Drivers of urban expansion over the past three decades: a comparative study of Beijing, Tianjin, and Shijiazhuang

  • Wenjia Wu
  • Shuqing ZhaoEmail author
  • Geoffrey M. Henebry


Urban expansion is influenced by various natural and anthropogenic factors. Understanding the driving forces of urban expansion is crucial for modeling the process of urban expansion as well as guiding urban planning and management. Here, we quantified and compared the effects of natural, socioeconomic, and neighboring factors on urban expansion and their temporal dynamics in three large cities in the Jing-Jin-Ji Urban Agglomeration: Beijing, Tianjin, and Shijiazhuang. We used remote sensing imagery from six epochs (circa 1980, 1990, 1995, 2000, 2005, and 2010) integrated with GIS techniques and analyzed using binary logistic regression. The relative importance of the three types of driving forces was further decomposed using variance partitioning. We found that the direction and/or magnitude of effects on the drivers of urban expansion varied with both epoch and city. Natural factors placed significant constraints at early stages of urban expansion, but this constraint relaxed over time. As precursor drivers of urbanization, socioeconomic factors significantly influenced urban growth in most epochs for each city. Non-urban lands near existing urban areas were more likely to be urbanized, due to easier access to existing transportation infrastructure and other facility resources. Furthermore, with urbanization, individual effects of drivers tended to be replaced by joint effects, especially for the neighboring factors. Similarities and differences in the individual and joint effects of drivers on urban expansion across cities and through time will provide valuable information for adaptive urban development strategies in the national capital region of China.


Remote sensing Urban expansion Driving forces Logistic regression Variance partitioning Jing-Jin-Ji Urban Agglomeration 



We thank Dr. Xiaochen Meng for providing the road and railway dataset.

Funding information

This study was supported by the National Natural Science Foundation of China grants 41590843, 41571079, and 41771093.


