Classifying urban venue types using temporal popularity patterns: a comparison of methods

Abstract

Urban environments generate rich temporal patterns of activity across diverse venues such as restaurants, bars, and coffee shops. Popularity dynamics over time provide valuable insight into daily routines, collective habits, and cultural differences in urban life. In this study, we compile a large-scale dataset of venue popularity data from cities in Brazil (BR) and the United States (U.S.) and evaluate a wide range of time series classification methods. Our objective is to assess how different approaches capture temporal signatures of urban activity and to examine their robustness across geographic and cultural contexts. The results show that dictionary-based and shapelet methods (e.g., WEASEL-D, RDST, TDE) consistently outperform deep learning approaches, achieving peak 𝐹1-scores above 72%. Incorporating metadata and automated feature extraction (with Time Series Feature Extraction Library – TSFEL) further improves classification accuracy, confirming the value of enriched feature spaces. However, cross-country evaluation reveals substantial challenges in model transferability, with most classifiers suffering severe performance degradation when applied across national contexts. These findings demonstrate that urban popularity patterns contain identifiable temporal signatures that are both classifiable and culturally specific. The study highlights the strengths of dictionary and shapelet-based methods, the benefits of feature engineering, and the limitations of deep learning for this task. Beyond methodological contributions, the results offer practical guidance for urban analytics, recommendation systems, and smart-city applications, where understanding and predicting population behavior over time is essential for effective service delivery and planning.