About half of humanity lives in urban environments today and that number will grow to 80% by the middle of this century. Cities are thus the loci of resource consumption, of economic activity, and of innovation. Given our increasing ability to collect, transmit, store, and analyze data, there is a great opportunity to better understand cities, and enable them to deliver services efficiently and sustainably while keeping their citizens safe, healthy, prosperous, and well-informed. But making sense of all the data available is hard. Currently, urban data exploration is often limited to confirmatory analyses consisting of batch-oriented queries and the exploration of well-defined questions over specific regions. The lack of interactivity makes this process both time-consuming and cumbersome. This problem is compounded in the presence of big, multivariate spatio-temporal data, which is ubiquitous in urban environments. Another challenge comes from the need to empower social scientists, policy makes and urban residents who lack computer science expertise to leverage these data. In this paper, we give an overview of our recent work on techniques that combine data management and visualization to enable a broad set of users to interactively explore large, spatio-temporal data. We describe a visual query interface that simplifies the process of specifying spatio-temporal queries as well as new indexing technique that enables these queries to be evaluated at interactive rates. We also present a scalable framework that applies computational topology to automatically find interesting data slices so as to help guide users in the exploratory process.