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. 2019 Nov 15;9:16911. doi: 10.1038/s41598-019-53127-z

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Commuting flows from home to work locations aggregated at the 55 planning areas. The location of the nodes corresponds to the centroid of the areas and their size corresponds to the incoming degree which corresponds the total amount of agents that commutes everyday to that area. In this figure we report only the most significant nodes in terms of incoming flow (i.e. greater than 95th percentile the distribution). (a) We can observe that major hub in the mobile phone data mobility model corresponds to the Central Business District where the majority of the jobs are located. (b) The random mobility mobility has different hubs randomly distributed in the space. (c) The Levy-distribution and (d) the radiation model show similar patterns, with an homogeneous distribution on the territory without significant hubs: however the mobility derived from the radiation model is more aggregated in the central part of the city.