  1. Anderson, M. J., & Gribble, N. A. (1998). Partitioning the variation among spatial, temporal and environmental components in a multivariate data set. Australian Journal of Ecology, 23, 158–167.CrossRefGoogle Scholar
  2. Angel, S., Sheppard, S. C., Civco, D. L., Buckley, R., Chabaeva, A., Gitlin, L., Kraley, A., Parent, J., & Perlin, M. (2005). The dynamics of global urban expansion, transport and urban development department. Washington D.C: The World Bank.Google Scholar
  3. Bettencourt, L. M. (2013). The origins of scaling in cities. Science, 340, 1438–1441.CrossRefGoogle Scholar
  4. Betts, M. G., Diamond, A. W., Forbes, G. J., Villard, M., & Gunn, J. S. (2006). The importance of spatial autocorrelation, extent and resolution in predicting forest bird occurrence. Ecological Modelling, 191, 197–224.CrossRefGoogle Scholar
  5. Braimoh, A. K., & Onishi, T. (2007). Spatial determinants of urban land use change in Lagos, Nigeria. Land Use Policy, 24, 502–515.CrossRefGoogle Scholar
  6. Cheng, J. Q., & Masser, I. (2004). Understanding spatial and temporal processes of urban growth: cellular automata modelling. Environment and Planning. B, Planning & Design, 31, 167–194.CrossRefGoogle Scholar
  7. Danilina, E. I., & Chebotarev, V. E. (2017). Comprehensive assessment of road and communal infrastructure as an important tool for sustainable development of the urban economy. Theoretical and Empirical Researches in Urban Management, 12, 33–51.Google Scholar
  8. Daniels, T. (1999). When city and country collide: managing growth in the metropolitan fringe. Environmental Quality, 45, 57–58.Google Scholar
  9. Doll, C. H., Muller, J. P., & Elvidge, C. D. (2000). Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions. AMBIO, 29, 157–162.CrossRefGoogle Scholar
  10. Doll, C. N. H., Muller, J. P., & Morley, J. G. (2006). Mapping regional economic activity from night-time light satellite imagery. Ecological Economics, 57, 75–92.CrossRefGoogle Scholar
  11. Dubovyk, O., Sliuzas, R., & Flacke, J. (2011). Spatio-temporal modelling of informal settlement development in Sancaktepe district, Istanbul, Turkey. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 235–246.CrossRefGoogle Scholar
  12. Elvidge, C. D., Ziskin, D., Baugh, K. E., Tuttle, B. T., Ghosh, T., Pack, D. W., Erwin, E. H., & Zhizhin, M. (2009). A fifteen year record of global natural gas flaring derived from satellite data. Energies, 2, 595–622.CrossRefGoogle Scholar
  13. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874.CrossRefGoogle Scholar
  14. Gong, P., Liang, S., Carlton, E. J., Jiang, Q., Wu, J., Wang, L., & Remais, J. V. (2012). Urbanisation and health in China. The Lancet, 379, 843–852.CrossRefGoogle Scholar
  15. Gude, P. H., Hansen, A. J., Rasker, R., & Maxwell, B. (2006). Rates and drivers of rural residential development in the Greater Yellowstone. Landscape and Urban Planning, 77, 131–151.CrossRefGoogle Scholar
  16. Heikkinen, R. K., Luoto, M., Kuussaari, M., & Poyry, J. (2005). New insights into butterfly-environment relationships using partitioning methods. Proceedings of the Royal Society B-Biological Sciences, 272, 2203–2210.CrossRefGoogle Scholar
  17. Hills, P. J. (1996). What is induced traffic? Transportation, 23, 5–16.CrossRefGoogle Scholar
  18. Homer, C., Dewitz, J., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N., Wickham, J., & Megown, K. (2015). Completion of the 2011 National Land Cover Database for the conterminous United States—representing a decade of land cover change information. Photogrammetric Engineering & Remote Sensing, 81, 345–354.Google Scholar
  19. Jantz, C. A., Goetz, S. J., & Shelley, M. K. (2003). Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropoliton area. Environment and Planning. B, Planning & Design, 30, 251–271.Google Scholar
  20. Jiang, D., Zhuang, D. F., Xu, X. L., & Lei, Y. (2008). Integrated evaluation of urban development suitability based on remote sensing and GIS techniques—a case study in Jingjinji area, China. Sensors, 8, 5975–5986.CrossRefGoogle Scholar
  21. Ju, H., Zhang, Z., Zuo, L., Wang, J., Zhang, S., Wang, X., & Zhao, X. (2016). Driving forces and their interactions of built-up land expansion based on the geographical detector—a case study of Beijing, China. International Journal of Geographical Information Science, 30, 2188–2207.CrossRefGoogle Scholar
  22. Kaye, J. P., Groffman, P. M., Grimm, N. B., Baker, L. A., & Pouyat, R. V. (2006). A distinct urban biogeochemistry? Trends in Ecology & Evolution, 21, 192–199.CrossRefGoogle Scholar
