The regional differences in the Yellow River Basin have increased, and the aggravation of this unbalanced state has seriously restricted the high-quality development of the Yellow River Basin during the accelerated urbanisation that has taken place in recent years. In this regard, heterogeneity in the trends of evolution and the causes of population shrinkage in different regions of the Yellow River Basin can be adopted as targeted countermeasures. The present study uses data from the fifth, sixth, and seventh national censuses and takes the administrative units of different levels in the Yellow River Basin as the object, considering 72 prefecture-level cities within the autonomous prefectures and 595 county-level administrative units in nine provinces (autonomous regions). The population shrinkage coefficient, night light index, bivariate spatial autocorrelation, geographic detectors, and other methods were used, with the final objective of exploring the spatial–temporal distribution pattern and impact mechanism of urban shrinkage from 2000 to 2020. The results of the study show the following: (1) The shrinkage patterns in 2000–2010 (T1) and 2010–2020 (T2) were quite different. From T1 to T2, the shrinkage situation worsened, with the number of districts experiencing population shrinkage increasing from 175 to 373 and the number of districts experiencing continuous night light and shrinkage districts increasing from 146 to 163. (2) The phenomenon of urban shrinkage is spatially scale dependent, with the shrinkage of prefecture-level cities and county-level cities being characterised by both spatial differentiation and spatial nesting relationships. (3) There is a certain inconsistency in the representation of the shrinkage patterns of the nighttime lighting and population data. The nighttime lighting dimension can reflect the structural shrinkage characteristics of the city more accurately and sensitively, and the representation of population loss is lagging. (4) The main impact factors and the intensity of urban shrinkage are the aggravated aging level, the declining level of industrial greening and intensification under market-driven economic structure adjustments, and the decreased natural growth rate in the population structure and public service facilities.

Based on the data of Chinese enterprises that entered the Fortune 500 list in 2015, this paper utilizes the eclectic model to construct the inter-city association network. Using the network analysis method, the spatial connection characteristics of 311 inter-city networks at prefecture level and above and 20 urban agglomerations networks in China are examined, respectively. The research found the following: (1) the overall connectivity of city network is poor, the centripetal concentration is strong, and the network is not complete. The city network structure shows three tendencies, with a concentration in political centers, a concentration in coastal areas, and a concentration in resource-based cities. The external economic dependence of each node city in national city network is high, and the city network structure has distinctly flattening characteristics. (2) Network function of cities is obviously different in multiscale region. Large cities and regional centers have more balanced function systems than the small- and medium-sized cities do. (3) The network of urban agglomerations is characterized by decentralization of power, differentiation of status, and dependence on external connections. The radiation effect of three major urban agglomerations in coastal China is strong, but the radiation effect of other urban agglomerations needs to be strengthened. (4) Both city networks and agglomeration economies have positive impact on economic growth of the city. The economic performance of city networks is differentiated between urban agglomeration cities and nonurban agglomeration cities, as well as between cities of different scale levels. This study provides new evidence for understanding the spatial relations and expansion of Chinese city networks.