Population: Number of Household: Beijing data was reported at 9.158 Unit th in 2023. This records an increase from the previous number of 9.019 Unit th for 2022. Population: Number of Household: Beijing data is updated yearly, averaging 6.781 Unit th from Dec 1982 (Median) to 2023, with 36 observations. The data reached an all-time high of 9,137.928 Unit th in 2020 and a record low of 3.936 Unit th in 1999. Population: Number of Household: Beijing 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: No of Household.

According to the monitoring data from the Embassy of the United States, there was on average 39 micrograms of PM2.5 particles per cubic meter to be found in the air in Beijing during 2023. The air quality has improved considerably since 2013.

Reasons for air pollution in Beijing

China’s capital city Beijing is one of the most populous cities in China with over 20 million inhabitants. Over the past 20 years, Beijing’s GDP has increased tenfold. With the significant growth of vehicles and energy consumption in the country, Beijing’s air quality is under great pressure from the economic development. In the past, the city had a high level of coal consumption. Especially in winter, in which coal consumption increased due to heating, the air quality could get extremely bad on the days without wind. In spring, the wind from the north would bring sand from Mongolian deserts, resulting in severe sandstorms in Beijing. The bad air quality also affected the air visibility and threatened people’s health. On days with very bad air quality, people wearing masks for protection can be seen on the streets in the city.

Methods to improve air quality in Beijing

Over the past years, the government has implemented various methods to improve the air quality in Northern China. Sandstorms, which were quite common 15 years ago, are now rarely seen in Beijing’s spring thanks to afforestation projects on China’s northern borders. The license-plate lottery system was introduced in Beijing to restrict the growth of private vehicles. Large trucks were not allowed to enter certain areas in Beijing. Above all, the coal consumption in Beijing has been restricted by shutting down industrial sites and improving heating systems. Beijing’s efforts to improve air quality has also been highly praised by the UN as a successful model for other cities. However, there is also criticism pointing out that the improvement of Beijing’s air quality is based on the sacrifice of surrounding provinces (including Hebei), as many factories were moved from Beijing to other regions. Besides air pollution, there are other environmental problems like water pollution that China is facing. The industrial transformation is the key to China’s environmental improvement.

