"Familiar strangers" : an exploration based on non-traditional big data around metro stations : a tale of Beijing

Abstract

Encounter, as a form of city, happens frequently and introduces familiar strangers. Familiar strangers are those whom we meet at the same place regularly in a period but without interactions. They work as one part of our urban life and show similarity in the spatial-temporal regular human movement. Besides, they also work as one kind of social network or one kind of urban environment to build a sense of belonging. They have considerable impacts on community building, urban development, and public transportation improvement. However, due to the limitation of traditional data, few explorations have been conducted about familiar strangers, especially the factors influencing their quantity and distribution. This paper uses the non-traditional big data to explore the association between familiar strangers and their influencing factors around the metro stations in Beijing. The non-traditional data are smartcard data, mobile phone data, and Point-of-Interest data. In the new data context, the concept of familiar strangers is re-defined and conceptualized. As for the influencing factors, they can be identified by the scattered information in existing literature and can be categorized into five dimensions, namely, the population, the built environment, the regularity in travel pattern, individual characteristics, and physical planning and public policy. Among them, only the regularity in travel pattern has been empirically tested. In this paper, an empirical study of familiar strangers, especially their two influencing dimensions: the population and the built environment, is carried out. Different quantitative tests by non-traditional big data show that there are relationships between familiar strangers and their influencing factors. Case studies are conducted to supplement the quantitative tests and to bring about more comprehensive understandings. Those understandings help us build communities, relieve urban problems, and improve public transportation services.