In 2024, about 943.5 million people lived in urban regions in China and 464.8 million in rural. That year, the country had a total population of approximately 1.41 billion people. As of 2024, China was the second most populous country in the world. Urbanization in China Urbanization refers to the process by which people move from rural to urban areas and how a society adapts to the population shift. It is usually seen as a driving force in economic growth, accompanied by industrialization, modernization and the spread of education. Urbanization levels tend to be higher in industrial countries, whereas the degree of urbanization in developing countries remains relatively low. According to World Bank, a mere 19.4 percent of the Chinese population had been living in urban areas in 1980. Since then, China’s urban population has skyrocketed. By 2024, about 67 percent of the Chinese population lived in urban areas. Regional urbanization rates In the last decades, urbanization has progressed greatly in every region of China. Even in most of the more remote Chinese provinces, the urbanization rate surpassed 50 percent in recent years. However, the most urbanized areas are still to be found in the coastal eastern and southern regions of China. The population of Shanghai, the largest city in China and the world’s seventh largest city ranged at around 24 million people in 2023. China’s urban areas are characterized by a developing middle class. Per capita disposable income of Chinese urban households has more than doubled between 2010 and 2020. The emerging middle class is expected to become a significant driver for the continuing growth of the Chinese economy.

In 2024, the average annual per capita disposable income of rural households in China was approximately ****** yuan, roughly ** percent of the income of urban households. Although living standards in China’s rural areas have improved significantly over the past 20 years, the income gap between rural and urban households is still large. Income increase of China’s households From 2000 to 2020, disposable income per capita in China increased by around *** percent. The fast-growing economy has inevitably led to the rapid income increase. Furthermore, inflation has been maintained at a lower rate in recent years compared to other countries. While the number of millionaires in China has increased, many of its population are still living in humble conditions. Consequently, the significant wealth gap between China’s rich and poor has become a social problem across the country. However, in recent years rural areas have been catching up and disposable income has been growing faster than in the cities. This development is also reflected in the Gini coefficient for China, which has decreased since 2008. Urbanization in China The urban population in China surpassed its rural population for the first time in 2011. In fact, the share of the population residing in urban areas is continuing to increase. This is not surprising considering remote, rural areas are among the poorest areas in China. Currently, poverty alleviation has been prioritized by the Chinese government. The measures that the government has taken are related to relocation and job placement. With the transformation and expansion of cities to accommodate the influx of city dwellers, neighboring rural areas are required for the development of infrastructure. Accordingly, land acquisition by the government has resulted in monetary gain by some rural households.

In 2024, approximately 67 percent of the total population in China lived in cities. The urbanization rate has increased steadily in China over the last decades. Degree of urbanization in China Urbanization is generally defined as a process of people migrating from rural to urban areas, during which towns and cities are formed and increase in size. Even though urbanization is not exclusively a modern phenomenon, industrialization and modernization did accelerate its progress. As shown in the statistic at hand, the degree of urbanization of China, the world's second-largest economy, rose from 36 percent in 2000 to around 51 percent in 2011. That year, the urban population surpassed the number of rural residents for the first time in the country's history.The urbanization rate varies greatly in different parts of China. While urbanization is lesser advanced in western or central China, in most coastal regions in eastern China more than two-thirds of the population lives already in cities. Among the ten largest Chinese cities in 2021, six were located in coastal regions in East and South China. Urbanization in international comparison Brazil and Russia, two other BRIC countries, display a much higher degree of urbanization than China. On the other hand, in India, the country with the worlds’ largest population, a mere 36.3 percent of the population lived in urban regions as of 2023. Similar to other parts of the world, the progress of urbanization in China is closely linked to modernization. From 2000 to 2024, the contribution of agriculture to the gross domestic product in China shrank from 14.7 percent to 6.8 percent. Even more evident was the decrease of workforce in agriculture.

