Today, 50% of the world's population lives in cities and the number will grow to 70% by 2050.  Cities are thus the loci of economic activity, and will continue to be the source of many of the innovations and novel approaches to the challenges of the 21st century. At the same time, cities are also the cause of looming sustainability problems and face huge challenges around, for example, transportation, resource consumption, housing affordability, and inadequate or aging infrastructure.  The large volumes of urban data currently available, along with vastly increased computing power, open up new opportunities for us to better understand cities. In fact, there are already successful stories that have resulted in better operations, informed planning, improved policies and a better quality of life for citizens. However, analyzing urban data often requires a staggering amount of work, from identifying relevant data sets, cleaning and integrating them, to performing exploratory analyses over complex, spatio-temporal data.

An important long-term goal of our research is to enable interdisciplinary teams to crack the code of cities by freely exploring the vast amounts of data cities generate.  In this talk, we will discuss some of the challenges involved which have led us to pursue new research on data management, data analysis and visualization techniques.  We will present methods and systems we have developed to increase the level of interactivity, scalability, and usability for spatio-temporal analyses.

This work was supported in part by the National Science Foundation, a Google Faculty Research award, the Moore-Sloan Data Science Environment at NYU, IBM Faculty Awards, NYU School of Engineering and Center for Urban Science and Progress.