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Cultural activity is an inherent aspect of urban life and the success of amodern city is largely determined by its capacity to offer generous culturalentertainment to its citizens. To this end, the optimal allocation of culturalestablishments and related resources across urban regions becomes of vitalimportance, as it can reduce financial costs in terms of planning and improvequality of life in the city, more generally. In this paper, we make use of alarge longitudinal dataset of user location check-ins from the online socialnetwork WeChat to develop a data-driven framework for cultural planning in thecity of Beijing. We exploit rich spatio-temporal representations on useractivity at cultural venues and use a novel extended version of the traditionallatent Dirichlet allocation model that incorporates temporal information toidentify latent patterns of urban cultural interactions. Using thecharacteristic typologies of mobile user cultural activities emitted by themodel, we determine the levels of demand for different types of culturalresources across urban areas. We then compare those with the correspondinglevels of supply as driven by the presence and spatial reach of cultural venuesin local areas to obtain high resolution maps that indicate urban regions withlack of cultural resources, and thus give suggestions for further urbancultural planning and investment optimisation.

Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning