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Quantifying the Spatial Fragmentation Pattern and Its Influencing Factors of Urban Land Use: A Case Study of Pingdingshan City, China

Author

Listed:
  • Li Yue

    (Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, China)

  • Hongbo Zhao

    (Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, China)

  • Xiaoman Xu

    (Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, China)

  • Tianshun Gu

    (Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, China)

  • Zeting Jia

    (Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, China)

Abstract

In the context of rapid urbanization, the phenomenon of spatial fragmentation in Chinese inland central cities is significant. The scientific measurement and evaluation of urban spatial fragmentation are conducive to its transformation, advancement, and sustainable development. Based on the fractal dimension index and Shannon index, this study measures urban spatial fragmentation in terms of form and function, respectively. In addition, multi-scale geographic weighted regression (MGWR) is used to study the influencing factors of spatial fragmentation. The conclusions are as follows: ① the measurement results of spatial form fragmentation and functional fragmentation of urban built-up areas are consistent. The fragmentation degree of the new urban area (new urban district and high-tech district) is higher than that of the old urban areas, and the urban space fragmentation degree around railways and rivers is high. The urban space fragmentation degree of coal resource concentrated distribution areas in the north is lower. The cold spot area of the fragmentation phenomenon appears in the old urban area, and the hot spot area is in the new urban area and along the railway. ② The positive influencing factors of urban spatial fragmentation in Pingdingshan city are the NDVI and the distance from CBD. The negative influencing factor is the number of bus stops per unit area. The DEM and population density have no significant impact on urban fragmentation in Pingdingshan city. ③ Among the variables with significance, its influence has a certain spatial heterogeneity. The spatial scale from small to large is the number of bus stops per unit area, NDVI, and the distance from CBD. The degree of urban fragmentation is very sensitive to the number of bus stops per unit area and the impact scale is quite small. The spatial impacts of the NDVI and the distance from CBD are relatively stable. This study provides a reference and basis for the spatial development of built-up areas of inland central cities and promotes the transformation, advancement, and sustainable development of inland central cities.

Suggested Citation

  • Li Yue & Hongbo Zhao & Xiaoman Xu & Tianshun Gu & Zeting Jia, 2022. "Quantifying the Spatial Fragmentation Pattern and Its Influencing Factors of Urban Land Use: A Case Study of Pingdingshan City, China," Land, MDPI, vol. 11(5), pages 1-15, May.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:686-:d:808390
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    References listed on IDEAS

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    1. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    2. Baldini, Carolina & Marasas, Mariana Edith & Tittonell, Pablo & Drozd, Andrea Alejandra, 2022. "Urban, periurban and horticultural landscapes – Conflict and sustainable planning in La Plata district, Argentina," Land Use Policy, Elsevier, vol. 117(C).
    3. Xiangyang Cao & Yishao Shi & Liangliang Zhou & Tianhui Tao & Qianqian Yang, 2021. "Analysis of Factors Influencing the Urban Carrying Capacity of the Shanghai Metropolis Based on a Multiscale Geographically Weighted Regression (MGWR) Model," Land, MDPI, vol. 10(6), pages 1-19, May.
    4. Eunice Nthambi Jimmy & Javier Martinez & Jeroen Verplanke, 2020. "Spatial Patterns of Residential Fragmentation and Quality of Life in Nairobi City, Kenya," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 15(5), pages 1493-1517, November.
    5. Zoe Slattery & Richard Fenner, 2021. "Spatial Analysis of the Drivers, Characteristics, and Effects of Forest Fragmentation," Sustainability, MDPI, vol. 13(6), pages 1-22, March.
    6. Xin Lao & Hengyu Gu, 2020. "Unveiling various spatial patterns of determinants of hukou transfer intentions in China: A multi‐scale geographically weighted regression approach," Growth and Change, Wiley Blackwell, vol. 51(4), pages 1860-1876, December.
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    2. Hongbo Zhao & Tianshun Gu & Li Yue & Xiaoman Xu, 2022. "Locational Dynamics of Luxury Hotels in Shanghai Metropolis, China: A Spatial-Temporal Perspective," Land, MDPI, vol. 11(10), pages 1-19, September.

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