  23. Körner, C., & Ohsawa, M. (2006). Mountain systems. In R. Scholes & N. Ash (Eds.), Ecosystem and human well-being: current state and trends (Millennium ecosystem assessment) (Vol. 1, pp. 681–716). Washington: Island Press.Google Scholar
  24. Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J. W., Coomes, O. T., Dirzo, R., Fischer, G., & Folke, C. (2001). The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change, 11, 261–269.CrossRefGoogle Scholar
  25. Levin, N., & Duke, Y. (2012). High spatial resolution night-time light images for demographic and socio-economic studies. Remote Sensing of Environment, 119, 1–10.CrossRefGoogle Scholar
  26. Li, X. M., Zhou, W. Q., & Ouyang, Z. Y. (2013). Forty years of urban expansion in Beijing: what is the relative importance of physical, socioeconomic, and neighborhood factors? Applied Geography, 38, 1–10.CrossRefGoogle Scholar
  27. Litteral, J., & Wu, J. (2012). Urban landscape matrix affects avian diversity in remnant vegetation fragments: evidence from the Phoenix metropolitan region, USA. Urban Ecosystems, 15, 939–959.CrossRefGoogle Scholar
  28. Liu, H., & Zhou, Q. (2005). Developing urban growth predictions from spatial indicators based on multi-temporal images. Computers, Environment and Urban Systems, 29, 580–594.CrossRefGoogle Scholar
  29. Liu, J., Kuang, W., Zhang, Z., Xu, X., Qin, Y., Ning, J., Zhou, W., Zhang, S., Li, R., Yan, C., Wu, S., Shi, X., Jiang, N., Yu, D., Pan, X., & Chi, W. (2014). Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. Journal of Geographical Sciences, 24, 195–210.CrossRefGoogle Scholar
  30. Liu, J. Y., Zhan, J. Y., & Deng, X. Z. (2005). Spatio-temporal patterns and driving forces of urban land expansion in China during the economic reform era. AMBIO, 34, 450–455.CrossRefGoogle Scholar
  31. Liu, Z. F., He, C. Y., Zhang, Q. F., Huang, Q. X., & Yang, Y. (2012). Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008. Landscape and Urban Planning, 106, 62–72.CrossRefGoogle Scholar
  32. Long, H., Tang, G., Li, X., & Heilig, G. K. (2007). Socio-economic driving forces of land-use change in Kunshan, the Yangtze River Delta economic area of China. Journal of Environmental Management, 83, 351–364.CrossRefGoogle Scholar
  33. Long, H., Wu, X., Wang, W., & Dong, G. (2008). Analysis of urban-rural land-use change during 1995-2006 and its policy dimensional driving forces in Chongqing, China. Sensors, 8, 681–699.CrossRefGoogle Scholar
  34. Lu, C., Wu, Y., Shen, Q., & Wang, H. (2013). Driving force of urban growth and regional planning: a case study of China’s Guangdong Province. Habitat International, 40, 35–41.CrossRefGoogle Scholar
  35. Lucas, R. (1988). On the mechanics of economic-development. Journal of Monetary Economics, 22, 3–42.CrossRefGoogle Scholar
  36. Mcdonnell, M. J., & Macgregor-Fors, I. (2016). The ecological future of cities. Science, 352, 936–938.CrossRefGoogle Scholar
  37. Mertes, C. M., Schneider, A., Sulla-Menashe, D., Tatem, A. J., & Tan, B. (2015). Detecting change in urban areas at continental scales with MODIS data. Remote Sensing of Environment, 158, 331–347.CrossRefGoogle Scholar
  38. Nagelkerke, N. J. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78, 691–692.CrossRefGoogle Scholar
  39. NBSC (National Bureau of Statistics of China). (2011). China statistical yearbook. Beijing: China Statistics Press.Google Scholar
  40. Osman, T., Divigalpitiya, P., & Arima, T. (2016). Driving factors of urban sprawl in Giza Governorate of Greater Cairo Metropolitan Region using AHP method. Land Use Policy, 58, 21–31.CrossRefGoogle Scholar
  41. Qu, W., Zhao, S., & Sun, Y. (2014). Spatiotemporal patterns of urbanization over the past three decades: a comparison between two large cities in Southwest China. Urban Ecosystems, 17, 1–17.CrossRefGoogle Scholar
  42. Rong, Z. R., An-Qing, M. A., Wang, Z. K., & Zhou, K. (2012). Driving forces analysis of landscape pattern changes based on logistic regression model in wetland of Liaohe. Environmental Science & Technology, 35, 193–198.Google Scholar
  43. Schweizer, P. E., & Matlack, G. R. (2014). Factors driving land use change and forest distribution on the coastal plain of Mississippi, USA. Landscape and Urban Planning, 121, 55–64.CrossRefGoogle Scholar
  44. Seto, K. C., Fragkias, M., Gueneralp, B., & Reilly, M. K. (2011). A meta-analysis of global urban land expansion. PLoS One, 6(8), e23777. Scholar
