Abstract
How do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained.
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Notes
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The analyzed trajectories are generated from raw GPS data using a tool called M-Atlas [6].
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Acknowledgements
This work is partially supported by the European Community’s H2020 Program under the scheme ‘INFRAIA-1-2014-2015: Research Infrastructures,’ grant agreement #654024 ‘SoBigData: Social Mining and Big Data Ecosystem’.
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Gabrielli, L. et al. (2018). Discovering Mobility Functional Areas: A Mobility Data Analysis Approach. In: Cornelius, S., Coronges, K., Gonçalves, B., Sinatra, R., Vespignani, A. (eds) Complex Networks IX. CompleNet 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-73198-8_27
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DOI: https://doi.org/10.1007/978-3-319-73198-8_27
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