With the fast growth of smart devices and sensor networks, large amounts of data are collected recording location, activity, and mobility of people living in urban environments. Additionally, data generated on location-aware social media provide rich information about places where people spend their time (shopping malls, cafés, parks, etc). The availability of this type of data provides novel opportunities for developing methods for extracting interesting patterns, detecting trends, modeling people's behavior, and eventually building intelligent systems that improve the interaction of citizens with their cities and help them to utilize better the available resources. In this talk we will review some of our work in the area of mining urban data. We formulate and discuss computational problems motivated by applications in detecting events, mining trajectories, modeling city neighborhoods, and recommending locations for groups. 

Invited talk by Aristides Gionis.