Identification and Spatiotemporal Evolution Analysis of the Urban–Rural Fringe in Polycentric Cities Based on K-Means Clustering and Multi-Source Data: A Case Study of Chengdu City

Dan Ji, Jian Tian (), Jiahao Zhang, Jian Zeng and Aihemaiti Namaiti ()
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Dan Ji: School of Architecture, Tianjin University, Tianjin 300072, China
Jian Tian: School of Architecture, Tianjin University, Tianjin 300072, China
Jiahao Zhang: School of Architecture, Tianjin University, Tianjin 300072, China
Jian Zeng: School of Architecture, Tianjin University, Tianjin 300072, China
Aihemaiti Namaiti: School of Architecture, Tianjin University, Tianjin 300072, China

Land, 2024, vol. 13, issue 11, 1-26

Abstract: Urban fringe areas, serving as transitional zones between urban and rural landscapes, are characterized by their transitional nature, high dynamics, and spatial heterogeneity. Identifying the extent of an urban–rural fringe (URF) and analyzing its evolutionary characteristics are crucial for urban planning and development. However, limited research exists regarding the identification of a URF and the analysis of its spatiotemporal evolution in polycentric cities. Using Chengdu as a case study, this research employed the K-means clustering method to identify the spatial extent and evolution patterns of the URF in Chengdu from 2010 to 2020 based on the spatiotemporal characteristics of multi-source data. The results indicate that (1) the K-means clustering method can reasonably and efficiently identify URF in polycentric cities; (2) Chengdu exhibited a polycentric urban structure with a “main center-subcenter” pattern, where the URF was adjacent to the main and subcenters, assuming an overall annular wedge shape; (3) there was a significant expansion of the URF in the northeast–southwest direction from 2010 to 2020, accompanied by substantial land use changes. The evolution of the URF was driven by the dual mechanisms of urban suburbanization and rural urbanization, exhibiting characteristics such as singular urban functions, dispersed and chaotic land use, fragmented landscapes, and increasing complexity. This study extended the research on URFs, aiding in the understanding of urban spatial growth patterns and providing decision support for the integrated development of urban and rural areas.

Keywords: urban–rural fringe; identification; spatiotemporal evolution analysis; polycentric city; K-means clustering; multi-source data (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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