Understanding Human Behavior in Urban Spaces using Social Network Data

Public streams of geo-located social media information have provided researchers with a rich source of information from which to draw patterns of urban-scale human-mobility. However, most of the literature relies on assumptions over the spatial distribution of this data (e.g., by considering only a uniform grid division of space). In this work the authors present a method that does not rely on such assumptions. They followed the social media activity of 24,135 Twitter users from Mexico City over a period of seven months (June 2013 - February 2014). The authors' method clusters user's geo-locations into a 19 zone data-driven division of Mexico City. These results can be interpreted from a graph theory-based perspective, by representing each division as nodes, and the edges between them as the number of people traveling between locations. Graph centrality reveals city's infrastructural key points. Without these gateways the authors can argue that mobility would either be radically transformed or break the city apart.