Spatiotemporal dynamics of urban sprawl in China from 2000 to 2020

Figures & data

Figure 1. The division of prefecture-level and above cities in different regions and city sizes in China. The four regional division (eastern, central, western and northeastern regions) was obtained from the classification scheme of the National Bureau of Statistics, the values represent the number of cities in each region.

Table 1. Data sources and descriptions.

Type	Name	Description	Source	Resolution	Year
Urban area data	Chinese urban built-up area dataset	Based on NTL; NDVI; LST, classified by support vector machine	National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/)	1 km	2000–2020
Remote sensing image data	Nighttime stable light data	Based on DMSP/OLS and VIIRS, produced composite images of NTL	The US National Geophysical Data Center (https://ngdc.noaa.gov/eog/)	1 km	2000–2020
	Land surface temperature data	Composed of average LST values of MODIS 8-day composite imagery	The National Aeronautics and Space Administration (https://ladsweb.modaps.eosdis.nasa.gov/)	1 km	2000–2020
	Normalized difference vegetation index data	Composite of maximum NDVI values over 16 days	The National Aeronautics and Space Administration (https://ladsweb.modaps.eosdis.nasa.gov/)	1 km	2000–2020
Urban population data	Population census data	Accurate statistics of the permanent population living in the city	National Bureau of Statistics of China (https://www.stats.gov.cn/sj/pcsj/)	/	2000/2010/2020
Socio-economic data	China City Statistical Yearbook	Exploring the driving force of urban sprawl	China Data Insights (https://data.cnki.net/yearBook/)	/	2000–2020
Geographic auxiliary data	The administrative boundaries	The Vector Boundary of Chinese Cities	The National Geomatics Center of China (http://bzdt.ch.mnr.gov.cn/)	Vector	2019

Figure 2. The methodological workflow of the study.

Table 2. Selected driving factors of urban sprawl in China.

Dimensions	Code	Indicator
National policies	X1	Public budget expenditure
	X2	Public budget revenue
Economic development	X3	GDP share of secondary industry
	X4	Foreign investment amount
Society	X5	Population density
	X6	Employees of the unit
Transportation	X7	Per capita road area
	X8	Per capita number of buses
Education level	X9	Number of universities
	X10	Number of university students

Figure 3. The values of urban sprawl index (USI) of 315 cities in China during 2000 ~ 2020.

Figure 4. Dynamics of urban sprawl measured by urban sprawl index (USI) during the (a) 2000~2010 and (b) 2010~2020 periods.

Figure 5. Variations in urban sprawl among different regions: (a) urban sprawl index in different regions during 2000 ~ 2020 (note: the box plot displays the average, the median, the first and third quartiles of urban sprawl index); (b) the proportion of cities with varying urban sprawl values relative to the national averages.

Figure 6. Variations in urban sprawl among different regions: (a) and (c) average urban sprawl index (USI) in different regions during 2000~2010 and 2010~2020, respectively; (b) and (d) the proportion of cities with varying urban sprawl values relative to the national averages during 2000~2010 and 2010~2020, respectively.

Table 3. Theil value of the urban sprawl index and the contributions of interregional and intraregional urban sprawl in China at different periods.

Classification	2000~2020		2000~2010		2010~2020
	Theil index	Contribution	Theil index	Contribution	Theil index	Contribution
China	0.24	100%	0.38	100%	0.31	100%
Interregional	0.05	20.8%	0.06	15.8%	0.05	16.1%
Intraregional	0.19	79.2%	0.32	84.2%	0.26	83.9%
East	0.24	—	0.25	—	0.46	—
Central	0.17	—	0.31	—	0.19	—
West	0.21	—	0.44	—	0.16	—
Northeast	0.16	—	0.28	—	0.26	—;

Figure 7. Spatial clusters of urban sprawl detected by Getis-Ord G_i* during the (a) 2000~2020; (b) 2000~2010; and (c) 2010~2020 period.

Figure 8. The average urban sprawl index obtained at different city scales during the (a) 2000~2020; (b) 2000~2010 and 2010~2020 periods.

Figure 9. Driving factors of urban sprawl based on the factor detection method: explanatory powers of driving factors during (a) 2000~2020; (b) 2000~2010 and 2010~2020; and (c) driving forces of urban sprawl from different dimensions.

Figure 10. Driving factors of urban sprawl during 2000~2020 based on the interactive detection of geodetector (the value represents the interactive effect of a combinations of factors, e.g. 0.461 for the combination of X₁ and X₅ on urban sprawl).

Figure 11. The driving forces of urban sprawl in (a) different regions; and (b) different city sizes.

Data availability statement

The urban area data used in this study can be accessed through the National Tibetan Plateau Scientific Data Center, which were derived from the Chinese urban built-up area dataset (1992 ~ 2020)V1.0 (https://doi.org/10.11888/HumanNat.tpdc.272851).The socio-economic data was available at the China City Statistical Yearbook (https://data.cnki.net/yearBook/). The urban population data from population census data can be obtained through https://www.stats.gov.cn/sj/pcsj/. The geographic auxiliary data can be accessed through the National Geomatics Center of China (http://bzdt.ch.mnr.gov.cn/). The data that support the findings of this study are available from the corresponding author upon reasonable request.