The purpose of this project was to measure and estimate the distribution of personal income 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: payments in kind valued at market prices, agricultural output produced for self-consumption valued at market prices, the value of food and other direct subsidies, and the imputed value of housing services. The rural component of this collection consists of two data files, one in which the individual is the unit of analysis (Part 1) and a second in which the household is the unit of analysis (Part 2). Individual rural respondents reported on their employment status, level of education, Communist Party membership, type of employer (e.g., public, private, or foreign), type of economic sector in which they were employed, occupation, whether they held a second job, retirement status, monthly pension, monthly wage, and other sources of income. Demographic variables include relationship to householder, gender, age, and student status. Rural households reported extensively on the character of the household and residence. Information was elicited on type of terrain surrounding the house, geographic position, type of house, and availability of electricity. Also reported were sources of household income (e.g., farming, industry, government, rents, and interest), taxes paid, value of farm, total amount and type of cultivated land, financial assets and debts, quantity and value of various crops, amount of grain purchased or provided by a collective, use of chemical fertilizers, gasoline, and oil, quantity and value of agricultural machinery, and all household expenditures (e.g., food, fuel, medicine, education, transportation, and electricity). The urban component of this collection also consists of two data files, one in which the individual is the unit of analysis (Part 3) and a second in which the household is the unit of analysis (Part 4). Individual urban respondents reported on their economic status within the household, Communist Party membership, sex, age, nature of employment, and relationship to the household head. Information was collected on all types and sources of income from each member of the household whether working, nonworking, or retired, all revenue received by owners of private or individual enterprises, and all in-kind payments (e.g., food, durable goods, and nondurable goods). Urban households reported total income (including salaries, interest on savings and bonds, dividends, rent, leases, alimony, gifts, and boarding fees), all types and values of food subsidies received, and total debt. Information was also gathered on household accommodations and living conditions, including number of rooms, total living area in square meters, availability and cost of running water, sanitary facilities, heating and air-conditioning equipment, kitchen availability, location of residence, ownership of home, and availability of electricity and telephone. Households reported on all their expenditures including amounts spent on food items such as wheat, rice, edible oils, pork, beef and mutton, poultry, fish and seafood, sugar, and vegetables by means of coupons in state-owned stores and at free market prices. Information was also collected on rents paid by the households, fuel available, type of transportation used, and availability and use of medical and child care. 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.

Along with the rapid development of the global digital economy, China is experiencing profound transformations in industrial digitization. These transformations may significantly affect the urban-rural income gap. Using panel data from 30 Chinese provinces from 2012 to 2022, this paper empirically examined the impact of industrial digitalization on the urban-rural income gap based on a fixed-effects model. The findings reveal that the development of industrial digitalization in China widens the urban-rural income gap. Mechanism analysis indicates that industrial digitalization increases software business revenue and employment in the information services sector, thereby expanding the urban-rural income gap; additionally, industrial digitalization widens the income gap between urban migrants and rural migrant populations, further increasing the overall urban-rural income disparity. Heterogeneity analysis demonstrates that in the eastern region, industrial digitalization significantly enlarges the urban-rural income gap, whereas its effects are not significant in the central and western regions. The conclusions of this study provide empirical support and policy insights for China in advancing industrial digitalization and promoting common prosperity.

