This channel would release Beijing, or the whole China, micro-data and maps (e.g. road networks, parcels, human mobility, historical city maps) for the BCL research fellows and external researchers. There are three levels of data access, free download, email request, and shared among research fellows / student members.
How to cite:
Beijing City Lab, Year, Data ID, Data Name, http://www.beijingcitylab.com
E.g. Beiing City Lab, 2013, Data 8, Housing price in Beijing, http://www.beijingcitylab.com
(For the dataset from external source other than BCL, we would recommend you to cite the original source)
We are sharing 16,721 urban green lands in 287 Chinese cities in 2017. We extracted them from AMAP manually (https://ditu.amap.com). The data includes scenic spots, urban parks, and green spaces.
Please cite our paper if you apply our data for your research.
The dataset "Redefined Cities" depicts the redefined central regions of natural cities across China using emerging spatial big data. The dataset includes four shapefile data: redefined cities in 2011 and 2016, and corresponding blocks within cities. The redefined cities and blocks are computed with the method of redefining cities (Song, Y. et al. 2018) and points of interest (POIs) data.
The brief descriptions and attributes of four shapefile data are listed below.
(1) Redefined cities in 2011
The data consists of 2005 redefined cities with the total area of 0.167 million square kilometres. Five attributes are available for the data, including ID of redefined cities (CityID), area (Areakm2), POI density (PoiDens), density of road junctions (JuncDens) and population density (PopDens).
(2) Blocks of redefined cities in 2011
The data consists of 78 thousand blocks within the 2005 redefined cities. It includes two attributes: the corresponding ID of redefined cities (CityID) and areas of blocks (Aream2).
(3) Redefined cities in 2016
The data consists of 4678 redefined cities with the total area of 0.726 million square kilometres. Three attributes are available for the data, including ID of redefined cities (CityID), area (Areakm2) and POI density (PoiDens).
(4) Blocks of redefined cities in 2016
The data consists of 187 thousand blocks within the 4678 redefined cities. It includes two attributes: the corresponding ID of redefined cities (CityID) and areas of blocks (Aream2).
Note that the comparison between redefined cities in 2011 and 2016 may not be exactly comparative due to different sources and collection methods of POI datasets in the two years.
We recommend you cite the following publication as a reference of the data and a courtesy for using the data (attached below as well):
China’s administrative cities and spatial cities are mismatched and the administrative cities are much larger than their spatial regions. In the administrative boundary, Chinese cities compose both urbanization area and rural area，thus it if very important for redefining Chinese city system. We are sharing our identified spatial cities of China in 2015 using communities as basic administrative units and the data of urban built-up areas.
1) name: the name of redefined spatial cities
2) area: the area of redefined spatial cities (unit: km2)
3) kind: the type of redefined spatial cities (1 the central area of an administrative city; 2 the city district; 3 the sub-central area of an administrative city; 4 the central area of a county; 5 the sub-central area of a county; 6 others (a spatial city across boundaries of two or more administrative cities); 7 10km2>a spatial city ≥5km2; 8 5km2> a spatialcity ≥2km2)
I would suggest you cite the following papers as a courtesy for using our data.
We are sharing all urban big data we have for the old city of Beijing (around 62 sqkm in area). The inventory and GIS layers are as follows.
To access these data, please join our online MOOC course BIG DATA AND URBAN PLANNING and they are available for downloading when you have registered the course in the below link.
I would suggest you cite the following papers as a courtesy for using our data.
As what has been promised in our publication Long and Huang 2017 EPB_Vitality, I am now sharing the grid-level and national-wide urban vitality and the impacting factors data for all in the format of Shape Files. If you have any question regarding this data, please email email@example.com for more information. Prior to email me, I would suggest you have a close look at the above online visualization and our shared paper/slides listed below.
Your citing the following paper would be appreciated if you use our sharing data for research.
Long, Y., & Huang, C. C. (2017). Does block size matter? The impact of urban design on economic vitality for Chinese cities. Environment and Planning B: Urban Analytics and City Science, 2399808317715640.
Timely and accurate information of large-scale urban land distributions is fundamental to the understanding of global environmental changes. However, research of the global-scale urban land expansion and its long-term environmental impacts has been restricted by the shortage of high-resolution multi-temporal global urban land data. Most of the contemporary global urban land products have the coarse resolution of 500 to 1000 m, and the pertinent data is available for one year or two years only. Inconsistency among these products further exacerbates issues faced by researchers. Therefore, it is still difficult to obtain a clear picture of how global urban land expands over a long historical period using solely contemporary global urban land products.