  45. Seto, K. C., & Kaufmann, R. K. (2003). Modeling the drivers of urban land use change in the Pearl River Delta, China: integrating remote sensing with socioeconomic data. Land Economics, 79, 106–121.CrossRefGoogle Scholar
  46. Siedentop, S., & Fina, S. (2010). Urban sprawl beyond growth: the effect of demographic change on infrastructure costs. Flux, 79–80, 90–100.CrossRefGoogle Scholar
  47. Silva, P., & Li, L. (2017). Mapping urban expansion and exploring its driving forces in the city of Praia, Cape Verde, from 1969 to 2015. Sustainability, 9, 1434. Scholar
  48. Team, R. D. C. (2013). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing ISBN 3-900051-07-0.Google Scholar
  49. Thapa, R. B., & Murayama, Y. (2010). Drivers of urban growth in the Kathmandu valley, Nepal: examining the efficacy of the analytic hierarchy process. Applied Geography, 30, 70–83.CrossRefGoogle Scholar
  50. Vitousek, P. M., Mooney, H. A., Lubchenco, J., & Melillo, J. M. (1997). Human domination of Earth’s ecosystems. Science, 277, 494–499.CrossRefGoogle Scholar
  51. Wang, Q., Ren, Q., & Liu, J. (2016). Identification and apportionment of the drivers of land use change on a regional scale: unbiased recursive partitioning-based stochastic model application. Agriculture, Ecosystems & Environment, 217, 99–110.CrossRefGoogle Scholar
  52. White, R., Engelen, G., & Uljee, I. (2015). Modeling cities and regions as complex systems: From theory to planning applications (pp. 344). Cambridge, Massachusetts: MIT Press.Google Scholar
  53. Wu, J., Wang, Z., Li, W., & Peng, J. (2013). Exploring factors affecting the relationship between light consumption and GDP based on DMSP/OLS nighttime satellite imagery. Remote Sensing of Environment, 134, 111–119.CrossRefGoogle Scholar
  54. Wu, W., Zhao, S., Zhu, C., & Jiang, J. (2015). A comparative study of urban expansion in Beijing, Tianjin and Shijiazhuang over the past three decades. Landscape and Urban Planning, 134, 93–106.CrossRefGoogle Scholar
  55. Yue, W. Z., Liu, Y., & Fan, P. L. (2013). Measuring urban sprawl and its drivers in large Chinese cities: the case of Hangzhou. Land Use Policy, 31, 358–370.CrossRefGoogle Scholar
  56. Zhao, C., Jensen, J., & Zhan, B. (2017). A comparison of urban growth and their influencing factors of two border cities: Laredo in the US and Nuevo Laredo in Mexico. Applied Geography, 79, 223–234.CrossRefGoogle Scholar
  57. Zhao, P. J. (2010). Sustainable urban expansion and transportation in a growing megacity: consequences of urban sprawl for mobility on the urban fringe of Beijing. Habitat International, 34, 236–243.CrossRefGoogle Scholar
  58. Zhao, S. Q., Da, L. J., Tang, Z. Y., Fang, H. J., Song, K., & Fang, J. Y. (2006). Ecological consequences of rapid urban expansion: Shanghai, China. Frontiers in Ecology and the Environment, 4, 341–346.CrossRefGoogle Scholar
  59. Zhao, S. Q., Zhou, D. C., Zhu, C., Qu, W. Y., Zhao, J. J., Sun, Y., Huang, D., Wu, W., & Liu, S. (2015a). Rates and patterns of urban expansion in China’s 32 major cities over the past three decades. Landscape Ecology, 30, 1541–1559.CrossRefGoogle Scholar
  60. Zhao, S. Q., Zhou, D., Zhu, C., Sun, Y., Wu, W., & Liu, S. (2015b). Spatial and temporal dimensions of urban expansion in China. Environmental Science & Technology, 49, 9600–9609.CrossRefGoogle Scholar
  61. Zhao, S. Q., Liu, S., Xu, C., Yuan, W., Yan, W., Zhao, M., Sun, Y., Henebry, G. M., & Fang, J. Y. (2018). Contemporary evolution and scaling of 32 major cities in China. Ecological Applications, 28, 1655–1668.CrossRefGoogle Scholar
  62. Zhou, D. C., Zhao, S. Q., Liu, S. G., & Zhang, L. X. (2014). Spatiotemporal trends of terrestrial vegetation activity along the urban development intensity gradient in China’s 32 major cities. Science of the Total Environment, 488, 136–145.CrossRefGoogle Scholar
  63. Zhuo, L., Ichinose, T., Zheng, J., Chen, J., Shi, P. J., & Li, X. (2009). Modelling the population density of China at the pixel level based on DMSP/OLS non-radiance-calibrated night-time light images. International Journal of Remote Sensing, 30, 1003–1018.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.College of Urban and Environmental Sciences and Key Laboratory for Earth Surface Processes of the Ministry of EducationPeking UniversityBeijingChina
  2. 2.Department of Geography, Environment, and Spatial Sciences and Center for Global Change and Earth Observations (CGCEO)Michigan State UniversityEast LansingUSA

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