Urban public spaces facilitate social interactions between people, reflecting the shifting functionality of spaces. There is no commonly-held consensus on the quantification methods for the dynamic interplay between spatial geometry, urban movement, and face-to-face encounters. Using anonymized social media check-in records from Shanghai, China, this study proposes pipelines for quantifying physical face-to-face encounter potential patterns through public space networks between local and non-local residents sensed by social media over time from space to space, in which social difference, cognitive cost, and time remoteness are integrated as the physical co-presence intensity index. This illustrates the spatiotemporally different ways in which the built environment binds various groups of space users configurationally via urban streets. The variation in face-to-face interaction patterns captures the fine-resolution patterns of urban flows and a new definition of street hierarchy, illustrating how urban public space systems deliver physical meeting opportunities and shape the spatial rhythms of human behavior from the public to the private. The shifting encounter potentials through streets are recognized as reflections of urban centrality structures with social interactions that are spatiotemporally varying, projected in the configurations of urban forms and functions. The results indicate that the occurrence probability of face-to-face encounters is more geometrically scaled than predicted based on the co-location probability of two people using metric distance alone. By adding temporal and social dimensions to urban morphology studies, and the field of space syntax research in particular, we suggest a new approach of analyzing the temporal urban centrality structures of the physical interaction potentials based on trajectory data, which is sensitive to the transformation of the spatial grid. It sheds light on how to adopt urban design as a social instrument to facilitate the dynamically changing social interaction potential in the new data environment, thereby enhancing spatial functionality and the social well-being.