This study investigates the coupling coordination between population and land urbanization at the county scale in the Yangtze River Delta urban agglomeration. The authors frame population-land coupling as a central concern of new-type urbanization: the spatial and functional matching of population agglomeration and construction land expansion. They note prior macro-level work typically focuses on provinces or urban agglomerations and that county-level heterogeneity has been underexplored. The county is identified as a fundamental unit linking national strategy and grassroots governance and as the main carrier of population and land dynamics.
To address limitations of single-indicator measurement and low spatial resolution in statistical data, the study integrates multiple spatial data sources. Core datasets include VIIRS NTL (night-time light), LandScan population grids, CLCD land cover data, and point-of-interest (POI) data. The authors construct a progressive analytical framework described as fusion–extraction–assessment. This framework is intended to improve spatial precision for county-scale urbanization measurement and to enable mechanistic inference.
The methodological sequence comprises four main components. First, wavelet transform is used to optimize multi-source data fusion, reducing noise and improving the consistency of fused signals. Second, a U-Net deep learning model is applied to fine-extract spatial patterns and temporal evolution of land and population urbanization at refined spatial resolution. Third, a coupling coordination model quantitatively evaluates the degree of matching between population urbanization and land urbanization for the period 2013–2025 at the county level. Fourth, geographic detectors are employed to identify key driving factors and to assess how explanatory power of those factors differs across stages.
The analysis finds that the expansion speed of land urbanization in counties across the Yangtze River Delta significantly exceeds that of population urbanization during the study period. This divergence in growth rates leads to varying degrees of mismatch between population concentration and land development across counties. Despite the mismatch, the overall coupling coordination degree across counties exhibits a steady upward trend from 2013 through 2025, indicating gradual improvement in the alignment of the two dimensions over time.
Spatially, counties show diverse population–land relationships. The authors report a pattern in which the highest coordination or agglomeration initially concentrates in point-like cores—primarily in central cities—and then progressively diffuses outward. Eastern parts of the region experience more pronounced planar diffusion, reflecting spatial spread of coordination beyond core urban centers. Nonetheless, counties across the region display heterogeneous degrees of population–land mismatch, consistent with unbalanced regional development and differing polarization–diffusion effects from core cities.
Using the coupling coordination model, the study maps a spatiotemporal evolution where coordination spreads from localized agglomerations to broader areas. The coupling coordination metric shows a generally rising trend across counties, indicating that although land has expanded faster than population, matching between the two dimensions has improved overall across the study period. The authors emphasize that this pattern varies by county and by subregional location within the Yangtze River Delta.
Geographic detector analysis reveals a stage-characteristic change in dominant drivers of coupling coordination. The identified leading factors for distinct years in the analysis are: foreign investment (2013), economic development level (2017), government expenditure (2021), and industrial structure (2025). The study reports that explanatory power of driving factors differs across periods, indicating a dynamic evolution logic in what most strongly influences county-level population-land coordination at each stage.
The authors argue that refining measurement at the county level and employing multi-source remote sensing fusion enhances both spatial precision and the ability to explain driving mechanisms. By revealing spatiotemporal evolution patterns and stage-specific dominant factors, the study aims to inform county-level spatial governance and the coordinated development of population and land urbanization. The findings underscore the need to consider stage-dependent policy levers—such as managing foreign investment effects, supporting economic development, calibrating government expenditure, and adjusting industrial structure—to improve population–land matching.
The paper presents a methodological framework—wavelet-optimized data fusion, U-Net extraction, coupling coordination assessment, and geographic detector mechanism analysis—applied to counties in the Yangtze River Delta for 2013–2025. Key empirical conclusions are that county land urbanization outpaced population urbanization, generating mismatches; coupling coordination has generally improved over time with spatial diffusion from cores to eastern planar areas; and dominant drivers shift by stage (foreign investment, economic level, government spending, industrial structure). The authors state that this framework and empirical evidence can support more refined county-scale spatial governance and contribute to theory and practice of new-type urbanization.
Notes on data and provenance
The study reports that the underlying data used are publicly archived at the authors' cited repository. Funding sources and declarations of no competing interests are reported in the original article. Detailed methodological parameters, model training specifics, and quantitative statistics were presented in the source article; if specific numeric values or model performance metrics are required, they should be consulted directly in the published paper.