This dataset contains information from a population-based survey, which investigated human exposure to live poultry, and population psychological response and behavioral changes of the community members during two waves of influenza A(H7N9) epidemics in Southern China in 2013-2014. The dataset including 3 files. * One file named "population_wt.csv" contained population information of the studied sites; * One file named "H7N9 survey China_Que stionarie_eng.doc" was the survey questionaire; * The third file named "dataset_H7N9.csv" contained datasets acquired during the two waves of A(H7N9) epidemics,a data frame with 1657 observations on the following 44 variables. Survey ##a numeric vector: where the subject live## 1= the first wave () 2= the second wave () Place ##a numeric vector: where the subject live## 5=Guangzhou 10=Zijin County, Heyuan City SG3 ##a numeric vector: the gender of the subject## 1=Female 2=Male SG4_b ##a numeric vector: the age group of the subject, unit=years## 1=18-24 2=25-34 3=35-44 4=45-54 5=55-64 6=65+ SG6 ##a numeric vector: the marital status of the subject## 1=Single 2=Married 3=Divorced /separated 4=Widowed 5=Refuse to answer SG8 ##a numeric vector: the educational attainment of the subject## 1=Illiteracy 2=Primary school 3=Middle school 4=High school 5=College and above SG12 ##a numeric vector: the average income of the subject, unit=Chinese Yuan## 1=Less than l,000 2=1,001—2,000 3=2,001—3,000 4=3,001—4,000 5=4,001—6,000 6=6,001—8,000 7=8,001—10,000 8=10,001—2,000 9=15,001—20,000 10=20,001—30,000 11=More than 30,001 12=No income 13=Don’t know 14=Refuse to answer AX1_a ##a numeric vector: the anxiety level of the subject, I feel rested ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_b ##a numeric vector: the anxiety level of the subject, I feel content ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_c ##a numeric vector: the anxiety level of the subject, I feel comfortable ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_d ##a numeric vector: the anxiety level of the subject, I am relaxed ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_e ##a numeric vector: the anxiety level of the subject, I feel pleasant ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_f ##a numeric vector: the anxiety level of the subject, I feel anxious ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_g ##a numeric vector: the anxiety level of the subject, I feel nervous ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_h ##a numeric vector: the anxiety level of the subject, I am jittery ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_i ##a numeric vector: the anxiety level of the subject, I feel “high strung” ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_j ##a numeric vector: the anxiety level of the subject, I feel over-excited and “rattled” ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So BF4b##a numeric vector indicating the subject's rate of worriness towards H7N9 avian flu, 1 being very mild to 10 being very severe## EM1 ##a numeric vector: How often did you go to wet markets in the past year ## 1=1-2/year 2=3-5/year 3=6-11/year 4=1-3/month 5=1-2/week 6=3-5/week 7=Almost every day 8=Almost not EM2 ##a numeric vector: How often did you buy poultry in wet markets in the past year ## 1=1-2/year 2=3-5/year 3=6-11/year 4=1-3/month 5=1-2/week 6=3-5/week 7=Almost every day 8=Almost not EM3 ##a numeric vector: Did you usually pick up the poultry for examination before deciding to buy it ## 1=Yes 2=No 3=Sometime “yes”, sometime “no” EM4 ##a numeric vector: Where was the live poultry slaughtered when you bought it? ## 1=Always in wet market 2=Usually in wet market 3=Usually in my household 4=Always in my household 5=Other places EM5 ##a numeric vector: Have your habit of buying live poultry changed since the first human H7N9 case was released in the past month ## 1=Yes, not buying since then 2=No, still buying and eating live poultry 3=Still buying but less than before EM6 ##a numeric