Here we used remote sensing data from multiple sources (time-series of Landsat and Sentinel images) to map the impervious surface area (ISA) at five-year intervals from 1990 to 2015, and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more, which were listed in the United Nations (UN) World Urbanization Prospects (WUP) database (including Mainland China, Hong Kong, Macao and Taiwan). We employed a range of spectral indices to generate the 1990–2015 ISA maps in urban areas based on remotely sensed data acquired from multiple sources. In this process, various types of auxiliary data were used to create the desired products for urban areas through manual segmentation of peri-urban and rural areas together with reference to several freely available products of urban extent derived from ISA data using automated urban–rural segmentation methods. After that, following the well-established rules adopted by the UN, we carried out the conversion to the standardized built-up area products from the 1990–2015 ISA maps in urban areas, which conformed to the definition of urban agglomeration area (UAA). Finally, we implemented data postprocessing to guarantee the spatial accuracy and temporal consistency of the final product.The standardized urban built-up area dataset (SUBAD–China) introduced here is the first product using the same definition of UAA adopted by the WUP database for 433 county and higher-level cities in China. The comparisons made with contemporary data produced by the National Bureau of Statistics of China, the World Bank and UN-habitat indicate that our results have a high spatial accuracy and good temporal consistency and thus can be used to characterize the process of urban expansion in China.The SUBAD–China contains 2,598 vector files in shapefile format containing data for all China's cities listed in the WUP database that have different urban sizes and income levels with populations over 300,000. Attached with it, we also provided the distribution of validation points for the 1990–2010 ISA products of these 433 Chinese cities in shapefile format and the confusion matrices between classified data and reference data during different time periods as a Microsoft Excel Open XML Spreadsheet (XLSX) file.Furthermore, The standardized built-up area products for such cities will be consistently updated and refined to ensure the quality of their spatiotemporal coverage and accuracy. The production of this dataset together with the usage of population counts derived from the WUP database will close some of the data gaps in the calculation of SDG11.3.1 and benefit other downstream applications relevant to a combined analysis of the spatial and socio-economic domains in urban areas.

If you want to use this data, please cite our article:Xiong, S., Zhang, X., Lei, Y., Tan, G., Wang, H., & Du, S. (2024). Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain. Remote Sensing of Environment, 312, 114344.The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km2), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.

CN: Population: Usual Residence: Urbanization Rate: Zhejiang data was reported at 75.500 % in 2024. This records an increase from the previous number of 74.230 % for 2023. CN: Population: Usual Residence: Urbanization Rate: Zhejiang data is updated yearly, averaging 62.910 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 75.500 % in 2024 and a record low of 48.700 % in 2000. CN: Population: Usual Residence: Urbanization Rate: Zhejiang 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: Urbanization Rate.

CN: Population: Usual Residence: Urbanization Rate: Hubei data was reported at 66.350 % in 2024. This records an increase from the previous number of 65.470 % for 2023. CN: Population: Usual Residence: Urbanization Rate: Hubei data is updated yearly, averaging 26.810 % from Dec 1949 (Median) to 2024, with 71 observations. The data reached an all-time high of 66.350 % in 2024 and a record low of 8.790 % in 1949. CN: Population: Usual Residence: Urbanization Rate: Hubei 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: Urbanization Rate.

A high-resolution circa-2020 map of urban lakes ( ≥0.001 km2) in China. The 10-m-resoultion Sentinel-2 imagery as well as a simple but robust water extraction method were used to generate waterbodies in China on Google Earth Engine. After initially filtering out urban water bodies based on the spatial relationships with urban area boundary, we combined high-resolution historical imagery to manually remove non-lake waters such as rivers and paddy fields, and to edit and supplement the missing urban lakes separately. The accuracy of our dataset was evaluated in terms of both area and count of lakes by comparing with manually vectorizing results in randomly sampled urban units. The results showed that our dataset is highly accurate and trustworthy, with the averaged accuracy of 81.85% in area and 93.35% in count.

Historical dataset showing China urban population by year from 1960 to 2023.

CN: Population: Usual Residence: Urbanization Rate: Fujian data was reported at 71.800 % in 2024. This records an increase from the previous number of 71.040 % for 2023. CN: Population: Usual Residence: Urbanization Rate: Fujian data is updated yearly, averaging 59.320 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 71.800 % in 2024 and a record low of 41.960 % in 2000. CN: Population: Usual Residence: Urbanization Rate: Fujian 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: Urbanization Rate.

This dataset is derived from the article: Huang, M., Wang, Z.C., Pan, X.H., Gong, B.H., Tu, M.Z., & Liu, Z.F. (2022). Delimiting China's urban growth boundaries under localized shared socioeconomic pathways and various urban expansion modes. Earth's Future, 10, e2021EF002572. The dataset shows the urban expansion and urban growth boundaries of China in 2021-2100 under different socioeconomic scenarios and diverse urban expansion modes. To produce this dataset, the patch-based LUSD-urban model was used to simulate the urban expansion with 11 modes under the localized shared socioeconomic pathways, and the morphology approach was used to delimit urban growth boundaries according to the maximum extent of urban expansion. Using this dataset, the authors quantified the impacts of future urban expansion on ecosystem services under different scenarios and diverse modes, as well as the pressure of urban shrinkage, which is helpful to the Chinese government to demarcate urban development boundaries.

