Dynamics of China’s natural cities and their living structures derived from nighttime lights and populated grids

The term “natural cities” refers to human settlements or human activities in general that are naturally or objectively defined. In this study, we utilized the head/tail breaks method to identify natural cities and characterize their living structures in mainland China from 2000 to 2020. This identification was based on nighttime light (NTL) and gridded population distribution (GPD) data, which, respectively, indicate urban areas and residential settlements. Furthermore, we explicitly identified the evolutions of two distinct categories of natural cities and the dynamics of their inherent living structures. Our objective was to verify that the head/tail breaks method is a powerful approach to deriving natural cities that signify various types of human settlements from diverse data sources, while also effectively characterizing their living structures. Also, this article contributes a novel perspective to examine the inequality between the urban-area expansion and the evolution of residential settlements in urban areas (or urban settlements). Our findings reveal a substantial increase in both the number and sizes of urban areas over time. However, there was an intriguing trend observed in urban settlements, where an increasing number corresponded to a gradual decrease in size. Additionally, the inequality exhibited regional disparities and rapidly developing regions showed a higher potential to enter a turning year of urban areas surpassing urban settlements. The dynamics of living structures show that urban settlements always had more hierarchical levels, more substructures, a much higher degree of order, and a much more living (orderly or beautiful) structure than urban areas. The urban-area expansion in mainland China was extremely rapid over the past decades, but the evolution of urban settlements was more “natural” than that (i.e., it consistently generated a living structure characterized by a higher degree of order).