<p>Urban Fringe Areas (UFA) are ecological barriers for cities, and building an Ecological Security Pattern (ESP) in these areas can promote sustainable and high-quality development of the urban environment. This paper took Beijing as an example. It used road network data to divide the grid and trained a model to determine the scope of UFA. This makes the determination of urban fringe areas more accurate. Furthermore, this study proposes an innovative framework for identifying ecological sources, “ecological sensitivity-ecological connectivity” method, and the minimum cumulative resistance model to construct a detailed ESP in these areas. The findings indicated: (1) This paper used road network data to segment the grid, which overcame the drawbacks of the traditional method and improved accuracy and reliability. (2) The quantitative analysis method was employed to construct scientific labeling data, and the urban fringe areas determination model was built and worked well. The UFA of Beijing were mainly in Shunyi, Changping, Tongzhou, Daxing and Fangshan Districts. (3) Ecological sensitivity in Beijing’s UFA was relatively dispersed. A total of 28 ecological sources were identified, covering an area of 31.35km<sup>2</sup>, mainly in the northwest and adjacent to mountainous areas. (4) There were 37 ecological corridors, densely distributed in the northern and southwestern parts of Beijing’s UFA, indicating two distinct partitioned states. The ESP of Beijing’s UFA requires further improvement. These findings provide critical insights for sustainable urban ecological development and the optimization of ESP.</p>

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Optimizing urban development through identification of urban fringe areas and construction of ecological security pattern

  • Yu Zhong,
  • Xia Zhu,
  • Tiange Zhang,
  • Yuanping Liu,
  • Hongpeng Zhao,
  • Cui Jia,
  • Jie Cao

摘要

Urban Fringe Areas (UFA) are ecological barriers for cities, and building an Ecological Security Pattern (ESP) in these areas can promote sustainable and high-quality development of the urban environment. This paper took Beijing as an example. It used road network data to divide the grid and trained a model to determine the scope of UFA. This makes the determination of urban fringe areas more accurate. Furthermore, this study proposes an innovative framework for identifying ecological sources, “ecological sensitivity-ecological connectivity” method, and the minimum cumulative resistance model to construct a detailed ESP in these areas. The findings indicated: (1) This paper used road network data to segment the grid, which overcame the drawbacks of the traditional method and improved accuracy and reliability. (2) The quantitative analysis method was employed to construct scientific labeling data, and the urban fringe areas determination model was built and worked well. The UFA of Beijing were mainly in Shunyi, Changping, Tongzhou, Daxing and Fangshan Districts. (3) Ecological sensitivity in Beijing’s UFA was relatively dispersed. A total of 28 ecological sources were identified, covering an area of 31.35km2, mainly in the northwest and adjacent to mountainous areas. (4) There were 37 ecological corridors, densely distributed in the northern and southwestern parts of Beijing’s UFA, indicating two distinct partitioned states. The ESP of Beijing’s UFA requires further improvement. These findings provide critical insights for sustainable urban ecological development and the optimization of ESP.