The volume, velocity, and variety at which data are now becoming available allow us to study urban environments based on human behavior at a spatial, temporal, and thematic granularity that was not achievable until now. Such data-driven approaches opens up additional, complementary perspectives on how urban systems function, especially if they are based on User-Generated Content (UGC). While the data sources, e.g., social media, introduce specific biases, they also open up new possibilities for scientists and the broader public. For instance, they provide answers to questions that previously could only be addressed by complex simulations or extensive human participant surveys. Unfortunately, many of the required datasets are locked in data silos that are only accessible via restricted APIs. Even if these data could be fully accessed, their na¨ıve processing and visualization would surpass the abilities of modern computer architectures. Finally, the established place schemata used to study urban spaces differ substantially from UGC-based Point of Interest (POI) schemata. In this work, we present a multi-granular, datadriven, and theory-informed approach that addressed the key issues outlined above by introducing the theoretical and technical framework to interactively explore the pulse of a city based on social media.