This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
Transitions of Urban–Rural Integration in the Yellow River Basin: Spatiotemporal Heterogeneity and Driving Mechanisms
by
Kangning Ma
Kangning Ma 1,
Shuai Zhang
Shuai Zhang 2,
Zhenxing Jin
Zhenxing Jin 1,
Wensheng Yu
Wensheng Yu 1 and
Chengxin Wang
Chengxin Wang 1,3,*
1
College of Geography and Environment, Shandong Normal University, Jinan 250358, China
2
Public Management Teaching and Research Department, Party School of the CPC Shandong Provincial Committee (Shandong Academy of Governance), Jinan 250100, China
3
Key Research Institute of Yellow River Civilization and Sustainable Development & Yellow River Civilization by Provincial and Ministerial Co-Construction of Collaborative Innovation Center, Henan University, Kaifeng 475001, China
*
Author to whom correspondence should be addressed.
Land 2026, 15(7), 1136; https://doi.org/10.3390/land15071136 (registering DOI)
Submission received: 26 May 2026
/
Revised: 19 June 2026
/
Accepted: 23 June 2026
/
Published: 25 June 2026
Abstract
Urban–rural integration (URI) represents a pivotal pathway to realizing sustainable development within urban–rural spatial systems. It is of paramount importance in addressing the challenge of reconciling ecological conservation with high-quality development in the Yellow River Basin. Leveraging panel data from 78 cities in the Yellow River Basin spanning the years 2006–2023, this research constructs an evaluation index system that encompasses five dimensions: population, economy, society, ecology, and space. Through the comprehensive application of kernel density estimation, exploratory spatiotemporal data analysis, and panel quantile regression models, a systematic analysis of the spatiotemporal evolution patterns and transition mechanisms of URI is conducted. The results disclose that URI in the Yellow River Basin demonstrates a trend of “overall enhancement with regional disparities”. From 2006 to 2023, the URI of the basin witnessed an average annual growth rate of 2.86%. Spatially, it presented distinct features: high-level agglomeration in the lower reaches, accelerating-growth path dependency accompanied by internal divergence in the middle reaches, and balanced yet low-level development in the upper reaches. The local spatial evolution of URI follows a pattern characterized as “predominant stability and limited transitions”. In detail, high-level regions sustain their advantages, low-level regions encounter obstacles in achieving breakthroughs, and the spillover effects between adjacent regions remain relatively restricted. The driving mechanisms exhibit significant “phase-spatial” dual heterogeneity, with four distinct patterns identified. In light of these findings, policy recommendations are put forward, including the establishment of a multi-scale, coordinated spatial governance system.
Share and Cite
MDPI and ACS Style
Ma, K.; Zhang, S.; Jin, Z.; Yu, W.; Wang, C.
Transitions of Urban–Rural Integration in the Yellow River Basin: Spatiotemporal Heterogeneity and Driving Mechanisms. Land 2026, 15, 1136.
https://doi.org/10.3390/land15071136
AMA Style
Ma K, Zhang S, Jin Z, Yu W, Wang C.
Transitions of Urban–Rural Integration in the Yellow River Basin: Spatiotemporal Heterogeneity and Driving Mechanisms. Land. 2026; 15(7):1136.
https://doi.org/10.3390/land15071136
Chicago/Turabian Style
Ma, Kangning, Shuai Zhang, Zhenxing Jin, Wensheng Yu, and Chengxin Wang.
2026. "Transitions of Urban–Rural Integration in the Yellow River Basin: Spatiotemporal Heterogeneity and Driving Mechanisms" Land 15, no. 7: 1136.
https://doi.org/10.3390/land15071136
APA Style
Ma, K., Zhang, S., Jin, Z., Yu, W., & Wang, C.
(2026). Transitions of Urban–Rural Integration in the Yellow River Basin: Spatiotemporal Heterogeneity and Driving Mechanisms. Land, 15(7), 1136.
https://doi.org/10.3390/land15071136
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article metric data becomes available approximately 24 hours after publication online.