关键词: COVID-19, 多元数据, 地理空间疫情风险, 空间自相关, Pearson相关性, 空间分层异质性, 影响因素, 疫情防控

Abstract:

COVID-19 has a spatial transmission risk and poses a threat to the safety and health of the city. Preventing the spread of COVID-19 is therefore the urgent need for society now. The COVID-19 experienced a process that occurred, developed rapidly and stabilized from January 1 to April 11, 2020. Using the initial COVID-19 data for macro-level epidemic risk assessment can provide a certain reference for epidemic prevention and control measures. In this study, by using multiple data, including administrative division data, designated hospital data, epidemic community data, and road traffic data, we proposed a macro-level geospatial risk assessment and validated its effectiveness in China. Based on this, this study also analyzed the construction factors of geospatial risk assessment in order to explore its distribution rules. The following four conclusions showed that: ① Geospatial risk assessment of Covid-19 in China is effective to some extent. ② The spatial distribution of global Moran's I index of geospatial risk in China was 0.758, which had significant spatial agglomeration characteristics. At the same time, the LISA index in different provinces showed spatial differences. Some regions, including Hubei, Henan, Hunan, Jiangxi, Anhui, Zhejiang, Jiangsu, and Shanghai, were identified as high-high clusters and accounted for 25.81% of the provinces in China. The geospatial risk of these provinces was higher. Regions like Qinghai, Tibet, and Xinjiang, with a low degree of geospatial risk, accounted for 9.68% of the provinces in the country. ③ Some indicators, including geographic location indicators, road traffic indicators, medical and health indicators, and epidemic status indicators, were related to the distribution of geospatial risk. According to the statistical Pearson correlation analysis, there were differences in the correlation index R ². In terms of numerical values, the epidemic status indicators, geographic location indicators, road traffic indicators, and medical and health indicators were ranked from highest to lowest. Different secondary factors were composed of four indicators, and they exhibited two effects of positive and negative correlation. Specifically, the factor of geographic distance from Wuhan, the designated hospital density factor, and the resident-hospital geographical distance factor showed significant negative correlations, with R ² of 0.813, 0.545, and 0.436, respectively. However, the remaining factors showed significant positive correlations, including railway network density factor, road network density factor, and epidemic community density factor, and their R ² were 0.751, 0.792, and 0.825, respectively. ④ The components of geospatial risk were intricate and complicated by multiple factors. According to the spatial stratified heterogeneity analysis, we found that there were interactions between different factors. Among them, the resident-hospital geographic distance factor interacted strongly with the railway network density factor and the road network density factor, and their q values were 0.9842 and 0.9837, respectively. This study not only explored the spatial resource allocation of major epidemics in urban management, but also provided a basis for regional spatial prevention and control strategies.

Key words: COVID-19, multivariate data, geospatial epidemic risk, spatial autocorrelation, pearson correlation, spatial stratified heterogeneity, influencing factors, epidemic prevention and control