China’s urbanization has attracted many scholars’ attention due to its significant impact on socioeconomic sustainability. Many studies have explored the spatial pattern and effects of the factors influencing urban expansion. However, the spatiotemporal dynamics integrating spatial and temporal dimensions and the spatial scales of the influencing factors are always ignored. This study applied the framework of exploratory space–time data analysis (ESTDA) to investigate the spatiotemporal dynamics of urban expansion across 342 cities in China from 1990 to 2017 and, further, used multiscale geographically weighted regression (MGWR) to estimate the effects of influencing factors on urban expansion. We found that urban expansion had an obvious south–north division, and yet the effects of influencing factors usually showed an east–west division. We also found that the dynamic local spatial dependency of urban expansion was accompanied by a volatile coevolution process and inclined to transfer from heterogeneity to homogeneity, and homogeneity tended to be stable. The coevolution of urban expansion between cities and other neighboring ones became stronger with increases in time and regional integration. These findings support the use of customized urban planning for specific regions in different spatial dependence to improve land-use efficiency and coordinate regional development.

Since the 1980s, rapid urbanization in China has been accompanied by city shrinkage. Identifying shrinking cities and clarifying the spatial and temporal patterns are of great significance for formulating policies and realizing smart shrinkage. City shrinkage characterized by population loss is a difficult challenge for urban planning and regional development policy-making. This paper uses 2012–2020 nighttime light (NTL) data to identify the spatial and temporal distribution patterns of shrinking cities in China and excavates the shrinking cities’ trend of agglomeration and dispersion further. The following results are obtained. (1) About 34.9% of prefecture-level cities are shrinking across the country but most severely in northeast and northwest China; (2) the number of shrinking cities fluctuates over time (2015 and 2020 are the peak shrinkage years). Shrinking cities in China show a northeast-to-southwest spatial distribution. (3) From 2012 to 2020, the aggregation degree of shrinkage continuously decreased (Low-Low) and the aggregation degree of growth continuously increased (High-High), indicating that shrinkage in northeast China was slightly alleviated and that the radiative effect of the growth pole was further enhanced. These findings help us better understand the trend of city shrinkage in China. Future work needs to be focused on the potential causes of the shrinkage. Furthermore, long-term trends also need to be investigated.

Urban commercial districts (UCDs) are the concentrated areas for commercial activities in a city, which provide shopping, leisure, business, and other functions. Urban planners usually face problems in how to plan and design UCDs. The layout of UCDs should not only be appropriately concentrated to realize economic benefits, but should also be properly dispersed to accommodate the distribution of the population. Using Beijing as a case study, this study conducted research into UCDs from a microscopic perspective by utilizing open source big data. A recognition and classification method of UCDs was proposed based on the data of POI and road networks. The proposed model combines Huff’s model and the Voronoi method to analyze how various UCDs should be distributed within a city according to the spatial pattern of the population. The results showed that different kinds of UCDs had different spatial distribution features. Problems were also found, for example, UCDs on the urban outskirts served a large population; there were limitations to the spatial distribution of UCDs in the downtown area; and there was incongruity between the UCD types and the population layout. Based on these findings, suggestions regarding the optimization of the urban commercial spatial structure were also put forward.

Railway is a fundamental transportation mode for medium and long-distance travel in China. China’s railway transport network (CRTN) has become increasingly complex. Clarifying the structure of the CRTN and its robustness to failures is important for ensuring safe operations. This paper uses train schedule data to construct a railway physical network (RPN) and a train service network (TSN), and proposes a method to simulate the CRTN change processes under different attack strategies, clarify its robustness, and identify its backbone. The results show: First, the RPN is a typical scale-free and small-world network, while the TSN presents a complex hierarchical structure; Second, the RPN is robust to random attacks but vulnerable to targeted attacks, and attacks based on the betweenness centrality as evaluated in the RPN is the most effective mode; Third, the backbone network consists 62 cities including Beijing, Tianjin, and Shijiazhuang.