vector: Would you support permanent closure of live poultry markets in order to control avian influenza epidemics ## 1=Strongly agree 2=Agree 3=Not agree 4=Strongly disagree 5=Don’t know EM8 ##a numeric vector: Have your raised live poultry in your backyard in the past year ## 1=Yes 2=No BF1 ##a numeric vector indicating risk perception of the subject: How likely do you think it is that you will contract H7N9 avian flu over the next 1 month ## 1=Never 2=Very unlikely 3=Unlikely 4=Evens 5=Likely 6=Very likely 7=Certain BF2a ##a numeric vector indicating risk perception of the subject: What do you think are your chances of getting H7N9 avian flu over the next 1 month compared to other people outside your family of a similar age ## 1=Not at all 2=Much less 3=Less 4=Evens 5=More 6=Much more 7=Certain BF3_l ##a numeric vector indicating knowledge of the subject: H7N9 avian flu is spread by the body contact with patients ## 1=Yes 2=No 3=Don’t Know BF3_m ##a numeric vector indicating knowledge of the subject: H7N9 avian flu is spread by touching objects that have been contaminated by the virus ## 1=Yes 2=No 3=Don’t Know BF3_n ##a numeric vector indicating knowledge of the subject: H7N9 avian flu is spread by the close contact with chickens in a wet market ## 1=Yes 2=No 3=Don’t Know BF4 ##a numeric vector: If you were to develop flu-like symptoms tomorrow, would you be... ## 1=Not at all worried 2=Much less worried than normal 3=Worried less than normal 4=About same 5=Worried more than normal 6=Worried much more than normal 7=Extremely worried BF4a ##a numeric vector indicating risk perception of the subject: In the past one week, have you ever worried about catching H7N9 avian flu ## 1=No, never think about it 2=Think about it but it doesn’t worry me 3=Worries me a bit 4=Worries me a lot 5=Worry about it all the time BF5a ##a numeric vector indicating risk perception of the subject: How does H7N9 avian flu compare with seasonal flu in terms of seriousness ## 1=Much higher 2=A little higher 3=Same 4=A little lower 5=Much lower 6=Don’t Know BF5b ##a numeric vector indicating risk perception of the subject: How does H7N9 avian flu compare with H5N1 avian flu in terms of seriousness ## 1=Much higher 2=A little higher 3=Same 4=A little lower 5=Much lower 6=Don’t Know BF5c ##a numeric vector indicating risk perception of the subject: How does H7N9 avian flu compare with SARS in terms of seriousness ## 1=Much higher 2=A little higher 3=Same 4=A little lower 5=Much lower 6=Don’t Know BF7 ##a numeric vector evaluating the current performance of the national government in controlling H7N9 avian flu, (0=extremely poor, 5=moderate, 10=excellent) ## BF7a ##a numeric vector evaluating the current performance of the provincial/city government in controlling H7N9 avian flu, (0=extremely poor, 5=moderate, 10=excellent) ## PM2 ##a numeric vector indicating the preventive behavior of the subject, covering the mouth when sneeze or cough ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know 6=Not applicable (no sneeze or cough) PM3 ##a numeric vector indicating the preventive behavior of the subject, washing hands after sneezing, coughing or touching nose ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know 6=Not applicable (no sneeze or cough) PM3a ##a numeric vector indicating the preventive behavior of the subject,washing hands after returning home ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know 6=Not applicable (never go out) PM4 ##a numeric vector indicating the preventive behavior of the subject,using liquid soap when washing hands ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know PM5 ##a numeric vector indicating the preventive behavior of the subject,wearing face mask ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know PM7 ##a numeric vector:If free H7N9 flu vaccine is available in the coming month, would you consider receiving it ## 1=Yes 2=No 3=Not sure 4=Don’t know ############################ THE END ##########################

