Urban and regional areas worldwide exhibit a complex and uneven distribution of activities with certain areas attracting more people during different time periods. In this study we systemically classify different parts of the urban area which are most attractive as measured by their ability to attract visitors. A weekly visiting profile is constructed for each travel demand zone and thereafter clustered to identify areas with common attraction patterns. We leverage on the availability of longitudinal individual mobility traces in the form of smart card data transactions. We apply our method to the case study of the multi-modal public trans-port system of the Stockholm urban agglomeration area. The results of our clustering based on the weekly visiting profiles reveal four distinctive types of visiting attraction based on the intensity and temporal distribution of activities performed. The results of this study can be used to inform planners and decision makers about the main activity locations of travellers and how their temporal patterns vary across the metropolitan area and the design of related policies.