Re-examining urban region and inferring regional function based on spatial-temporal interaction

Urban system is shaped by the interactions between different regions and regions planned by the government, then reshaped by human activities and residents' needs. Understanding the changes of regional structure and dynamics of city function based on the residents' movement demand are important to evaluate and adjust the planning and management of urban services and internal structures. This paper constructed a probabilistic factor model on the basis of probabilistic latent semantic analysis and tensor decomposition, for purpose of understanding the higher order interactive population mobility and its impact on urban structure changes. First, a four-dimensional tensor of time (T) x week (W) x origin (O) x destination (D) was constructed to identify the day-to-day activities in three time modes and weekly regularity of weekday/weekend pattern. Then we reclassified the urban regions based on the space clustering formed by the space factor matrix and core tensor. Finally, we further analysed the space-time interaction on different time scales to deduce the actual function and connection strength of each region. Our research shows that the application of individual-based spatial-temporal data in human mobility and space-time interaction study can help to analyse urban spatial structure and understand the actual regional function from a new perspective.