Our understanding of the spatial structure of cities has been traditionally shaped by the availability of static data. In the last few years, thanks to improvements in geospatial technology as well as computing storage and power, there has been an explosion of geo-referenced data, which monitor cities and urban activities in real time. Although this shift in the data landscape promises to change and augment the way we measure, understand, and act on cities, it poses significant methodological challenges and uncovers substantial gaps in the analytics required. This article contributes in this direction by delivering insights on two fundamental fronts: first, we compare several methods that conceptualize both space and time in rather different ways, highlighting their main advantages and limitations; second and more important, we propose a novel approach the Space-Time Calendar that uncovers, characterizes and visualizes in an explicitly spatial way both fast and slow urban dynamics. We illustrate its advantages using a data set derived from over 2 years of mobile phone activity in Amsterdam (the Netherlands). Our findings highlight the advantages of the Space-Time Calendar approach, but also the benefits of appropriately matching the methodological approach to the nature of the data at hand.