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Cities are living systems where urban infrastructures and their functions aredefined and evolved due to population behaviors. Profiling the cities andfunctional regions has been an important topic in urban design and planning.This paper studies a unique big data set which includes daily movement data oftens of millions of city residents, and develop a visual analytics system,namely UrbanFACET, to discover and visualize the dynamical profiles of multiplecities and their residents. This big user movement data set, acquired frommobile users' agnostic check-ins at thousands of phone APPs, is well utilizedin an integrative study and visualization together with urban structure (e.g.,road network) and POI (Point of Interest) distributions. In particular, wenovelly develop a set of information-theory based metrics to characterize themobility patterns of city areas and groups of residents. These multifacetedmetrics including Fluidity, vibrAncy, Commutation, divErsity, and densiTy(FACET) which categorize and manifest hidden urban functions and behaviors.UrbanFACET system further allows users to visually analyze and compare themetrics over different areas and cities in metropolitan scales. The system isevaluated through both case studies on several big and heavily populatedcities, and user studies involving real-world users.

UrbanFACET: Visually Profiling Cities from Mobile Device Recorded Movement Data of Millions of City Residents