CHCP Overview:The human behavior and brain are shaped by genetic, environmental and cultural interactions. Recent advances in neuroimaging integrate multimodal imaging data from a large population and start to explore the large-scale structural and functional connectomic architectures of the human brain. One of the major pioneers is the Human Connectome Project (HCP) that developed sophisticated imaging protocols and has built a collection of high-quality multimodal neuroimaging, behavioral and genetic data from US population. A large-scale neuroimaging project parallel to the HCP, but with a focus on the East Asian population, will allow comparisons of brain-behavior associations across different ethnicities and cultures. The Chinese Human Connectome Project (CHCP) is launched in 2017 and led by Professor Jia-Hong GAO at Peking University, Beijing, China. CHCP aims to provide large sets of multimodal neuroimaging, behavioral and genetic data on the Chinese population that are comparable to the data of the HCP. The CHCP protocols were almost identical to those of the HCP, including the procedure for 3T MRI scanning, the data acquisition parameters, and the task paradigms for functional brain imaging. The CHCP also collected behavioral and genetic data that were compatible with the HCP dataset. The first public release of the CHCP dataset is in 2022. CHCP dataset includes high-resolution structural MR images (T1W and T2W), resting-state fMRI (rfMRI), task fMRI (tfMRI), and high angular resolution diffusion MR images (dMRI) of the human brain as well as behavioral data based on Chinese population. The unprocessed "raw" images of CHCP dataset (about 1.85 TB) have been released on the platform and can be downloaded. Considering our current cloud-storage service, sharing full preprocessed images (up to 70 TB) requires further construction. We will be actively cooperating with researchers who contact us for academic request, offering case-by-case solution to access the preprocessed data in a timely manner, such as by mailing hard disks or a third-party trusted cloud-storage service. V2 Release (Date: January 16, 2023):Here, we released the seven major domains task fMRI EVs files, including: 1) visual, motion, somatosensory, and motor systems; 2) category specific representations; 3) working memory/cognitive control systems; 4) language processing (semantic and phonological processing); 5) social cognition (Theory of Mind); 6) relational processing; and 7) emotion processing.V3 Release (Date: January 12, 2024):This version of data release primarily discloses the CHCP raw MRI dataset that underwent “HCP minimal preprocessing pipeline”, located in CHCP_ScienceDB_preproc folder (about 6.90 TB). In this folder, preprocessed MRI data includes T1W, T2W, rfMRI, tfMRI, and dMRI modalities for all young adulthood participants, as well as partial results for middle-aged and older adulthood participants in the CHCP dataset. Following the data sharing strategy of the HCP, we have eliminated some redundant preprocessed data, resulting in a final total size of the preprocessed CHCP dataset is about 6.90 TB in zip files. V4 Release (Date: December 4, 2024):In this update, we have fixed the issue with the corrupted compressed file of preprocessed data for subject 3011, and removed the incorrect preprocessed results for subject 3090. Additionally, we have updated the subject file information list. Additionally, this release includes the update of unprocessed "raw" images of the CHCP dataset in CHCP_ScienceDB_unpreproc folder (about 1.85 TB), addressing the previously insufficient anonymization of T1W and T2W modalities data for some older adulthood participants in versions V1 and V2. For more detailed information, please refer to the data descriptions in versions V1 and V2.CHCP Summary:Subjects:366 healthy adults (Chinese Han)Imaging Scanner:3T MR (Siemens Prisma)Institution:Peking University, Beijing, ChinaFunding Agencies:Beijing Municipal Science & Technology CommissionChinese Institute for Brain Research (Beijing)National Natural Science Foundation of ChinaMinistry of Science and Technology of China CHCP Citations:Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from CHCP data should contain the following wording in the acknowledgments section: "Data were provided [in part] by the Chinese Human Connectome Project (CHCP, PI: Jia-Hong Gao) funded by the Beijing Municipal Science & Technology Commission, Chinese Institute for Brain Research (Beijing), National Natural Science Foundation of China, and the Ministry of Science and Technology of China."

Metallic glass formation data for binary alloys, collected from various experimental techniques such as melt-spinning or mechanical alloying. This dataset covers all compositions with an interval of 5 at.% in 59 binary systems, containing a total of 5959 alloys in the dataset. The target property of this dataset is the glass forming ability (GFA), i.e. whether the composition can form monolithic glass or not, which is either 1 for glass forming or 0 for non-full glass forming.The V2 versions of this dataset have been cleaned to remove duplicate data points. Any entries with identical formula and both negative and positive GFA classes were combined to a single entry with a positive GFA class.Data is available as Monty Encoder encoded JSON and as the source CSV file. Recommended access method is with the matminer Python package using the datasets module.Note on citations: If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page.Dataset discussed in:Machine Learning Approach for Prediction and Understanding of Glass-Forming AbilityY. T. Sun†§ , H. Y. Bai†§, M. Z. Li*‡, and W. H. Wang*†§† Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China‡ Department of Physics, Beijing Key Laboratory of Optoelectronic Functional Materials & Micro-nano Devices, Renmin University of China, Beijing 100872, People’s Republic of China§ University of Chinese Academy of Science, Beijing 100049, People’s Republic of ChinaJ. Phys. Chem. Lett., 2017, 8 (14), pp 3434–3439DOI: 10.1021/acs.jpclett.7b01046Publication Date (Web): July 11, 2017

Population: Beijing: Changping data was reported at 2,267.000 Person th in 2022. This records a decrease from the previous number of 2,270.000 Person th for 2021. Population: Beijing: Changping data is updated yearly, averaging 1,908.000 Person th from Dec 2006 (Median) to 2022, with 17 observations. The data reached an all-time high of 2,270.000 Person th in 2021 and a record low of 492.000 Person th in 2006. Population: Beijing: Changping data remains active status in CEIC and is reported by Beijing Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GW: Population: Municipality District.

