The COVID-19 epidemic has spread worldwide, becoming a pandemic. This indicates that the interaction between infected migrants and community facilities played an important role in transmitting COVID-19 at the county level. The infected migrant factor interacted strongly with the community facility factor with the q value of 0.895. Our results showed that: 1) the epidemic exhibited significant positive global spatial autocorrelation high–high spatial clusters were mainly distributed in the Pearl River Estuary region 2) city-level population migration data corroborated with the incidence rate of each city in Hubei showed significant association with confirmed cases 3) in terms of potential factors, infected migrants greatly contributed to the spread of COVID-19, which has strong ability to explain the COVID-19 epidemic besides, the companies, transport services, residential communities, restaurants, and community facilities were also the dominant factors in the spread of the epidemic 4) the combined effect produced by the intersecting factors can increase the explanatory power. We also used a geographical detector to explore infected migrants and socioeconomic factors associated with transmission of COVID-19 in Guangdong. Using the spatial autocorrelation method, we identified high-cluster areas of the epidemic. County-level infected migrants of Guangdong moving from Hubei were calculated by integrating the incidence rate, population migration data of Baidu Qianxi, and the resident population. Data on confirmed cases, population migration, and socioeconomic factors for 121 counties were collected from 1 December 2019 to 17 February 2020, during which there were a total of 1,328 confirmed cases. In this study, we aimed to analyze spatial clusters of the COVID-19 epidemic and explore the effects of population emigration and socioeconomic factors on the epidemic at the county level in Guangdong, China. 3Institute for Geospatial Research and Education, Eastern Michigan University, Ypsilanti, MI, United StatesĬoronavirus disease 2019 (COVID-19) has become a major public health concern worldwide.2Department of Geography, National University of Singapore, Singapore, Singapore.1Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangdong Province Engineering Laboratory for Geographic Spatio-temporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China.those not cited during the following year.Jianhui Xu 1, Yingbin Deng 1, Ji Yang 1 *, Wumeng Huang 1, Yingwei Yan 1,2, Yichun Xie 3, Yong Li 1 and Wenlong Jing 1 Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those documents other than research articles, reviews and conference papers. Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. journal self-citations removed) received by a journal's published documents during the three previous years.Įxternal citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.Įvolution of the number of total citation per document and external citation per document (i.e. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.Įvolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.Įarth and Planetary Sciences (miscellaneous) The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles.
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