Spatialization of user-generated content to uncover the multirelational world city network

In this paper we tackle a fundamental and long-time challenge in urban geography, to uncover a functionally differentiated global city network. To this day, the empirical investigation of a global multifunctional city network remains a challenge given the scarcity of appropriate relational and multifunctional data sources. To overcome this research gap, we present an interdisciplinary network modelling approach that integrates methods from geographic information science with social network analysis, including automated semantic analyses. We apply our modelling framework to a globally available, user-generated database (ie, Wikipedia), still underutilized in urban geography and planning research. The proposed visual analytical investigation of the multifunctional world city network also includes a systematic evaluation to assess the robustness of the proposed approach, and the adequacy of crowd-sourced databases for scientific uses. By example, we discuss economical and political relations of a latent multifunctional global city network which we uncovered with our data-driven approach. Our results not only empirically replicate previously well-established world city network theory, but also generate new research questions about multiple functions of cities, as hypothesized in world city network research. Furthermore, we showcase the potential of coupling text-based user-generated data analysis with geovisual analytics for scientific investigations in urban studies