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Intensive development of urban systems creates a number of challenges forurban planners and policy makers in order to maintain sustainable growth.Running efficient urban policies requires meaningful urban metrics, which couldquantify important urban characteristics including various aspects of an actualhuman behavior. Since a city size is known to have a major, yet oftennonlinear, impact on the human activity, it also becomes important to developscale-free metrics that capture qualitative city properties, beyond the effectsof scale. Recent availability of extensive datasets created by human activityinvolving digital technologies creates new opportunities in this area. In thispaper we propose a novel approach of city scoring and classification based onquantitative scale-free metrics related to economic activity of city residents,as well as domestic and foreign visitors. It is demonstrated on the example ofSpain, but the proposed methodology is of a general character. We employ a newsource of large-scale ubiquitous data, which consists of anonymized countrywiderecords of bank card transactions collected by one of the largest Spanishbanks. Different aspects of the classification reveal important properties ofSpanish cities, which significantly complement the pattern that might bediscovered with the official socioeconomic statistics.

Mining Urban Performance: Scale-Independent Classification of Cities Based on Individual Economic Transactions