新冠疫情与航空运输规模的空间非平稳关系研究

Research on Spatial Heterogeneity in the Relation Between Air Transport and COVID-19

以我国通航城市为研究区,选取2020年初的疫情数据和航空运输网络数据作为实验数据,并从人口流动、区域人口特征和医疗水平3个维度选取了多个解释变量以提高模型拟合精度,构建了基于属性空间距离的情景化多尺度地理加权回归模型(CMGWR),分析了各因素对疫情的空间分异影响。结果表明,相较于传统GWR和MGWR模型,CMGWR模型对于疫情影响因素作用程度的空间分异特征具有更强的解释能力;疫情与航空运输规模的关联具有较强的空间异质性,且关联度较高的地区均为区域航空枢纽,其他因素对疫情影响力较大的范围也以区域核心城市为主。

Taking the cities with airports as the research area,we used the epidemic data and air transport network data in early 2020,and five explanatory variables from population flow,regional demographic characteristics and medical level improving the model fitting accuracy to build a contextualized multi-scale geographically weighted regression model(CMGWR)based on the distance in attribute space.And then,we analyzed the spatial heterogenous impact of factors on the epidemic.The results show that the CMGWR model can better explain the spatial heterogeneity in the influence factors of the epidemic than the traditional GWR and MGWR models.The association between the epidemic and the scale of air transport has strong spatial heterogeneity,and the areas with higher associations are all regional air hubs.Other factors that have a strong influence on the epidemic are also core cities in the regions.