Demographic information of study samples from rural and urban areas (N = number).

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Geographic coverage

Shandong province

Kind of data

Sample survey data [ssd]

Sampling procedure

Shandong province was selected for the survey. The respondents were selected from urban and rural areas respectively, using the household registry system which provides information on names, age, gender, education and address for each household member.

In rural areas, two counties, one high-income and the other low-income, were selected. 4,000 respondents were selected from these two counties.

In the urban area, Jinan City was selected. 1,000 respondents were selected from six districts within this city.

Sample sizes in rural and urban areas were estimated according to the present rural/urban population ratio in Shandong.

The individual selected from the household was 18+ years in age and the closest birthday method was used to select the respondent.

Sample size=2,480

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

In 2024, about 60.9 percent of the Chinese population was between 16 and 59 years old. Apart from the information given on broad age groups in this statistic, some more information is provided by a timeline for the age distribution and a population breakdown by smaller age groups. Demographic development in China China ranked as the second most populous country in the world with a population of nearly 1.41 billion as of mid 2024, surpassed only by India. As the world population reached more than eight billion in mid 2024, China represented almost one fifth of the global population. China's population increased exponentially between the 1950s and the early 1980s due to Mao Zedong's population policy. To tackle the problem of overpopulation, a one-child policy was implemented in 1979. Since then, China's population growth has slowed from more than two percent per annum in the 1970s to around 0.5 percent per annum in the 2000s, and finally turned negative in 2022. China's aging population One outcome of the strict population policy is the acceleration of demographic aging trends. According to the United Nations, China's population median age has more than doubled over the last five decades, from 18 years in 1970 to 37.5 years in 2020. Few countries have aged faster than China. The dramatic aging of the population is matched by slower growth. The total fertility rate, measuring the number of children a woman can expect to have in her life, stood at just around 1.2 children. This incremental decline in labor force could lead to future challenges for the Chinese government, causing instability in current health care and social insurance mechanisms. To learn more about demographic development of the rural and urban population in China, please take a look at our reports on population in China and aging population in China.

This analysis is based on a balanced panel dataset of 283 prefecture level cities in China from 2010 to 2020. The list of cities designated as SC pilot cities comes from official announcements released by the Ministry of Housing and Urban Rural Development (MOHURD) in three batches (2012, 2013, and 2014). The year in which a city is included in the pilot list is defined as the policy implementation year. The data of mechanism variables is collected from specialized sources. The data on information transmission efficiency comes from the government informationization evaluation report of the Ministry of Industry and Information Technology. Departmental coordination efficiency data is manually collected from the local municipal government's public document database. The data of mechanism variables is collected from specialized sources. The data on information transmission efficiency comes from the government informationization evaluation report of the Ministry of Industry and Information Technology. Departmental coordination efficiency data is manually collected from the local municipal government's public document database. To ensure data quality, the raw data undergoes strict cleaning. Cities with severe data loss (such as municipalities directly under the central government) are excluded. Extreme values of disaster response time (exceeding 72 hours) are considered missing data to avoid the impact of outliers. All continuous variables are randomly sorted at the 1st and 99th percentiles to mitigate the impact of extreme values.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 6.1163 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.6539 and 0.0775 (in million kms), corressponding to 10.6909% and 1.2663% respectively of the total road length in the dataset region. 5.385 million km or 88.0428% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.012 million km of information (corressponding to 0.2231% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

The overall level of digital literacy and skills of internet users in China continued to improve. In 2025, six in every ten internet users in China possessed at least basic knowledge and skills to conduct online searches and verify information. Fact-checking skills have become increasingly essential in the digital age to combat against fake news or misinformation.

China is home to the largest online community in the world. According to estimates, the Chinese internet population was around **** billion in 2022 and would reach **** billion by 2026. By comparison, the internet user-base of the United States was around *** million in 2022. The country’s internet penetration rate is around ** percent, indicating that almost one out of three Chinese are still offline.

Mobile-first kingdom

Affordable smartphones play an important role in China’s rising internet population. In 2022, almost all Chinese people accessed the internet through mobile phones. Messaging, watching videos, and listening to music are popular activities. In fact, Chinese people spent over three hours of their daily time online. This large and engaged mobile internet population has provided a wide range of opportunities to hi-tech companies in China, including online dating and matchmaking business.

What’s next?

As of June 2022, around ** percent of online users were living in rural China. To provide more affordable and faster internet services, measures have been carried out in the country to reduce service rates and extend the 1,000-Megabyte broadband connection to over *** cities. In 2019, the government announced to roll out 5G networks in Beijing, Shanghai, Shenzhen, and other major cities. The technology advancement would likely enhance internet accessibility in rural areas, further increasing the country’s internet user base.

