A constraint-based approach for identifying the urban–rural fringe of polycentric cities using multi-sourced data

ABSTRACT

Studies on urban–rural fringes, which represent regions facing various urbanization problems caused by rapid expansion, have steadily increased in recent years. However, problems persist in the quantitative delimitation of such regions. Based on the characteristics of abrupt urbanization-level changes in urban–rural fringe areas, we propose a constraint-based method in this study to detect the urban–rural fringes of cities with a spatial polycentric structure of ‘Main center–Subcenter’ based on data from multiple sources. We used the proposed approach to delimitate the fringe areas of Jiangyin and Zhangjiagang and identify their urban main center and subcenter pre-defined by their city master plans, towns, and rural hinterlands. Comparison of the identified results of different single urbanization indices, a single detection center, kernel density estimation, and a single constraint revealed that the patch density and Shannon’s diversity index of the proposed method were higher in urban–rural fringes and smaller in city centers and rural hinterlands. This suggests that the landscape of urban–rural fringes delimitated by the proposed method is more fragmented, diverse, and complicated, thereby performing better. This study is significant for future urban spatial analysis, planning, and management.

KEYWORDS:

• Comprehensive urbanization index
• spatial continuous wavelet transform
• constraint-based boundary recognition method
• urban–rural fringes

Acknowledgments

We would like to express our sincerest gratitude to editor Prof. May Yuan and Prof. Bo Huang and the anonymous reviewers, for their insightful comments and feedbacks, especially during all this chaos caused by Covid-19.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available at http://doi.org/10.6084/m9.figshare.11493678.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41671392,41871297]; The Foundation of Key Lab of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education [NO.2020VGE04]; Research and Innovation Program for Postgraduates in Jiangsu Province [KYCX20_1178].

Notes on contributors

Jing Yang

Jing Yang is a PhD candidate in the School of Geography at Nanjing Normal University. Her research focuses on urban computing, urban planning, geographic information science and cellular automata.

Jingwen Dong

Jingwen Dong is a postgraduate student in the School of Geography at Nanjing Normal University. Her research focuses on land use simulation.

Yizhong Sun

Yizhong Sun is currently a professor in the School of Geography at Nanjing Normal University. His research focuses on geographic information science, cellular automata, urban planning and spatio-temporal data mining.

Jie Zhu

Jie Zhu is currently a lecture in the College of Civil Engineering at Nanjing Forestry University. His research focuses on clustering algorithm, geographic information science and cellular automata.

Yi Huang

Yi Huang is a PhD candidate in the School of Geography at Nanjing Normal University. His research focuses on big data and spatial analysis.

Sheng Yang

Sheng Yang is a postgraduate student in the School of Geography at Nanjing Normal University. His research focuses on spatial data mining algorithm.