1.1 Data Overview

This dataset is the validation data for the effectiveness of intervention technology research and development of social function adaptation technology in indicator 3.3. It mainly includes demographic data of participants in intervention research, psychological health indicator data before and after testing, social individual network related indicator data, and overall network related indicator data.

1.2 Content Description

The pre test data in this dataset was collected from February 2023 to March 2023, with a total period of one month; The post test data was collected from May 2023 to June 2023, with a total collection period of one month. The intervention group was collected from a rural community in the western mountainous area of Beijing, targeting elderly people aged 60 to 85 within the community; The control group was collected from a rural community in the western canal area of Beijing, and the subjects were elderly people aged 60 to 85 within the community.

1.3 Directory Structure Description

This dataset is presented in Excel format, where SHEET1 is the demographic data of the subjects, SHEET2 is the psychological health indicator of the pre and post tests, SHEET3 is the individual social network indicator of the pre and post tests, and SHEET4-5 is the overall social network indicator of the subjects.

1.4 Document Description

In this dataset, SHEET1 is named "Demographic", which refers to the demographic information of the participants. "Gend." represents gender, "Age." represents age, "Edu." represents years of education, "Marr." represents marital status, "Child." represents the number of children, "Cohab." represents the number of co residents, and "Health." represents the self rated health level.

The name of SHEET2 is "MentalHealth", which refers to the psychological health indicators measured before and after. Among them, "D" represents the individual score of life satisfaction, "E" represents the individual score of loneliness, and "F" represents the individual score of depression level.

SHEET3 is named "SocielNetwork", which refers to the individual social network indicators measured before and after. Among them, "EgoS" represents the size of the individual network, "EgoD" represents the heterogeneity of the individual network, "EgoFre" represents the frequency of individual network connections, "EgoClose" represents the intimacy of the individual network, "InDe" represents the point centrality of the individual in the overall network, and "OutDe" represents the point centrality of the individual in the overall network. SHEET4-5 is the overall social network matrix of the participants.

This dataset is a provincial sub-dataset generated from the all-sky 1 km daily surface air temperature product over mainland China (http://doi.org/10.5281/zenodo.4399453) after resampling and clipping. The raw dataset was developed mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. The sub-dataset has a total data volume of only 264MB after compressed, making data download and understanding easier, and it also presents the characteristics of the raw dataset well. People who are interested in that big dataset can download the provincial sub-dataset first.

This sub-dataset was validated using ground measurements from 20 meteorological stations, with R2 and root mean square error (RMSE) values of 0.987 and 1.295 K, respectively, which proved reliability of this high-resolution dataset. In order to make this big dataset easier to understand and use, we made a provincial sub-dataset with a smaller geographic coverage.

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.

Population: Education Level: College & Higher: Beijing data was reported at 11.055 Person th in 2023. This records an increase from the previous number of 10.719 Person th for 2022. Population: Education Level: College & Higher: Beijing data is updated yearly, averaging 6.281 Person th from Dec 1982 (Median) to 2023, with 28 observations. The data reached an all-time high of 9,190.783 Person th in 2020 and a record low of 1.648 Person th in 1997. Population: Education Level: College & Higher: Beijing 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: Level of Education: By Region.

Population: Beijing: Haidian data was reported at 3,125.000 Person th in 2023. This records an increase from the previous number of 3,124.000 Person th for 2022. Population: Beijing: Haidian data is updated yearly, averaging 3,259.000 Person th from Dec 2006 (Median) to 2023, with 18 observations. The data reached an all-time high of 3,694.000 Person th in 2015 and a record low of 1,989.000 Person th in 2006. Population: Beijing: Haidian data remains active status in CEIC and is reported by Beijing Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GW: Population: Municipality District.

