Identification of spatial and functional interactions in Beijing based on trajectory data

Abstract

The spatial interactions in a city help us understand how the city spaces are structured. Human needs change constantly throughout the day, leading to changing functions of urban space and spatial interactions. Most existing studies on spatial interaction are confined to static interaction, which does not reflect the dynamic spatial interactions and urban space functions. Taxies are an important means of urban transportation, and their trajectories are time sensitive. The clusters of pick-up and drop-off points of taxies directly reflect the spatiotemporal human mobility pattern in a city, and their implicit semantic information can be used to infer the passengers’ activities. Using Beijing as a case study, this paper employs taxi origin and destination trajectory data to explore the dynamic spatial and functional interaction patterns. First, we apply tensor decomposition to obtain the spatial and temporal patterns of human mobility. Second, geotagged Weibo texts are incorporated to extract the dynamic urban functional areas. Then, we obtain the spatial and functional interactions by trajectory clustering and visualization. The results reveal two daily patterns and four hourly patterns of human mobility. The spatial interactions and the functional interaction reveal the spatial patterns and semantics of human taxi travel behaviors in different time periods.