Journal of Geographical Sciences ›› 2020, Vol. 30 ›› Issue (2): 297-318.doi: 10.1007/s11442-020-1729-4

• Research Articles • Previous Articles     Next Articles

Urban expansion patterns and their driving forces based on the center of gravity-GTWR model: A case study of the Beijing-Tianjin-Hebei urban agglomeration

WANG Haijun1,2, ZHANG Bin1, LIU Yaolin1,2,3, LIU Yanfang1,2,3, XU Shan4,*(), ZHAO Yuntai5, CHEN Yuchen5, HONG Song1   

  1. 1. School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China
    2. Key Laboratories of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China
    3. Collaborative Innovation Center for Geospatial Information Science, Wuhan University, Wuhan 430079, China
    4. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101; China
    5. China Land Survey and Planning Institute, Beijing 100035, China
  • Received:2018-10-23 Accepted:2018-12-12 Online:2020-02-25 Published:2020-02-21
  • Contact: XU Shan E-mail:xushan@igsnrr.ac.cn
  • About author:Wang Haijun (1972-), PhD and Professor, specialized in geographic simulation, urban planning and land resource evaluation research. E-mail: landgiswhj@163.com
  • Supported by:
    National Natural Science Foundation of China(No.41571384);Land Resources Survey and Evaluation Project of Ministry of Land and Resources of China(No.DCPJ161207-01);Fund for Fostering Talents in Basic Science of National Natural Science Foundation of China(No.J1103409);Key Program of National Natural Science Foundation of China(No.71433008);Programme of Excellent Young Scientists of the Institute of Geographic Sciences and Natural Resources Research, CAS

Abstract:

Research into urban expansion patterns and their driving forces is of great significance for urban agglomeration development planning and decision-making. In this paper, we reveal the multi-dimensional characteristics of urban expansion patterns, based on the intensity index of the urban expansion, the differentiation index of the urban expansion, the fractal dimension index, the land urbanization rate, and the center of gravity model, by taking the Beijing-Tianjin-Hebei (Jing-Jin-Ji) urban agglomeration as an example. We then build the center of gravity-geographically and temporally weighted regression (GTWR) model by coupling the center of gravity model with the GTWR model. Through the analysis of the temporal and spatial patterns and by using the center of gravity-GTWR model, we analyze the driving forces of the urban land expansion and summarize the dominant development modes and core driving forces of the Jing-Jin-Ji urban agglomeration. The results show that: 1) Between 1990 and 2015, the expansion intensity of the Jing-Jin-Ji urban agglomeration showed a down-up-down trend, and the peak period was in 2005-2010. Before 2005, high-speed development took place in Beijing, Tianjin, Baoding, and Langfang; after 2005, rapid development was seen in Xingtai and Handan. 2) Although the barycenter of cities in the Jing-Jin-Ji urban agglomeration has shown a divergent trend, the local interaction between cities has been enhanced, and the driving forces of urban land expansion have shown a characteristic of spatial spillover. 3) The spatial development mode of the Jing-Jin-Ji urban agglomeration has changed from a dual-core development mode to a multi-core development mode, which is made up of three functional cores: the transportation core in the northern part, the economic development core in the central part, and the investment core in the southern part. The synergistic development between each functional core has led to the multi-core development mode. 4) The center of gravity-GTWR model combines the analysis of spatial and temporal nonstationarity with urban spatial interaction, and analyzes the urban land expansion as a space-time dynamic system. The results of this study show that the model is a feasible approach in the analysis of the driving forces of urban land expansion.

Key words: urban land expansion, driving forces, center of gravity, geographically and temporally weighted regression, Jing-Jin-Ji