There is evidence that the built environment has an influence on street vitality. However, previous studies seldom assess the direct, indirect, and total effect of multiple environmental elements at the city level. In this study, the features of the street vitality on Xiamen Island are described based on the location-based service Big Data. Xiamen Island is the central urban area of Xiamen, one of the national central cities in China. With the help of multi-source data such as street view images, the condition of design that is difficult to effectively measure with traditional data can be better explored in detail on a macro scale. The built environment is measured through a 5D system at the city level, including Density, Diversity, Design, Destination accessibility, and Distance to transit. Spatial panel Durbin models are constructed to analyze the influence of the built environment on the street vitality on weekdays and weekends, and the direct, indirect, and total effects are evaluated. Results indicate that at the city level, the built environment plays a significant role in promoting street vitality. Functional density is not statistically significant. Most of the elements have spatial effects, except for several indicators in the condition of the design. Compared with the conclusions of previous studies, some indicators have different effects on different spatial scales. For instance, on the micro scale, greening can enhance the attractiveness of streets. However, on the macro scale, too much greening brings fewer functions along the street, which inhibits the street vitality. The condition of design has the greatest effect, followed by destination accessibility. The differences in the influences of weekdays and weekends are mainly caused by commuting behaviors. Most of the built environment elements have stronger effects on weekends, indicating that people interact with the environment more easily during this period.

A successful built environment is assumed to encourage street vitality in the time and space dimensions. The availability of mobile location data has made it possible to measure street vitality from a large-scale and multiperiod perspective. We used the mobile location data recorded in West Nanjing Road and the surrounding streets in Shanghai as a proxy for street activity, and introduced intensity and instability as indicators of street vitality to test whether there is still a correlation between street vitality and built environment in high-density cities, and whether there are applicable conditions. The results show that for spatial units with higher intensity, the street activities tend to be more unstable. It is more effective to promote street vitality by increasing the diversity of commercial formats. For the streets in high-intensity areas, increasing the mix degree of building functions and the development intensity of the surrounding blocks may not necessarily enhance the street vitality. The design of the external spaces is always an effective measure to maintain continuous vitality. Subway stations play a significant role in promoting street vitality.

The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. During the Chinese New Year holiday, population outflow from Wuhan induced the spread of the epidemic to other cities in China. This study analyzed massive intercity movement data from Baidu and epidemic data to study how intercity population outflows affected the spatiotemporal spread of the epidemic. This study further investigated how urban factors influenced the spatiotemporal spread of COVID-19. The analysis indicates that intercity movement was an important factor in the spread of the epidemic in China, and the impact of intercity movement on the spread was heterogeneous across different classes of cities. The spread of the epidemic also varied among cities and was affected by urban factors including the total population, population density, and gross domestic product (GDP). The findings have implications for public health management. Mega-cities should consider tougher measures to contain the spread of the epidemic compared with other cities. It is of great significance for policymakers in any nation to assess the potential risk of epidemics and make cautious plans ahead of time.

The increasing popularity of intercity commuting is affecting regional development and people’s lifestyles. A key approach to addressing the challenges brought about by intercity commuting is analyzing its determinants. Although spatial nonstationarity seems inevitable, or at least worth examining in spatial analysis and modeling, the global perspective was commonly employed to explore the determinants of intercity commuting flows in previous studies, which might result in inaccurate estimation. This paper aims to interpret intercity commuting flows from Suzhou to Shanghai in the Yangtze River Delta region. For this purpose, mobile signaling data was used to capture human movement trajectories, and multi-source big data was used to evaluate social-economic determinants. Negative binomial (NB) regression and spatially weighted interaction models (SWIM) were applied to select significant determinants and identify their spatial nonstationarity. The results show that the following determinants are significant: (1) commuting time, (2) scale of producer services in workplace, (3) scale of non-producer services in residence, (4) housing supply in residence, (5) year of construction in residence, and (6) housing price in residence. In addition, all six significant determinants exhibit evident spatial nonstationarity in terms of significance scope and coefficient level. Compared with the geographically weighted regression (GWR), SWIM reveals that the determinants of intercity commuting flows may manifest spatial nonstationarity in both residence and workplace areas, which might deepen our understanding of the spatial nonstationarity of OD flows.

Regarding whether a polycentric urban spatial structure is a sustainable urban development model, various studies use different employment center identification methods, resulting in inconsistent conclusions. This paper takes Hangzhou, Wuhan, and Nanning, three Chinese cities at different stages of development as cases, and adopts various methods to identify employment centers to compares the polycentric spatial structure differences from morphological and functional dimensions, and puts forward a multi-dimensional perspective for analyzing the characteristics of the polycentric spatial structure. The results show that the polycentric model of GL_SC threshold method has the relatively good explanatory power, which can best represent the characteristics of urban development in China. The explanatory power of the polycentric model of the Wuhan commuter flow method (adjusted R-squared value is 0.37) is slightly weaker than that of the GL_SC threshold method (adjusted R-squared value is 0.40), indicating that the agglomeration development forces are equivalent to the decentralized development forces and it is a balanced polycentric development structure. In Hangzhou and Nanning, the GL_SC threshold method (adjusted R-squared values are 0.28 and 0.36, respectively) is stronger than the commuter flow method (adjusted R-squared values are 0.19 and 0.33, respectively), which reflects the strong characteristics of agglomeration polycentric development. However, from the comparison of the number and distribution of employment center identification, the polycentric development characteristics of Hangzhou are stronger than those of Nanning. The comparison of the identification results of the morphological and functional dimensions shows that the two dimensions as a unified evaluation process can more fully reflect the characteristics of an urban polycentric development structure, thereby providing preliminary research support for the study of the impact of polycentric structure on economic development, air pollution, work and residential commuting, etc.