Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning
Xiao Zhou, Anastasios Noulas, Cecilia Mascoloo, and Zhongxiang Zhao

TL;DR
This paper leverages WeChat user check-in data and an extended latent Dirichlet allocation model to identify latent cultural interaction patterns in Beijing, aiding urban cultural planning and resource allocation.
Contribution
It introduces a novel temporal LDA model to analyze urban cultural activities and provides high-resolution maps for optimizing cultural resource distribution.
Findings
Identified distinct cultural activity typologies in Beijing.
Mapped areas with cultural resource shortages.
Provided data-driven suggestions for urban cultural planning.
Abstract
Cultural activity is an inherent aspect of urban life and the success of a modern city is largely determined by its capacity to offer generous cultural entertainment to its citizens. To this end, the optimal allocation of cultural establishments and related resources across urban regions becomes of vital importance, as it can reduce financial costs in terms of planning and improve quality of life in the city, more generally. In this paper, we make use of a large longitudinal dataset of user location check-ins from the online social network WeChat to develop a data-driven framework for cultural planning in the city of Beijing. We exploit rich spatio-temporal representations on user activity at cultural venues and use a novel extended version of the traditional latent Dirichlet allocation model that incorporates temporal information to identify latent patterns of urban cultural…
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