To overcome this issue, we developed a new multi-temporal global impervious surface product, which is derived from Landsat images pertaining to the 1990-2010 period with a five-year interval. This is the world’s first multi-temporal data set of global impervious surface at 30-m resolution. The production of this data requires sophisticated tools that provide functions for efficient image selection and extensive computation. The Google Earth Engine, which is an open access cloud-based computing platform with comprehensive image data (including the collection of Landsat images), can perfectly fulfill the technical needs for the extraction of global impervious surfaces from an extensive amount of Landsat images. Using this platform, we designed an approach for automatic impervious surface extraction by segmenting the calculated Normalized Urban Areas Composite Index (NUACI), a recently developed indicator for detecting impervious surfaces. We conducted the region-specific calibration and testing for this approach based on the stratification scheme of ‘urban ecoregions’ proposed in extant literature. In comparison with the existing global urban land products, our mapping results provide much more detailed information, while also yielding a significantly improved accuracy, as indicated by the Kappa values are 0.4280-0.4953 at the global level, and ~0.3306 (in China) and ~0.4163 (in the US) at the country level. These figures reveal that the produced multi-temporal global impervious surface data are of reasonably good quality and can substantially support ongoing and future research focusing on the dynamics of global urban land expansion.
More information about the data is HERE.
Multi-temporal urban land products and reference datasets from 1990 - 2010 are available to download in Google Drive and Baidu Cloud (access password "tihz")
Points of interest of China in 2014 (10.6 million) shared in the format of ESRI ArcGIS File Geodatabase
Please send your data request to firstname.lastname@example.org while indicating your data using purpose as well as yourself.
Welcome cite the following papers (which use the data as well) for courtesy of using the data for publication.
Almost all parking places of Beijing in 2014 are shared in the dataset.
Data format: Shape Files
The data contributors: Ying Long
According to our previous bibliometrics study (城市规划的知识产出、消费与网络), the large Chinese cities have been attracted over much attention from researchers, and most of small cities in China are not well studied. For alleviating this situation, we are releasing the emerging new data (open data) for a small city in North East China, Yichun, which is experiencing population shrinking (for more, see the BCL project 15 Shrinking Cities, http://www.beijingcitylab.com/projects-1/15-shrinking-cities/). We hope this effort may shed light on the research for Shrinking Cities in China as well as potentially improve the quality of life of this small city through the lends of more studies and better decision making.
Data format: ESRI ArcGIS 10.x, File Geodatabase
The data contributors: Ying Long, Dong Li (more to come)
Welcome cite our papers:
1. Long Y, Wu K, 2016, “Shrinking cities in a rapidly urbanizing China”, Environment and Planning A 48 220-222
2. Liu X, Song Y, Wu K., Wang J, Li D, Long Y. (corresponding author), 2015, “Understanding urban China with open data”, Cities 47 53-61
3. Li D, Long Y, 2015, “A crowed-sourced data based analytical framework for urban planning”, China City Planning Review 24 49-57
We are sharing the urban areas interpreted from night time images DMSP-OSL and MODIS. The data provided by Prof Xiaoping Liu from Sun Yat-sen University cover the whole China for 2000, 2005 and 2010. Please cite the attached paper in case you use the data for research.
We gathered bus stops of most Chinese cities in 2013. We have used this data for bus coverage estimation. Bus coverage ratio of each city, a key indicator of 公交都市(交通部), was calculated by dividing the area of urban land overlaid with bus service coverage area with the total urban area of the city. Please see "1 Bus coverage of Chinese cities" for details (http://www.beijingcitylab.com/ranking/).
We are now sharing the bus stops of China which we used for our study. Please cite our recent paper in your publication using our data (attached below).
Prof Bin Jiang is willing to share points of interest in Germany, France and UK with all BCLers and those who are paying attention to us. Please direct visit his portal at ResearchGate for the data downloading.
All these data have been used in the previous work:
Citation to the paper is welcomed.
If you meet a problem on data downloading from ResearchGate, please address your email to beijingcitylab(at)gmail(dot)com and we would send you a Baiduyun link directly. Please keep in mind that the data on ResearchGate may be updated by Prof Jiang, and those updates may not included in the Baiduyun link.
Generated by DMSP/OLS 夜光遥感影像
Provided by the BCL research fellow Prof HE Chunyang at Beijing Normal University
Download: click the file link below.
