. The visualization interface of UrbanFACET: (a) the selection on city and time; (b) the selection of mobility metrics displayed on the map, and the visualization options; (c) detailed information on the selected region (the whole city by default if nothing is selected); (d) probability density function of the displayed mobility metric; (e) average probability distribution on POI/DIV classes to compute the mobility metric; (f) the spatial visualization of mobility metrics (vibrancy in this case); (g) legend and controller of the map. Abstract-Cities are living systems where urban infrastructures and their functions are defined and evolved due to population behaviors. Profiling the cities and functional regions has been an important topic in urban design and planning. This paper studies a unique big data set which includes daily movement data of tens of millions of city residents, and develop a visual analytics system, namely UrbanFACET, to discover and visualize the dynamical profiles of multiple cities and their residents. This big user movement data set, acquired from mobile users' agnostic check-ins at thousands of phone APPs, is well utilized in an integrative study and visualization together with urban structure (e.g., road network) and POI (Point of Interest) distributions. In particular, we novelly develop a set of information-theory based metrics to characterize the mobility patterns of city areas and groups of residents. These multifaceted metrics 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 the metrics over different areas and cities in metropolitan scales. The system is evaluated through both case studies on several big and heavily populated cities, and user studies involving real-world users.
Shi et al. REF classified and expressed hidden urban functions and behaviors from the aspects of urban mobility, vitality, communication, diversity and density, and designed a system that allows users to visually analyze and compare indicators of different regions and cities on a city scale.
UrbanFACET: Visually Profiling Cities from Mobile Device Recorded Movement Data of Millions of City Residents
ArXiv
ArXiv
Computer Science