Risk Assessment of COVID-19 Based on Multisource Data From a Geographical Viewpoint

In June 4, 2020, Corona Virus Disease 2019(COVID-19) cases in Wuhan were cleared, and the epidemic situation was basically controlled. Such public safety infectious disease includes influences great pressure on the national economy. At present, some countries and regions in the world are still in epidemic situation, and there is an urgent need to judge the infection situation and travel risk in the region. In a relatively fine scale down to perceive the surrounding situation, and then rational zoning decisions to promote the resumption of production and work. In this study, indicators for the evaluation of COVID-19 epidemic were constructed using multi-sourced data. A computational evaluation of 736 fine-grained grids was performed using the GeoDetector model and the decision tree model. The study found that the risk level in older neighborhoods was much higher than in newer neighborhoods; the population density was the most important determinant of infection; the number of urban people slumped to 37% of that in usual times according to Tencent data after the “city closure”; The model this paper used portrays the major factor in defining low-risk areas and high-risk areas, and offers suggestions and assessment from a geographical perspective to fight COVID-19, thus presenting great practical value.

Abstract licence: CC BY-NC-ND

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