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Shrinking cities have become a global concern, exhibiting diverse spatial patterns with distinct causes and varying impacts on urban development. Previous studies typically simplified the spatial patterns of shrinking cities into single categories such as global, peripheral, central, local, or scattered shrinkage. However, in practice, a city may experience multiple shrinkage patterns simultaneously. This study proposes a novel classification system to delineate composite spatial patterns, encompassing peripheral-scattered, central-scattered, and peripheral-central-scattered shrinkage. An enhanced method is subsequently developed to identify shrinking cities and their spatial patterns using Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data. This method addresses the limitations of applying uniform thresholds across regions with spatial heterogeneity and relying solely on comparisons between the first and last years, achieving 79.57% overall accuracy. The results indicate that 1,013 natural cities experienced shrinkage, of which 597 exhibit composite spatial patterns. Smaller cities are more likely to exhibit peripheral-central-scattered shrinkage owing to limited economic diversity and compact urban structures, whereas larger cities tend to exhibit peripheral-scattered shrinkage, owing to the stability and vitality of their central areas. The newly identified inverse-scattered shrinkage pattern, mainly observed in smaller cities, presents distinctive characteristics that offer opportunities for targeted policy interventions, such as stabilizing key growth areas and revitalizing shrinking regions through infrastructure development and economic diversification. Overall, this study enhances the understanding of shrinkage spatial patterns and highlights the complexity of urban shrinkage in China, offering valuable insights for policymakers to formulate targeted strategies for high-quality urban development.
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