Unsupervised Clustering of Cities Using Commercial Air Traffic: A Proxy for Economic Connectivity

This paper proposes a data-driven framework to identify and rank economically connected cities by using commercial air traffic as a proxy for urban economic connectivity. The study is motivated by the limitation of traditional city classifications, which often rely on costly and multidimensional socioeconomic indicators, and by the need for scalable alternatives based on open mobility data. Using daily flight frequencies between 213 cities included in the GaWC classification for the year 2022, we built a time series for each origin–destination pair and unsupervised clustering these temporal profiles. The resulting clusters were used to define the layers of a multiplex network, where each layer represents a different pattern of flight connectivity. City importance was then estimated through Multiplex PageRank, which allows for temporal behavior and multilayer network structure to be combined in a single ranking scheme. Rather than introducing a new standalone algorithm, this paper contributes a reproducible analytical pipeline that integrates time-series clustering with multiplex centrality analysis using open aviation data. The results show that the ranking obtained is broadly aligned with established classifications such as GaWC, supporting the idea that commercial flight dynamics can provide a useful proxy for economic interconnectedness. The proposed approach offers a simple and replicable tool for comparative urban analysis, although the results should be interpreted with caution given the limited post-pandemic period covered by the data.