Exploring the Distribution of Urban Socio-Spatial Interactions through the Perspective of Digital Mobile Communication

Cities are among the most complex structures humankind has ever constructed; they are multidimensional entities comprising layers of movements, interactions, and information. The complexity and interconnectedness of interactive urban layers are ever increasing in the contemporary world. The institute of communication has faced a paradigm shift with the widespread use of digital mobile devices. Accordingly, research on cities must consider this phenomenon as it directly affects two of the basic components of the urban network: location and communication. Mobile digital communication has undermined the has undermined the necessity of being present and the need for physical proximity. The effects of this fundamental shift are not well understood in recent urban literature. This research first tries to develop a theoretical framework for addressing the mutual dialectic between urban space and socio-spatial interaction via the perspective of the emerging digital communication and the process of integrating social media into everyday life. Second, the dissertation explores the relationships between levels of accessibility to urban space throughout the spatial grid and the possibility for the establishment of socio-spatial interaction through geo-tagged data (in this case: Twitter). The study aims to contribute to the existing literature by suggesting a new methodological approach for reading the complex socio-spatial network of the city that takes mobile digital interactions into account. Accordingly, the methodological approach of the study comprises two overlapping types of network data: firstly, the graphically-tagged social network data that citizens produced within the urban space, and secondly, the physical accessibility through the space syntax measurements. The results show the significance of local accessibility in predicting geo-tagged Twitter data. The outcome also suggests that, in exploring emerging types of data, it is critical to look at the outliers and patterns of behavior that align with the increasing complexity of the urban structure.