For urban areas in 2008 produced by Prof HE, please see BCL Data 16.
Cite: Yang, Y., He, C., Zhang, Q., Han, L., & Du, S. (2013).Timely and accurate national-scale mapping of urban land in China using Defense Meteorological Satellite Program’s Operational Linescan System nighttime stable light data. Journal of Applied Remote Sensing, 7(1), 073535-073535. (see the link below for the full paper)
Other datasets of BCL that might interest you.
City-related data, with a rapid growth in amount, is involving various aspects of everyone’s daily life. City researchers are devoting efforts to deepen our understanding of city based on unorthodox data. However, as most data are too precise and thus, sharing these data may offend the benefits of the original data holders. In such context, focusing on the extent of China, we initiated SinoGrids, a platform for the sharing of micro-scale data based on a 1km fishnet. Guidelines and Tools are provided for the micro-scale data holders to downscale their original datasets onto the 1km fishnet and upload to the platform of SinoGrids, forming a crowdfunding platform for basic data in China.
In terms of the scale, 1km2 is a scale both available for regional analysis between cities and internal studies of a certain city. Also, SinoGrids will share its data in the crowdsourcing way. The collected datasets, from either donations of scholars or open internet resources, (e.g. Weibo, taxi trajectory, road junctions, bus stops, photos) will be summarized to the fishnet and made public on SinoGrids data platform. In other words, the total amount of Weibo, Flickr photos and bus stops, etc. per 1km2 according to the fishnet will be displayed on the platform. The platform will also maintain the most and complete indexes and data guidelines for the convenient implementation of the public. On the one hand, data holders donate their micro-scale data through SinoGrids. On the other hand, they can realize regional analysis, urban studies, city planning consultation, and public participation under the guidelines of SinoGrids. SinoGrids will be a public and open platform for city-related data, with a hope to provide complete and transparent data support for quantized researches and regional analysis.
For data browsing and downloading, please navigate to "14 SinoGrids" in the BCL Projects channel (Click me).
Natural cities in each year during 1992-2012 generated by Prof Bin Jiang (http://fromto.hig.se/~bjg/)
For more about Natural City, please visit Prof Bin Jiang's arXiv: http://arxiv.org/a/jiang_b_1
To cite: Jiang B. (2015), Head/tail breaks for visualization of city structure and dynamics, Cities, 43, 69-77. (http://www.sciencedirect.com/science/article/pii/S026427511400198X)
All 2,171,162 photos of China (till March 2014), prepared by Dr LI Dong
All check-in records collected from Sina Weibo in 2013
Totally 868 m check-ins for all 143,576 venues
Note that the coordinates of this data have been modified officially (火星坐标系). Additional georeferencing might be needed.
To cite: Long Y, Liu X, 2013, “How mixed is Beijing, China? A visual exploration of mixed land use” Environment and Planning A 45: 2797–2798
We release the dataset of several papers on inter-city network analysis by BCL research fellows. More is coming in future.
1 Liu, Y., Sui, Z., Kang, C., & Gao, Y. (2014). Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data. PloS one, 9(1), e86026.
2 甄峰, 王波, & 陈映雪. (2012). 基于网络社会空间的中国城市网络特征. 地理学报, 67(8).
We BCLers have been busy with mapping urban areas of China using various approaches like benchmarking road junction density and population density, as well as vector cellular automata.
Download: This dataset is open to all BCL members.
To cite: Long Y, Shen Y, 2014, Mapping parcel-level urban areas for a large geographical area, arXiv preprint arXiv:1403.5864. (http://arxiv.org/abs/1403.5864) Later this has been published in Annals of AAG
Four ShapeFile layers are included in the package:
(1) Road junction density
(2) Population density (based on BCL Data 19)
(3) Vector cellular automata
In addition, you can download BCL Data 16 DMSP/OLS interpreted urban areas of China in 2008 (night light images), and urban areas reflected by BCL Data 17 Impervious area of China (actually urban areas overestimated by the data).
Process: kernal density of all road junctions of China in 2011 (searching radius 1000m)
Spatial resolution 200m
Density (ESRI GRID)、Junctions (ShapeFile): please email email@example.com for the raw junctions of China (over 2 million junctions)
Provided by Dr Ying Long
To cite: Long Y, Shen Y, Jin X, 2015, “Mapping block-level urban areas for all Chinese cities”, Annals of the American Association of Geographers 106 96-113