Multifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainland
Abstract Revealing spatio-temporal evolution regularity in the spatial diffusion of epidemics is helpful for preventing and controlling the spread of epidemics. Based on the real-time COVID-19 datasets by prefecture-level cities, this paper is devoted to exploring the multifractal scaling in spatial...
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| Format: | Article |
| Language: | English |
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Springer Nature
2023-10-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-023-02130-x |
| _version_ | 1797453538373140480 |
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| author | Yuqing Long Yanguang Chen Yajing Li |
| author_facet | Yuqing Long Yanguang Chen Yajing Li |
| author_sort | Yuqing Long |
| collection | DOAJ |
| description | Abstract Revealing spatio-temporal evolution regularity in the spatial diffusion of epidemics is helpful for preventing and controlling the spread of epidemics. Based on the real-time COVID-19 datasets by prefecture-level cities, this paper is devoted to exploring the multifractal scaling in spatial diffusion pattern of COVID-19 pandemic and its evolution characteristics in Chinese mainland. The ArcGIS technology and box-counting method are employed to extract spatial data and the least square regression based on rescaling probability (μ-weight method) is used to calculate fractal parameters. The results show multifractal distribution of COVID-19 pandemic in China. The generalized correlation dimension spectrums are inverse S-shaped curves, but the fractal dimension values significantly exceed the Euclidean dimension of embedding space when moment order q«0. The local singularity spectrums are asymmetric unimodal curves, which slant to right. The fractal dimension growth curves are shown as quasi S-shaped curves. From these spectrums and growth curves, the main conclusions can be drawn as follows: First, self-similar patterns developed in the process of COVID-19 pandemic, which seems to be dominated by multifractal scaling law. Second, the spatial pattern of COVID-19 across China can be characterized by global clustering with local disordered diffusion. Third, the spatial diffusion process of COVID-19 in China experienced four stages, i.e., initial stage, the rapid diffusion stage, the hierarchical diffusion stage, and finally the contraction stage. This study suggests that multifractal theory can be utilized to characterize spatio-temporal diffusion of COVID-19 pandemic, and the case analyses may be instructive for further exploring natural laws of spatial diffusion. |
| first_indexed | 2024-03-09T15:24:19Z |
| format | Article |
| id | doaj.art-5bbd87ad1fab44a5a8a0e4eed6e76de9 |
| institution | Directory Open Access Journal |
| issn | 2662-9992 |
| language | English |
| last_indexed | 2024-03-09T15:24:19Z |
| publishDate | 2023-10-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Humanities & Social Sciences Communications |
| spelling | doaj.art-5bbd87ad1fab44a5a8a0e4eed6e76de92023-11-26T12:38:27ZengSpringer NatureHumanities & Social Sciences Communications2662-99922023-10-0110111310.1057/s41599-023-02130-xMultifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainlandYuqing Long0Yanguang Chen1Yajing Li2Department of Geography, College of Urban and Environmental Sciences, Peking UniversityDepartment of Geography, College of Urban and Environmental Sciences, Peking UniversityDepartment of Geography, College of Urban and Environmental Sciences, Peking UniversityAbstract Revealing spatio-temporal evolution regularity in the spatial diffusion of epidemics is helpful for preventing and controlling the spread of epidemics. Based on the real-time COVID-19 datasets by prefecture-level cities, this paper is devoted to exploring the multifractal scaling in spatial diffusion pattern of COVID-19 pandemic and its evolution characteristics in Chinese mainland. The ArcGIS technology and box-counting method are employed to extract spatial data and the least square regression based on rescaling probability (μ-weight method) is used to calculate fractal parameters. The results show multifractal distribution of COVID-19 pandemic in China. The generalized correlation dimension spectrums are inverse S-shaped curves, but the fractal dimension values significantly exceed the Euclidean dimension of embedding space when moment order q«0. The local singularity spectrums are asymmetric unimodal curves, which slant to right. The fractal dimension growth curves are shown as quasi S-shaped curves. From these spectrums and growth curves, the main conclusions can be drawn as follows: First, self-similar patterns developed in the process of COVID-19 pandemic, which seems to be dominated by multifractal scaling law. Second, the spatial pattern of COVID-19 across China can be characterized by global clustering with local disordered diffusion. Third, the spatial diffusion process of COVID-19 in China experienced four stages, i.e., initial stage, the rapid diffusion stage, the hierarchical diffusion stage, and finally the contraction stage. This study suggests that multifractal theory can be utilized to characterize spatio-temporal diffusion of COVID-19 pandemic, and the case analyses may be instructive for further exploring natural laws of spatial diffusion.https://doi.org/10.1057/s41599-023-02130-x |
| spellingShingle | Yuqing Long Yanguang Chen Yajing Li Multifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainland Humanities & Social Sciences Communications |
| title | Multifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainland |
| title_full | Multifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainland |
| title_fullStr | Multifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainland |
| title_full_unstemmed | Multifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainland |
| title_short | Multifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainland |
| title_sort | multifractal scaling analyses of the spatial diffusion pattern of covid 19 pandemic in chinese mainland |
| url | https://doi.org/10.1057/s41599-023-02130-x |
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