Territorial space is a valuable resource, and it is the home on which human beings depend for their survival and development. However, due to disorder, excessive and decentralized development, resource constraints are tightening, ecological environment deterioration, regional development imbalance and other prominent problems. Agglomeration development has become the strategic choice of territorial space development in China. Therefore, understanding the agglomeration characteristics of territorial space is helpful for local governments to carry out territorial spatial planning. Based on POI (point of interest) data, the method of spatial point pattern analysis is used to characterize the territorial spatial agglomeration in Ningbo city. We found that the elliptic center of Ningbo’s territorial space is distributed in Haisu district. The whole trend is along the “northwest to southeast” direction, and there is a significant directionality. The entire territorial space presents spatial agglomeration, and the agglomeration characteristics of the commercial space are more significant. Moreover, there is a spatial scale effect on the agglomeration of territorial space, which changes with the geographical distance attenuation and presents an inverted “U” structure. With Sanjiangkou as the core, the agglomeration hotspots of all levels distribute in a gradient manner, and the spatial differentiation of “center-periphery” is obvious. The characteristics of its spatial distribution are “river-oriented” and “sea-oriented”. In conclusion, the locking effect of a single center is still significantly higher than the evacuation “magnetic force” of multiple centers in Ningbo city. In the future, we should effectively disperse the densely clustered areas in the central urban area, and focus on cultivating peripheral sub-centers/groups to enhance the positive interaction between multiple centers.

The important role of the entity economy, especially manufacturing, has been further highlighted after the outbreak of COVID-19. This study fills a research gap on manufacturing in the Wuhan Metropolitan Area by analyzing the spatio-temporal evolution patterns and characteristics of manufacturing, exploring the major location factors causing spatial reconstruction and comparing the effect intensities of the different factors in the manufacturing sector. From 2003 to 2018, the process of industrial suburbanization in the Wuhan Metropolitan Area continued to strengthen and currently the overall spatial pattern of manufacturing in the Wuhan Metropolitan Area is characterized by spreading in metropolitan areas and aggregation in industrial parks. The results of a spatial metering model showed that the dominant factors affecting the layout of manufacturing included innovation and technical service platforms, industrial parks, the number of large enterprises, living convenience, and air quality. However, the effect intensity of the different location factors varied among industries. The findings may help the government to understand the characteristics of agglomeration and spreading in the manufacturing industry and, in accordance with the dominant factors affecting the location of this industry, rationally develop ideas for adjusting the industrial layout in the post-coronavirus age.

Previous studies generally used land use and travel flow to investigate the efficiency of the railway system in Transit-Oriented Development (TOD) cities. Furthermore, to study the association of land use and jobs–housing distribution of commuters, we can find out the potential development of rail commuting. In this research, four railway lines in Wuhan, China, were selected to explore the land use in promoting practical commuting population according to the smart-card data obtained. For land use issues, except the road density and building density, a Normalized Location-Weighted Landscape Index (NLWLI) based on the source – sink theory in landscape ecology was established to assess the jobs–housing land use around rail transit stations. Meanwhile, employment and housing details of commuters around rail transit stations were identified using smart-card data. We found that the generation of commuting flow was affected by building density and the land use of employment in the immediate vicinity of rail transit stations. The distribution of building density and commuters in a mature rail line was roughly a normal distribution. However, due to the inconsistency of land use and jobs – housing distribution around stations, the requirement of the balance of land use and jobs – housing distribution should be reduced at the scale of rail stations in the TOD planning systems. This study is the application of massive smart-card data in the field of urban research. It identifies land use issues that affect rail transit commuting flow, and can help urban planners improve the efficiency of rail transit through planning and design.