Work

Large-scale expansion and localized shrinkage of cities in a major grain-producing area: empirical evidence from the Huaihe River Basin

Year: 2025

Type: article

Abstract: During China’s urbanization process, cities located in major grain-producing areas confront a developmental paradox marked by large-scale expansion and localized shrinkage. However, existing research lacks systematic investigation into this complex situation. This study examines the Huaihe River Basin and constructs long time-series nighttime light data from 1995 to 2020. We develop various urban expansion indices and pixel-scale shrinkage models to analyze the spatio-temporal dynamics of large-scale urban expansion and localized shrinkage. The optimal parameter geographical detector model is used to elucidate the underlying driving mechanisms. The results demonstrate: (1) The long time-series nighttime light dataset, corrected through multiple correction models, showed significant improvements in accuracy after integrating population and GDP data, fulfilling the study’s data requirements. (2) The urban scale distribution was gradually idealized, with urban form evolving toward intensification and expansion structure transitioning from fragmentation to concentration in the Huaihe River Basin. (3) Medium-sized cities with a single industrial structure in the midstream of the basin (Lu’an, Zhoukou, and Bozhou) have experienced significant shrinkage, with cyclical variations in the intensity of shrinkage. (4) Natural factors played a restraining role in the early years, and policy regulation and urban scale characteristics jointly influenced population migration, reinforcing the core driving position of the population factor. This study reveals the Huaihe River case of rationalized expansion in major grain-producing areas, providing an effective method for identifying shrinking phenomena in the context of large-scale expansion. The proposed recommendations for industrial diversification and regional coordinated development are of great reference value for ensuring food security (SDG 2) and achieving sustainable urban development (SDG 11).

Source: GIScience & Remote Sensing

DOI: 10.1080/15481603.2025.2548082