In 2022, the average resident population density in the Shanghai municipality was 3,905 people per square kilometer. This figure remained largely unchanged in the recent five years.

In 2022, the district of the Shanghai municipality with the highest resident population density was Hongkou district with an average of 29,042 people living on one square kilometer. The average density of the population of the Shanghai municipality in total was 3,905 people per square kilometer that year.

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

In 2022, the estimated population density of China was around 150.42 people per square kilometer. That year, China's population size declined for the first time in decades. Although China is the most populous country in the world, its overall population density is not much higher than the average population density in Asia. Uneven population distribution China is one of the largest countries in terms of land area, and its population density figures vary dramatically from region to region. Overall, the coastal regions in the East and Southeast have the highest population densities, as they belong to the more economically developed regions of the country. These coastal regions also have a higher urbanization rate. On the contrary, the regions in the West are covered with mountain landscapes which are not suitable for the development of big cities. Populous cities in China Several Chinese cities rank among the most populous cities in the world. According to estimates, Beijing and Shanghai will rank among the top ten megacities in the world by 2030. Both cities are also the largest Chinese cities in terms of land area. The previous colonial regions, Macao and Hong Kong, are two of the most densely populated cities in the world.

In 2023, the average population density of Beijing municipality was 1,331 people per square kilometer, slightly less than in the previous year. Beijing municipality includes the city center and the relatively large urban area around the city. The population density in different districts of Beijing municipality varies greatly.

Population: Shanghai: Pudong New data was reported at 5,811.100 Person th in 2023. This records an increase from the previous number of 5,782.000 Person th for 2022. Population: Shanghai: Pudong New data is updated yearly, averaging 5,451.200 Person th from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 6,096.100 Person th in 2021 and a record low of 1,848.000 Person th in 2005. Population: Shanghai: Pudong New data remains active status in CEIC and is reported by Shanghai Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GW: Population: Municipality District.

According to official figures, around 24.87 million permanent residents were living in the administrative area of Shanghai municipality in 2023. This was 115,600 people more than in the previous year.

Population development in Shanghai

During the economic reform and opening-up period, Shanghai’s population more than doubled and reached 24.5 million in 2013. However, the limits of demographic growth in Chinese megacities became increasingly apparent since the beginning of the 21st century. In 2017, the Shanghai municipal government planned to limit Shanghai's population and to keep the population within the 25 million-threshold until 2035. As a result, the total population has remained relatively stable since 2013. Furthermore, inhabitants are unevenly distributed across the city districts, with the central urban areas having population densities of around 20,000 people or more per square kilometer.

Current demographic shifts

Under the conditions of restricted demographic inflows, the effect of population aging becomes increasingly apparent in Shanghai. The city traditionally had a low birth rate compared to other regions in China. In recent years, the number of deaths exceeded the number of births, a development that most probably started in 2020. This development is also reflected in the share of people aged 65 and over which increased steadily in recent times. If migration barriers are not lowered, population decrease in Shanghai is likely in the future.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

As of 2023, about 19.6 percent of the population of Shanghai municipality in China were 65 years and older. This share is expected to grow rapidly in the coming decades. Shanghai is one of the four first tier cities in China, the other three being Beijing, Shenzhen, and Guangzhou.

Correspondence of basic service facility category names and main contents.

Population matching index and population.

By 2035, over 34 million people are projected to call Shanghai home. To reduce this number, the Chinese Government implemented population controls for the city in 2017 which aimed to limit the population living in the administrative area of Shanghai municipality to just around 25 million people in 2035.

Megacity – Shanghai

As China’s cities become increasingly urbanized, the demographic of this megacity has also changed considerably over the years, with more and more Chinese locals and foreigners opting to dwell in Shanghai for work and cultural opportunities. A huge proportion of residents in the city originate from other regions in China. Over 39 percent of the city’s residents are long-term migrants and Shanghai host’s many foreigners and expats.

A global financial hub as well as the largest city by population, Shanghai is located on China’s central coast, making it an ideal location to accommodate the world’s busiest container port. The economic contribution of the city to China is significant - Shanghai’s gross domestic product contribution amounted to almost 4.7 trillion yuan in 2023. Despite recent restrictions to land made available for construction, the value of investment in real estate development in Shanghai has continued to increase. To mitigate the effects of its high population, the city has stated it will intensify environmental protection measures.

Model validation result.

Coverage rate of various basic service facilities.

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.

Abstract: Recreational green spaces are associated with human thriving and well-being. During the COVID-19 pandemic a spotlight has been shed on the importance of these spaces such as recreational green in close proximity to the place of residence. In a novel approach, we apply a multiscale analysis using different density measurements, correlations between density and green space, as well as the influence of architectural form and spatial structures to understand the accessibility of recreational green on the micro-scale of a building block. For this purpose, we use geospatial-data analysis and in-depth neighborhood studies to compare two cities in Asia and Europe revealing different ways of organizing density in the built environment and identifying a derivation of approaches for sustainable development in dense urban regions. The geodatabase includes information on building footprints, derived settlement units and core urban areas for the Berlin-Brandenburg urban region and Qingdao urban region and population grid. Vegetation has been isolated from satellite images using NDVI for both study regions, neighborhood site borders, neighborhood site buildings, neighborhood site vegetation and calculated green densities. The research was done by SpACE Lab at ISU (TU Braunschweig) in collaboration with the College of Urban Planning (CAUP) at Tongji University Shanghai (China).

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

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.

Human disturbances are considered to break reproduction barriers among species. Significant increases in hybridization events have been reported among a large number of taxonomic groups in anthropogenic environments, providing novel insights into species evolution mechanisms and conservation management in the Anthropocene. The Eastern Golden Frog (Pelophylax plancyi) and Black-Spotted Frog (P. nigromaculatus) are two sympatric anuran species with a long history of mitochondrial genome introgression in highly urbanized continental East Asia. However, there is only limited understanding of the pattern of their contemporary hybridization and factors influencing their interspecific relationship under anthropogenic disturbances. Here, interspecific hybridizationbetween P. plancyi and P. nigromaculatus at the population level was investigated in Shanghai. All except two haplotypes obtained from both species in Shanghai were mixed together, and located in the introgression clade, implying multiple ancient mitochondrial introgression events occurred in the populations of our study area. Asymmetric genetic introgression was detected by microsatellite markers, with 0.7% of P. plancyi and 14.6% of P. nigromaculatus identified as contemporary admixed individuals. Consistent with the trend of population density,higher genetic diversity of neutral microsatellite loci was found in the more abundant P. plancyi; however, variation in mitochondrial (Cyt-b) and nuclear (POMC) genes was higher in relatively rare P. nigromaculatus. The population density of P. plancyi and number of water patches within local habitats were significantly positively correlated with both occurrences and proportions of admixed individuals in the populations of P. plancyi and P. nigromaculatus.Considering the prevalent transformation of habitats in urbanized areas, these results imply that a high population density in isolated artificially altered habitats is likely to increase interspecific hybridization. Thus, population monitoring and improvement of landscape connectivity between habitats would be needed to control the intensity ofinterspecific hybridization between P. plancyi and P. nigromaculatus in anthropogenic-disturbed environments.

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