Shanghai Jingwei Information Co Limited 803 Leshan Village Jinhu Town Fengxian District Shanghai China Import Export Turnover 0 and 0.01 USD Million during December 2020 to November 2021. Also check supply chain analytics, top import and export commodities with price, buyers, suppliers, main competitors of Shanghai Jingwei Information Co Limited 803 Leshan Village Jinhu Town Fengxian District Shanghai China in .

The growth of the digital economy has created new forms of inequality of opportunity. This paper studies whether the development of the digital economy expands the income gap between urban and rural areas from theoretical and empirical. The research based on the panel data of 202 cities from 2011 to 2019 in China shows that: (1) Although the digital economy can promote the improvement of both urban and rural absolute income levels, it has a greater positive impact on urban residents’ income levels than on rural residents’, resulting in a widening of the urban-rural income gap. (2) The analysis of the action mechanism reveals that employment in the information service industry and the depth of digital finance use are two crucial mechanisms for the digital economy to widen the income gap between urban and rural areas. (3) The spatial Durbin model(SDM) and the spatial error model(SEM) based on three spatial weight matrices show that the impact of the digital economy on the urban-rural income gap is also characterized by spatial spillover, and the development of the digital economy will also have a negative impact on the urban-rural income gap in neighboring regions as well. (4) The main conclusions still hold after the robustness of quasi-natural experiments based on the strategy of "Broadband China" and the selection of historical data as instrumental variables. This research is helpful to understand the effects, mechanisms and spatial characteristics of digital economy on urban-rural income gap.

China Internet Service: Internet Penetration Rate: Rural data was reported at 65.600 % in Dec 2024. This records an increase from the previous number of 63.800 % for Jun 2024. China Internet Service: Internet Penetration Rate: Rural data is updated semiannually, averaging 35.950 % from Dec 2007 (Median) to Dec 2024, with 28 observations. The data reached an all-time high of 66.500 % in Dec 2023 and a record low of 7.400 % in Dec 2007. China Internet Service: Internet Penetration Rate: Rural data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Internet Market Size. Affected by the COVID-19, the data cut-off time for the 2019 is March 2020.

A direct examination of absolute income levels of urban and rural residents.

Hausman test result.

The growth of the digital economy has created new forms of inequality of opportunity. This paper studies whether the development of the digital economy expands the income gap between urban and rural areas from theoretical and empirical. The research based on the panel data of 202 cities from 2011 to 2019 in China shows that: (1) Although the digital economy can promote the improvement of both urban and rural absolute income levels, it has a greater positive impact on urban residents’ income levels than on rural residents’, resulting in a widening of the urban-rural income gap. (2) The analysis of the action mechanism reveals that employment in the information service industry and the depth of digital finance use are two crucial mechanisms for the digital economy to widen the income gap between urban and rural areas. (3) The spatial Durbin model(SDM) and the spatial error model(SEM) based on three spatial weight matrices show that the impact of the digital economy on the urban-rural income gap is also characterized by spatial spillover, and the development of the digital economy will also have a negative impact on the urban-rural income gap in neighboring regions as well. (4) The main conclusions still hold after the robustness of quasi-natural experiments based on the strategy of "Broadband China" and the selection of historical data as instrumental variables. This research is helpful to understand the effects, mechanisms and spatial characteristics of digital economy on urban-rural income gap.

In the context of global aging, promoting the health of the elderly has become a critical issue. However, whether the development of smart cities can impact the health of older adults remains to be further validated. In this paper, based on panel data from the China Health and Retirement Longitudinal Study (CHARLS), a difference in difference model is used to empirically investigate whether smart city construction improves the health of older people in the region. The results show that smart city construction enhances the health of the elderly. Specifically, the construction achieved a significant improvement in the physical health of the elderly who did not live with their children. The health promotion effect of the smart city was more significant for the urban elderly than for the rural elderly. The elucidated mechanisms of influence suggest that smart cities bring about their effects through the promotion of urban leisure infrastructure, enhancement of medical service provision, advancement in urban environmental protection and stimulation of urban information and communication technology infrastructure development.