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

Intensive development of urban systems creates a number of challenges for urban planners and policy makers in order to maintain sustainable growth. Running e cient urban policies requires meaningful urban metrics, which could quantify important urban characteristics including various aspects of an actual human behavior. Since a city size is known to have a major, yet often nonlinear, impact on the human activity, it also becomes important to develop scale-free metrics that capture qualitative city properties, beyond the e↵ects of scale. Recent availability of extensive datasets created by human activity involving digital technologies creates new opportunities in this area. In this paper we propose a novel approach of city scoring and classification based on quantitative scale-free metrics related to economic activity of city residents, as well as domestic and foreign visitors. It is demonstrated on the example of Spain, but the proposed methodology is of a general character. We employ a new source of large-scale ubiquitous data, which consists of anonymized countrywide records of bank card transactions collected by one of the largest Spanish banks. Di↵erent aspects of the classification reveal important properties of Spanish cities, which significantly complement the pattern that might be discovered with the o cial socioeconomic statistics.