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. 2020 Oct 29;10:18642. doi: 10.1038/s41598-020-75697-z

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© The Author(s) 2020

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Spatial distribution of the three key indicators: weighted degree, zone-entropy and coreness-entropy. Left column: local weighted in- and out-degrees; Central column: outgoing or incoming zone-entropy; Right column: outgoing or incoming coreness-entropy. First two rows: weekdays; Bottom two rows: weekends. The subzones are separated into four groups using the 25%, 50% and 75% percentile as cutoffs, thereby giving the “low”, “mid-low”, “mid-high” and “high” categories. Generated with Python (3.7.5), Matplotlib (3.2.1) and GeoPandas (0.7.0).