International Journal of Environmental Research and Public Health Article Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China Yongzhu Xiong 1,*, Yunpeng Wang 2 , Feng Chen 3,4 and Mingyong Zhu 1,* 1 School of Geography and Tourism, Jiaying University, Meizhou 514015, China 2 Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China;
[email protected] 3 College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China;
[email protected] 4 Big Data Institute of Digital Natural Disaster Monitoring in Fujian, Xiamen University of Technology, Xiamen 361024, China * Correspondence:
[email protected] (Y.X.);
[email protected] (M.Z.); Tel.: +86-753-218-6956 (Y.X.) Received: 28 April 2020; Accepted: 29 May 2020; Published: 31 May 2020 Abstract: The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people’s lives and socio-economic development. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman’s rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspots and cluster/outlier areas were observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020.