traditional relational databases, NoSQL databases can seamlessly manage large volumes of diverse data types, making them ideal for applications in big data and real-time web applications. In China, the adoption of NoSQL databases is particularly prominent in the e-commerce and social media sectors, where the ability to scale out horizontally and handle high-velocity data is crucial.

NewSQL Databases represent a hybrid approach that combines the best features of traditional relational databases and NoSQL databases. They offer the scalability and flexibility of NoSQL while maintaining the ACID (Atomicity, Consistency, Isolation, Durability) prope

Since China’s initiative of the Belt and Road Initiative, overseas industrial parks have become an important carrier for economic and trade cooperation and have become an important force for Chinese enterprises to go global.However, although there are many industrial parks invested by Chinese companies abroad, there is not yet a comprehensive statistical work that is crucial for national or corporate investors.The start-up time of some parks and the name of Chinese enterprises that are under construction are difficult to find, so comprehensive statistical work is relatively difficult.This paper collects data through the network crawling technology, the public number of the Belt and Road International Industrial Park, the official website of the major enterprises participating in the Belt and Road construction, and the database of the Ministry of Commerce.Under the most comprehensive collection possible, a detailed data set of the China Outland Campus Belt and Road Project from 1992 to 2018 was compiled.This data set summarizes the existing park names and determines the total number of parks currently built in China; statistics on the number of parks on each continent to understand the distribution of the park; then analyze the type of the park, and understand the distribution of resources in the area by type; finally,compare the time between the construction of the park and the time of the country where the park is located join the Asian Infrastructure Investment Bank(AIIB) to know the relationship between the AIIB and the park.

Note: Updates to this data product are discontinued. The China agricultural and economic database is a collection of agricultural-related data from official statistical publications of the People's Republic of China. Analysts and policy professionals around the world need information about the rapidly changing Chinese economy, but statistics are often published only in China and sometimes only in Chinese-language publications. This product assembles a wide variety of data items covering agricultural production, inputs, prices, food consumption, output of industrial products relevant to the agricultural sector, and macroeconomic data.

China % of Population with Access to Water: City data was reported at 99.433 % in 2023. This records an increase from the previous number of 99.387 % for 2022. China % of Population with Access to Water: City data is updated yearly, averaging 96.120 % from Dec 1985 (Median) to 2023, with 31 observations. The data reached an all-time high of 99.433 % in 2023 and a record low of 63.900 % in 2000. China % of Population with Access to Water: City 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 Utility Sector – Table CN.RCA: Percentage of Population with Access to Water.

In the past decades, numerous clinical researches have been conducted to illuminate the effects of traditional Chinese medicine for better inheritance and promotion of it, which are mostly clinical trials designed from the doctor's point of view. This large-scale data mining study was conducted from real-world point of view in up to 10 years' big data sets of Traditional Chinese Medicine (TCM) in China, including both medical visits to hospital and cyberspace and contemporaneous social survey data. Finally, some important and interesting findings appear: (1) More Criticisms vs. More Visits. The intensity of criticism increased by 2.33 times over the past 10 years, while the actual number of visits increased by 2.41 times. (2) The people of younger age, highly educated and from economically developed areas have become the primary population for utilizing TCM, which is contrary to common opinions on the characteristics of TCM users. The discovery of this phenomenon indicates that TCM deserves further study on how it treats illness and maintains health.

These specimens are extracted from online database of plant herbaria in China, with more than 60 herbaria. All the data are collected in other countries, not inside China. So, it's a very valuable resource to make a discovery to see the what specimens are stored in China, and more things could be done with these data.

China Population: Average Household Size data was reported at 2.800 Person in 2023. This records an increase from the previous number of 2.760 Person for 2022. China Population: Average Household Size data is updated yearly, averaging 3.150 Person from Dec 1982 (Median) to 2023, with 31 observations. The data reached an all-time high of 4.430 Person in 1982 and a record low of 2.620 Person in 2020. China Population: Average Household Size 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: No of Person per Household.

China Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 11.600 % in 2021. This records a decrease from the previous number of 11.900 % for 2020. China Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 15.100 % from Dec 1990 (Median) to 2021, with 19 observations. The data reached an all-time high of 19.500 % in 2010 and a record low of 8.900 % in 1990. China Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).