According to a report on Chinese cities from 2024 that provided a ranking of their cultural vitality and quality of life, Shanghai led the list with a total composite score of ***. Beijing and Hangzhou came in second and third. The overall city ranking, which comprised ten subsets, was headed by China's capital Beijing.

According to a report on Chinese cities from 2024 that provided a ranking of their intellectual capital, Shenzhen led the list with a total composite score of ***. Beijing and Shanghai came in second and third. The overall city ranking, which comprised *** subsets, was headed by China's capital Beijing.

In a 2024 World Population Review report, Hong Kong was the top ranked smart city in China with a motion index score of *****. Moreover, Shanghai ranked second with *****. China has been a leader in the development of smart cities, and Hong Kong has made significant steps in that direction, launching a Smart City Blueprint in 2017.

According to a report on Chinese cities from 2024 that provided a ranking of their regional influence, Shanghai led the list with a total composite score of ***. Guangzhou and Chongqing came in second and third. The overall city ranking, which consisted of ten subsets, was headed by China's capital Beijing.

According to a report on Chinese cities from 2024 that provided a ranking of their urban resilience, Beijing led the list with a total composite score of ***. Shanghai and Hangzhou came in second and third. The overall city ranking, which consisted of *** subsets, was headed by China's capital Beijing.

According to a report on Chinese cities from 2024 that provided a ranking of their business environment, Shenzhen led the list with a total composite score of ***. Suzhou and Shanghai came in second and third. The overall city ranking, which was composed of ten subsets, was headed by China's capital Beijing.

China Air Quality: PM2.5 Concentration: Monthly Average: 74 City data was reported at 35.000 mcg/Cub m in May 2018. This records a decrease from the previous number of 44.000 mcg/Cub m for Apr 2018. China Air Quality: PM2.5 Concentration: Monthly Average: 74 City data is updated monthly, averaging 53.000 mcg/Cub m from Jan 2013 (Median) to May 2018, with 63 observations. The data reached an all-time high of 130.000 mcg/Cub m in Jan 2013 and a record low of 27.000 mcg/Cub m in Aug 2017. China Air Quality: PM2.5 Concentration: Monthly Average: 74 City data remains active status in CEIC and is reported by China National Environmental Monitoring Centre. The data is categorized under China Premium Database’s Environmental Protection – Table CN.EPJ: Air Quality: PM2.5 Concentration: Region.

This dataset is about universities in China. It has 357 rows. It features 5 columns: city, ranking, foundation year, and international students.

Comparative ranking of ten Chinese cities in NEVs development prospects by multiple MADM methods.

Distributional estimation of forecasted housing prices in China.

Comprehensive dataset of 1 City district offices in China as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 0 Ghost towns in China as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

According to a report on Chinese cities from 2024 that provided a ranking of their economic clout, Beijing, Shanghai, and Hong Kong shared first place with a total composite score of ***. The overall city ranking, which consisted of ten subsets, was headed by China's capital Beijing.

Comprehensive dataset of 0 Military towns in China as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Major cities of Jiangsu Province, China at County level

The ranking of search content for cities in one week (from 2016.12.26 to 2017.01.01).

Comprehensive dataset of 7 City government offices in Shanghai, China as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

These compressed files contain data and Python code to replicate the paper “Measuring physical disorder in urban street spaces: A large-scale analysis using street view images and deep learning”. The data arise from a large-scale analysis of physical disorder in 264 major cities in China. The purpose is to use street view images and deep learning model to estimate the physical disorder score of each sampling point, street and city.

MobileNetV3 code: This is a PyTorch implementation of MobileNetV3 architecture as described in the paper. The trained models are also included in the code for prediction. Please see “README.md” for detailed procedures.

Training samples: The image samples used to train the 15 physical disorder factors are included and can be used to train other machine learning models.

Virtual audit tool: This tool is used to label images in a given directory, developed with a single Python script with GUI. Labeled images can be moved or copied into sub-directories, that are named with the assigned labels. Please see “README.md” for detailed procedures.

