Dynamic community division is crucial for comprehending the interactive structure and mechanism between residents and space. Currently, there is insufficient research on dynamic community changes in travel networks regarding travel distance. This study utilizes residents’ travel trajectories to construct a multi-layer network based on travel purposes. The study establishes methods for identifying travel network communities and analyzing community evolution influence. Dynamic characteristics of travel network communities at different distances are extracted. Results show that within 3–7 km travel distance, different travel network communities exhibit continuous spatial aggregation, whereas beyond 11 km, heterogeneity is observed. Moreover, communities within 3-7 km experience drastic dynamic evolution, whereas beyond 7 km, they tend to stabilize, forming a large-scale and fixed travel mode. This study enriches the dynamic interaction research of functional space from the perspective of distance, and the experimental results provide an effective reference for urban planning and management.

Fast urbanization brings great challenges to sustainable development goals, such as excessive exploitation and population explosion. Classical cellular automata (CA) have been widely used to independently simulate the change of spatial features, i.e. land use, population, economic production, etc. However, most CA models rely on historical data as static driving factors to simulate future scenarios while ignoring the inter-wined influences among multiple features in the development process. To address this issue, this study proposes a spatial cooperative simulation (SCS) approach to simulate the land use, population, and economy changes. The SCS approach starts with a separate CA model to obtain the initial scenes of each feature. Then, the simulation results of each other two features are used as dynamically updated driving factors, rather than the static historical data, to capture the inter-wined influence of multiple features during the development process. This step is iteratively performed until the changes of each feature converge and the final simulation results will be reported. The simulation experiment in Greater Bay Area demonstrates that the SCS approach can well capture the simultaneous development process and outperforms baseline approaches. The SCS approach is capable of forecasting future development scenarios and facilitates spatial planning and infrastructure synergies.

Understanding population distribution has excellent applications for planning and provision of municipal services. This study aims to explore the space-time structure of population distribution with area-level mobile phone data. We discuss a kind of Bayesian hierarchical models, fitted by Markov chain Monte Carlo simulation, that combines the overall spatial pattern and temporal trends as well as the departures from these stable components. We carry out an empirical study in Shenzhen, China, using the area-level mobile phone users in 24 hours. The results indicate that the estimation of the overall spatial pattern is not deteriorated when using a sophisticated spatio-temporal model. The temporal trend exhibits a reasonable fluctuation during the study period. Then we apply two rules to detect areas showing unstable trends of population fluctuation based on the posterior probabilities of the space-time interactions. We also include the population statistics and indices for mixed-use to explore the spatial pattern of population fluctuation. Our findings confirm that the Bayesian spatio-temporal model can enhance the understanding of the space-time variability of population distribution using mobile phone data. Further research should examine the spatial nonstationary effects of explanatory factors on mobile phone-based population fluctuation.