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Comparative study of quantitative identification methods for peri-urban areas based on a multi-indicator system.

Zhen Shi1, Manshu Liu2, Ying Wang3

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This study compares methods for identifying peri-urban areas (PUAs), finding the Multilayer Perceptron most accurate. Spatial patterns reveal more PUAs in plains than mountains, offering insights for urban planning.

Keywords:
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Area of Science:

  • Urban and Regional Planning
  • Geographic Information Systems
  • Spatial Analysis

Background:

  • Peri-urbanisation is a global phenomenon, leading to diverse quantitative methods for identifying peri-urban areas (PUAs).
  • Comparing these methods within a single study area is crucial for understanding their performance and applicability.
  • Zhengzhou City, China, serves as the case study for this comparative analysis.

Purpose of the Study:

  • To quantitatively compare the identification results of different PUA identification methods.
  • To analyze the spatial patterns and characteristics of PUAs identified by these methods.
  • To provide references for selecting appropriate PUA identification techniques and informing urban planning.

Main Methods:

  • Comparative analysis of three quantitative methods: Threshold Method, Breakpoint Clustering, and Multilayer Perceptron.
  • Application of these methods to identify PUAs in Zhengzhou City, China.
  • Spatial pattern analysis of the identified PUAs.

Main Results:

  • The Threshold Method identified the largest area of PUAs but exhibited the lowest accuracy.
  • The Multilayer Perceptron demonstrated higher accuracy than Breakpoint Clustering.
  • PUAs were more prevalent in the northeastern plains compared to the southwestern mountainous regions of Zhengzhou, exhibiting ring-shaped, belt-shaped, and patch-shaped forms.

Conclusions:

  • The choice of PUA identification method significantly impacts results and accuracy.
  • Spatial patterns of PUAs are influenced by topography, urban expansion, and planning policies.
  • Findings support informed selection of PUA identification methods for future research and urban development strategies.