Physical disorder results: The folder contains the results of the estimation of multi-scale physical disorder. The three shapefiles “china_cities.shp”, “china_streets.shp”, and “china_svipoints.shp” record physical disorder scores for 264 cities, 769,407 streets and 1,219,238 sampling points, respectively. The attributes of the data are explained below and detailed in “Metadata.doc”:

1. china cities.shp The attributes of this shapefile include: unique id of each city (ID), Chinese name of 264 cities (name), English name of 264 cities (nameEng), spatial distribution pattern of street physical disorder (mode), including: (a) scattered; (b) diffused along the urban expansion direction; (c) linear concentrated along arterial roads, and physical disorder value of each city (pdvalue), physical disorder value for each factor at each city (Field names are acronyms for factor names, e.g. "AB" is an acronym for abandoned buildings). See "Metadata.doc" for a cross-reference to factor names and their acronyms.

2. china_streets.shp The attributes of this shapefile include: unique id of each street (street_id), Chinese name of 264 cities (name), English name of 264 cities (nameEng), physical disorder value of each street (pdvalue), and physical disorder value for each factor at each street (Field names are acronyms for factor names, e.g. "AB" is an acronym for abandoned buildings). See "Metadata.doc" for a cross-reference to factor names and their acronyms.

3. china_svipoints.shp The attributes of this shapefile include: unique id of each sampling point (point_id), unique id of the street where it is located (street_id), physical disorder value of each point (pdvalue) and physical disorder value for each factor at each point (Field names are acronyms for factor names, e.g. "AB" is an acronym for abandoned buildings). See "Metadata.doc" for a cross-reference to factor names and their acronyms.

Clarifying the association between city population size and older adults’ health is vital in understanding the health disparity across different cities in China. Using a nationally representative dataset, this study employed Multilevel Mixed-effects Probit regression models and Sorting Analysis to elucidate this association, taking into account the sorting decisions made by older adults. The main results of the study include: (1) The association between city population size and the self-rated health of older adults shifts from a positive linear to an inverted U-shaped relationship once individual socioeconomic status is controlled for; the socioeconomic development of cities, intertwined with the growth of their populations, plays a pivotal role in yielding health benefits. (2) There is a sorting effect in older adults’ residential decisions; compared to cities with over 5 million residents, unobserved factors result in smaller cities hosting more less-healthy older adults, which may cause overestimation of health benefits in cities with greater population size. (3) The evolving socioeconomic and human-made environment resulting from urban population growth introduces health risks for migratory older adults but yields benefits for those with local resident status who are male, aged over 70, and have lower living standards and socioeconomic status. And (4) The sorting effects are more pronounced among older adults with greater resources supporting their mobility or those without permanent local resident status. Thus, policymakers should adapt planning and development strategies to consider the intricate relationship between city population size and the health of older adults.

As an effective approach to mitigating urban environmental issues, New Energy Vehicles (NEVs) have become a focal point of research regarding their current development status and future prospects in China. Addressing the significant disparities in the development of the NEVs industry across different cities, this study focuses on ten typical Chinese cities and develops a novel multi-attribute decision-making (MADM) framework to evaluate the prospects of NEVs promotion in these cities. The study first establishes a comprehensive indicator system that covers key dimensions such as economy, policy support, infrastructure, technological innovation, and environment, encompassing five different types of evaluation information. This system incorporates five different types of evaluation information: exact numbers, interval numbers, triangular fuzzy numbers, hesitant fuzzy numbers, and probabilistic linguistic term sets (PLTS), enhancing the framework’s ability to handle diverse data types. Subsequently, the improved entropy (IEntropy) weight method is employed to determine the objective weights of the evaluation indicators. These objective weights are then integrated with the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, facilitating a structured group decision-making approach that synthesizes hybrid evaluation information. Based on modular thinking, hybrid evaluation information is synthesized to evaluate and rank the NEVs development prospects of each city. Sensitivity analysis and comparative analysis further demonstrate the robustness and reliability of the proposed MADM framework. The ranking results indicate that Shanghai and Guangzhou lead in NEVs promotion, while cities like Harbin and Zhengzhou lag behind. Based on these findings, the study proposes targeted policy recommendations to promote the sustainable development of the NEVs industry in major Chinese cities.