Learning Urban Nightlife Routines from Mobile Data.

The use of smartphone sensing for public health studies is appealing to understand routines. We present an approach to learn nightlife routines in a smartphone sensing dataset volunteered by 184 young people (1586 weekend nights with location data captured between 8PM and 4AM.) Human activity is represented at two levels, namely as the types of places visited and as the areas of the city where those places are. Routines extracted with two topic models (Latent Dirichlet Allocation and Hierarchical Dirichlet Process) are semantically meaningful and represent different moments of the weekend night, depicting activities such as pub crawling. The inference capacity of the routine representation is demonstrated with two classification tasks of value for alcohol research (alcohol consumption throughout the night, and heavy alcohol consumption.) The results suggest that nightlife routine mining could be used as a complementary tool to traditional survey-based methods in public health studies, and also inform other institutional actors interested in understanding and supporting youth well-being.