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. 2021 Apr 23;6(1):32. doi: 10.1007/s41109-021-00373-8

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© The Author(s) 2021

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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Fig. 6 — Dynamic Community Detection in China. a Spatial mapping of dynamic communities: The four dynamic communities align geographically with megacities and large industrial complexes. b Frequency of occurrence and size evolution of dynamic communities: The southeastern community (blue) is the least persistent due to the lower PM concentrations in that region. c Methodology illustration during five weeks of 2017, displaying the key events of community evolution tracking. d Dynamic communities’ core nodes may sufficiently reproduce the key spatiotemporal patterns of the analysis