This is an urban traffic speed dataset which consists of 214 anonymous road segments (mainly consist of urban expressways and arterials) within two months (i.e., 61 days from August 1, 2016 to September 30, 2016) at 10-minute interval, and the speed observations were collected in Guangzhou, China. In practice, it can be used to conduct missing data imputation, short-term traffic prediction, and traffic pattern discovery experiments.

According to the spatial and temporal attributes, we can easily derive a third-order tensor as \(\mathcal{X}\in\mathbb{R}^{214\times 61\times 144}\) and its dimensions include road segment, day and time window (see the file tensor.mat). The total number of speed observations (or non-zero entries of the tensor \(\mathcal{X}\)) is \(1,855,589\). If the dataset is complete, then we have \(214\times 61\times 144=1,879,776\) observations, therefore, the original missing rate of this dataset is \(1.29\%\).

Note that the file traffic_speed_data.csv is the original traffic speed data with four columns including road segment attribute, day attribute, time window attribute, and traffic speed value. The file day_information_table.csv is a table referring to the specific date, and the file time_information_table.csv is a table expressing time window with start time and end time information.

Feel free to email me with any questions: chenxy346@mail2.sysu.edu.cn (author: Xinyu Chen).

Acknowledgement: Mr. Weiwei Sun (affiliated with Sun Yat-Sen University) also provided insightful suggestion and help for publishing this data set. Thank you!

MCGD_Data_V2.2 contains all the data that we have collected on locations in modern China, plus a number of locations outside of China that we encounter frequently in historical sources on China. All further updates will appear under the name "MCGD_Data" with a time stamp (e.g., MCGD_Data2023-06-21)

You can also have access to this dataset and all the datasets that the ENP-China makes available on GitLab: https://gitlab.com/enpchina/IndexesEnp

Altogether there are 464,970 entries. The data include the name of locations and their variants in Chinese, pinyin, and any recorded transliteration; the name of the province in Chinese and in pinyin; Province ID; the latitude and longitude; the Name ID and Location ID, and NameID_Legacy. The Name IDs all start with H followed by seven digits. This is the internal ID system of MCGD (the NameID_Legacy column records the Name IDs in their original format depending on the source). Locations IDs that start with "DH" are data points extracted from China Historical GIS (Harvard University); those that start with "D" are locations extracted from the data points in Geonames; those that have only digits (8 digits) are data points we have added from various map sources.

One of the main features of the MCGD Main Dataset is the systematic collection and compilation of place names from non-Chinese language historical sources. Locations were designated in transliteration systems that are hardly comprehensible today, which makes it very difficult to find the actual locations they correspond to. This dataset allows for the conversion from these obsolete transliterations to the current names and geocoordinates.

From June 2021 onward, we have adopted a different file naming system to keep track of versions. From MCGD_Data_V1 we have moved to MCGD_Data_V2. In June 2022, we introduced time stamps, which result in the following naming convention: MCGD_Data_YYYY.MM.DD.

UPDATES

MCGD_Data2025_02_28 includes a major change with the duplication of all the locations listed under Beijing, Shanghai, Tianjin, and Chongqing (北京, 上海, 天津, 重慶) and their listing under the name of the provinces to which they belonge origially before the creation of the four special municipalities after 1949. This is meant to facilitate the matching of data from historical sources. Each location has a unique NameID. Altogether there are 472,818 entries

MCGD_Data2025_02_27 inclues an update on locations extracted from Minguo zhengfu ge yuanhui keyuan yishang zhiyuanlu 國民政府各院部會科員以上職員錄 (Directory of staff members and above in the ministries and committees of the National Government). Nanjing: Guomin zhengfu wenguanchu yinzhuju 國民政府文官處印鑄局國民政府文官處印鑄局, 1944). We also made corrections in the Prov_Py and Prov_Zh columns as there were some misalignments between the pinyin name and the name in Chines characters. The file now includes 465,128 entries.

MCGD_Data2024_03_23 includes an update on locations in Taiwan from the Asia Directories. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown").

MCGD_Data2023.12.22 contains all the data that we have collected on locations in China, whatever the period. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown"). The dataset also includes locations outside of China for the purpose of matching such locations to the place names extracted from historical sources. For example, one may need to locate individuals born outside of China. Rather than maintaining two separate files, we made the decision to incorporate all the place names found in historical sources in the gazetteer. Such place names can easily be removed by selecting all the entries where the 'Province' data is missing.

CN: Population: Usual Residence: Urbanization Rate: Ningxia data was reported at 67.310 % in 2023. This records an increase from the previous number of 66.340 % for 2022. CN: Population: Usual Residence: Urbanization Rate: Ningxia data is updated yearly, averaging 21.480 % from Dec 1949 (Median) to 2023, with 75 observations. The data reached an all-time high of 67.310 % in 2023 and a record low of 7.090 % in 1949. CN: Population: Usual Residence: Urbanization Rate: Ningxia 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: Urbanization Rate.