Beijing: Number of Tourist: Tourist Attraction data was reported at 26,653.000 Person-Time th in Dec 2024. This records a decrease from the previous number of 34,532.000 Person-Time th for Nov 2024. Beijing: Number of Tourist: Tourist Attraction data is updated monthly, averaging 21,255.500 Person-Time th from Jan 2008 (Median) to Dec 2024, with 192 observations. The data reached an all-time high of 53,105.000 Person-Time th in Aug 2024 and a record low of 3,321.000 Person-Time th in Feb 2020. Beijing: Number of Tourist: Tourist Attraction data remains active status in CEIC and is reported by Beijing Municipal Commission of Tourism Development. The data is categorized under China Premium Database’s Tourism Sector – Table CN.QRA: Tourism: Beijing.

Number of Motor Vehicle: Passenger: Beijing data was reported at 5,737.730 Unit th in 2023. This records an increase from the previous number of 5,638.384 Unit th for 2022. Number of Motor Vehicle: Passenger: Beijing data is updated yearly, averaging 2,516.345 Unit th from Dec 1991 (Median) to 2023, with 33 observations. The data reached an all-time high of 5,737.730 Unit th in 2023 and a record low of 123.800 Unit th in 1991. Number of Motor Vehicle: Passenger: Beijing data remains active status in CEIC and is reported by Ministry of Transport. The data is categorized under China Premium Database’s Automobile Sector – Table CN.RAG: No of Motor Vehicle: Passenger Car.

Beijing: Visitor Arrival: Year to Date data was reported at 475.928 Person-Time th in Feb 2025. This records an increase from the previous number of 253.154 Person-Time th for Jan 2025. Beijing: Visitor Arrival: Year to Date data is updated monthly, averaging 1,574.675 Person-Time th from Feb 1998 (Median) to Feb 2025, with 304 observations. The data reached an all-time high of 5,204.021 Person-Time th in Dec 2011 and a record low of 9.238 Person-Time th in Feb 2021. Beijing: Visitor Arrival: Year to Date data remains active status in CEIC and is reported by Beijing Municipal Commission of Tourism Development. The data is categorized under China Premium Database’s Tourism Sector – Table CN.QRA: Tourism: Beijing.

Employment: Urban: Beijing data was reported at 9.890 Person mn in 2023. This records a decrease from the previous number of 9.910 Person mn for 2022. Employment: Urban: Beijing data is updated yearly, averaging 8.471 Person mn from Dec 1993 (Median) to 2023, with 31 observations. The data reached an all-time high of 15.696 Person mn in 2018 and a record low of 4.563 Person mn in 2000. Employment: Urban: Beijing data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GB: Employment: Region.

CN: Population: Birth Rate: Anhui data was reported at 0.617 % in 2024. This records a decrease from the previous number of 0.645 % for 2023. CN: Population: Birth Rate: Anhui data is updated yearly, averaging 1.288 % from Dec 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 2.447 % in 1990 and a record low of 0.617 % in 2024. CN: Population: Birth Rate: Anhui 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: Birth Rate: By Region.

Number of Motor Vehicle: Beijing data was reported at 6,376.000 Unit th in 2023. This records an increase from the previous number of 6,256.000 Unit th for 2022. Number of Motor Vehicle: Beijing data is updated yearly, averaging 5,189.000 Unit th from Dec 2003 (Median) to 2023, with 21 observations. The data reached an all-time high of 6,376.000 Unit th in 2023 and a record low of 1,630.704 Unit th in 2003. Number of Motor Vehicle: Beijing data remains active status in CEIC and is reported by Beijing Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Automobile Sector – Table CN.RAH: No of Motor Vehicle: Prefecture Level City.