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.

This study analyzes the structure of China’s overall railway transport network and its sub-networks of conventional railway and high-speed railway, with the aim to understand the country’s economic spatial structure that is reflected by or underlines the railway transport network. First, the results indicate that compared with developed cities, backward cities not only have fewer train services but also lack a symmetrical transport plan; backward cities tend to connect with developed cities rather than within themselves. Second, the national-level urban hierarchy was established using the proposed algorithm, which helped reveal the economic geography of three economic plates in China. Third, the law of the primate city is not prominent in a large country such as China, which is undergoing regional restructuring with the economic center of gravity shifting to the coastal area while also moving south.

Despite the rising interest in understanding the various uses of space of flows, few studies have combined the traditional static urban development level with dynamic space of flows concepts. In the context of the coordinated development of the urban agglomeration of the Yellow River Ji‐shaped bend (UAYB), this study identifies the hierarchical spatial structure of the UAYB through a combination of Baidu migration big data and traditional data. The following conclusions can be drawn. (1) The cities with the strongest regional comprehensive power are Ordos, Taiyuan, Hohhot, Yinchuan, and Yulin, which cause the UAYB to present a significant “center‐periphery” spatial pattern. (2) The biggest population flows mostly occur between cities in the same province, while interprovincial population flows mainly exist between cities with the strongest comprehensive power. (3) The hierarchical spatial structure of the UAYB forms a multitree structure, with Ordos as the core. (4) The attractiveness of the UAYB is very weak, being only slightly attractive to individual surrounding provinces, while the population outflow index to economically developed areas is high. Several policy implications are proposed, which can provide important insights for planning intercity connections among the UAYB, in order to achieve more coordinated regional development.