In 2023, the average population density of Beijing municipality was ***** 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.

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.

Metropolitan area from resources and environment science and social economic data, including data center, the Beijing municipal emergency administration, China's seismic data set download: 2015 beijing-tianjin-hebei, Yangtze river delta urban agglomeration (flow) of the floating population and large bay area characteristic research data sets, the density of population data (2000-2005-2010-2015-2020), human settlements, 1978-2017 (30 m by 30 m), the seventh in 2020 census data with vector (form), GDP raster data (2019), the data of the construction land expansion in 1978, 1985-2017 (30 m by 30 m), the population birth rate (1 km x 1 km) in 2015, the population spatial distribution of the 2000-2005-2010-2015-2020 (100 m by 100 m), and other social and economic data, statistical yearbook, three large scale urban agglomeration districts and counties, villages and towns social economic statistics and metropolis POI data (20 cities).

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 ...

As China’s political and economic centre, the Beijing–Tianjin–Hebei (BTH) urban agglomeration experiences serious environmental challenges on particulate matter (PM) concentration, which results in fundamental or irreparable damages in various socioeconomic aspects. This study investigates the seasonal and spatial distribution characteristics of PM2.5 concentration in the BTH urban agglomeration and their critical impact factors. Spatial interpolation are used to analyse the real-time monitoring of PM2.5 data in BTH from December 2013 to May 2017, and partial least squares regression is applied to investigate the latest data of potential polluting variables in 2015. Several important findings are obtained: (1) Notable differences exist amongst PM2.5 concentrations in different seasons; January (133.10 mg/m3) and December (120.19 mg/m3) are the most polluted months, whereas July (38.76 mg/m3) and August (41.31 mg/m3) are the least polluted months. PM2.5 concentration shows a periodic U-shaped variation pattern with high pollution levels in autumn and winter and low levels in spring and summer. (2) In terms of spatial distribution characteristics, the most highly polluted areas are located south and east of the BTH urban agglomeration, and PM2.5 concentration is significantly low in the north. (3) Empirical results demonstrate that the deterioration of PM2.5 concentration in 2015 is closely related to a set of critical impact factors, including population density, urbanisation rate, road freight volume, secondary industry gross domestic product, overall energy consumption and industrial pollutants, such as steel production and volume of sulphur dioxide emission, which are ranked in terms of their contributing powers. The findings provide a basis for the causes and conditions of PM2.5 pollution in the BTH regions. Viable policy recommendations are provided for effective air pollution treatment.

Changes in land surface properties during urbanization have a significant impact on variations in precipitation. Little research has been carried out on spatial heterogeneity and influence strength of the driving factors of precipitation changes at different urbanization scales. Using a trend analysis and multi-scale geographically weighted regression, this study analysed the spatial heterogeneity and impact scale of driving factors of precipitation changes in 156 urban units in the Beijing-Tianjin-Hebei urban agglomeration region (Jing-Jin-Ji). In summer, RAD (radiation), RHU (relative humidity), WIN (wind speed), and POP (urban population density) were found to act on a small regional scale, AOD (aerosol optical depth) on a medium regional scale, and NDVI (normalized difference vegetation index), NLI (night time light intensity), UHI (urban heat island intensity), and AREA (urban area size) on a global scale. In winter, AREA and WIN acted on a medium regional scale, UHI on a large regional scale, and AOD, NDVI and NLI on a global scale. Across the whole year, NDVI and AREA had a medium regional impact and NLI a large regional one. Variations in natural factors, such as RAD and RHU, had a great influence on the spatial heterogeneity of precipitation changes, whereas human factors, such as NLI and UHI, had a small influence. In summer, AOD mainly affected Tangshan and Qinhuangdao in the northeast and Cangzhou in the southeast of the Jing-Jin-Ji. RHU and AREA primarily affected the cities of Handan and Xingtai. In winter, NLI, AREA, WIN, and UHI had significant effects in the cities of Handan and Xingtai, with AREA being the most important factor. In the Shijiazhuang-Hengshui area, RAD and NLI played a significant role; in the Beijing-Zhangjiakou-Chengde area, the most important factor affecting precipitation changes was the variation in POP. These results provide a scientific basis for flood disaster risk management in the Jing-Jin-Ji and the establishment of differentiated climate policies in different cities.

As urbanization speeds up, the concept of healthy cities is receiving more focus. This article compares Chongzuo and Nanning in Guangxi with Beijing to assess the development gaps in cities in Guangxi. An indicator system for healthy cities was designed from six dimensions—healthy economy, healthy population, healthy healthcare, healthy environment, healthy facilities, and healthy transportation—and 26 secondary indicators, which were selected from 2005 to 2022, and an improved factor analysis was used to synthesize a healthy city index (HCI). The number of factors was determined by combining characteristic roots and the variance contribution rate, and the HCI was weighted using the entropy-weighted Topsis method. A comprehensive evaluation of the urban health status of these cities was conducted. The results showed that extracting six common factors had the greatest effect, with a cumulative variance contribution rate of 93.83%. Chongzuo city scored higher in the field of healthcare. The healthy environment score of Nanning was relatively high, which may be related to continuous increases in green measures. In terms of the healthy economy dimension, Beijing was far ahead. However, in recent years, the healthy economy level in Chongzuo has increased, and the GDP growth rate has ranked among the highest in Guangxi. In addition, the growth rate of healthy facilities in Nanning was relatively fast and has been greater than that in Chongzuo in recent years, which indicates that the Nanning Municipal Government believes urban construction and municipal supporting facilities are highly important. In terms of healthy transportation, Chongzuo and Nanning scored higher than Beijing. This may be because the transportation in these two cities is convenient and the traffic density is more balanced than that in Beijing, thereby reducing traffic congestion. Chongzuo had the highest score for a healthy population, and a steadily growing population provides the city with stable human resources, which helps promote urban economic and social development. Finally, relevant policy recommendations were put forwards to enhance the health level of the cities.

The global secondary water supply services market size was valued at USD 113.42 billion in 2021 and is projected to reach USD 192.62 billion by 2033, exhibiting a CAGR of 5.8% during the forecast period. Secondary water supply services refer to the delivery of treated wastewater or stormwater for non-potable purposes, such as irrigation, industrial cooling, and vehicle washing. The increasing demand for water conservation and the growing scarcity of freshwater resources are the primary drivers fueling market growth. The market is segmented based on type into household water supply, maintenance of secondary water supply facilities, and others. The household water supply segment held the largest market share in 2021, owing to the rising demand for alternative water sources for non-potable applications in residential areas. In terms of application, the first-tier city segment dominated the market, driven by the rapid urbanization and the growing population density in major cities. The increasing emphasis on sustainable building practices and government initiatives are expected to create significant growth opportunities in this segment. Key players operating in the market include Veolia Environnement, SUEZ Environnement, Beijing Enterprises Water Group, and Beijing Capital Eco-environment Protection.