MCGD_PRC is a list of cities in today's People’s Republic of China. It includes 2,525 locations, with with the following variables: name in Chinese (both traditional and simplified Chinese), name in pinyin, name of the province in Chinese and in pinyin; latitude and longitude.

The database contains shapefiles and official policy documents relevant to Chinese Urban Agglomerations (中国城市群), also known as Megaregions. Its base map, in shapefile format, integrates a diverse array of 369 cities and counties, as implemented in Baidu Map. This expansive collection encompasses spatial administrative boundary data and a variety of official Chinese policy documents issued over different periods. It is an invaluable tool for those seeking to comprehend the development and present dynamics of Chinese Urban Agglomerations. Furthermore, the database includes formal plans related to secondary cross-regional cooperation, shedding light on the joint efforts and strategic plans among different areas. This extensive dataset is crucial for researchers, policymakers, and anyone interested in the progression and management of urban clusters in China. Please Cite: Liu, L., & Wang, F. (2025). Delineating urban agglomeration regions in China by network community scanning: Structures and policy implications. Cities, 158, 105721. https://doi.org/10.1016/j.cities.2025.105721

The three datasets provided here identify the city location of all CDM projects in China by referencing the individual Project Description Documents (via the UNFCCC) attached to each project. Through this method, all 3,764 Clean Development Mechanism projects at the city-level in China are identified out of a total of over 8,000 globally.

Air pollution is one of China's most serious environmental issues, seriously affecting human health and well-being. However, the detection of PM2.5 began in most Chinese cities after 2013, and data in some cities are missing. First, we collected global ground-observed PM2.5 concentration data calibrated using geographically weighted regression at Washington University in St. Louis and data from air quality monitoring stations in China. Using the zonal statistics tool in ArcGIS software, the annual average PM2.5 concentration datasets of 342 administrative units (prefecture-level cities and regions) in China from 2000 to 2024 were calculated. This dataset could support some research on air pollution control and urban environmental regulation in China, and can also provide references for the assessment of local government's environmental performance.

EULUC China 2.0 is a product generated by an advanced urban land use mapping framework that integrates multimodal deep learning and multi-source geospatial data proposed in the paper entitled "Enhanced Mapping of Essential Urban Land Use Categories in China (EULUC-China 2.0): Integrating Multimodal Deep Learning and Multi-source Geospatial Data". This dataset provides highly accurate and fine-grained urban land use maps covering all cities in China for the year 2022. The product is publicly available and is expected to benefit a broad range of research and applications.

This data record provides details of the data supporting the claims of the related article: “An Assessment of Urbanization Sustainability in China between 1990 and 2015 Using Land Use Efficiency Indicators”.

The related study aimed to integrate Earth observation and census data to estimate the relationship between land, population and economic domains of urbanization in 433 cities over 25 years using land use efficiency indicators. This dataset includes original information on the ratio of land consumption rate to population growth rate (LCRPGR) and ratio of economic growth rate to land consumption rate (EGRLCR) of georeferenced sample cities with multiple attributes, accuracy assessment for urban impervious surface products, land occupied by construction in Mainland China since 2000.

Data access

The supporting data files are openly available as part of this figshare metadata record.

Data files

The LCRPGR and EGRLCR data are each available in the following formats: .cpg, .dbf, .prj, .sbn, .sbx, .shp, .shx

Accuracy assessment data; Sample city averages for EGR, LCR, EGRLCR, PGR and LCRPGR; Land occupancy; and EGRLCR/ LCRPGR classification data are all available in Excel format.

Corresponding authors for this study

Huadong Guo (hdguo@radi.ac.cn)

Qihao Weng (qweng@indstate.edu)

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Based on an investigation of urban forests across local transects from urban central areas to rural areas in Beijing and a regional transect from north to south in eastern China, we estabished a database on leaf and topsoil (0-10cm) Hg concentrations. The data are used for the study entitled "Geographical patterns of leaf and topsoil mercury in China’s urban forests" (Earth's Future).

We reconstruct the walled cities for China that extend back from 15th century to 19th century based on multiple historical documents. Cities in late imperial China (the Ming and the Qing Dynasties, 1368-1911) generally had city walls, and these walls were usually built around the urban built-up area. By restoring the scope of the city walls, it is helpful to explore the urban extend in this period Firstly, we collected the years of construction or reconstruction of city walls from the historical data. Specifically, the period in which the scope of the city wall keeps unchanged is recorded as a lifetime of it. Secondly, specialization of the scope of the city wall could be conducted based on the urban morphology method, and variety of documentation, including the historical literature materials, the military topographic maps of the first half of the 20th century, and the remote sensing images of the 1970s. Correlation and integration of the lifetime and the spatial data would produce China City Wall Areas Dataset (CCWAD) in late imperial. Based on the proximity to the time of most of the city walls, we selected six representative years (i.e., 1400, 1537, 1648, 1708, 1787, and 1866) from CCWAD to produce China Urban Extent Dataset (CUED) in the 15th-19th centuries.