Risks and the prevention and control deployment of COVID-19 infection along the border of Ruili City based on the AHP-entropy weight method

LI Yimin; WU Bowen; LIU Shiyi; LI Yingying; YUAN Jing

doi:10.6046/zrzyyg.2021299

2022 Vol. 34, No. 3

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Article navigation > Remote Sensing for Natural Resources > 2022 > 34(3): 218-226

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LI Yimin, WU Bowen, LIU Shiyi, LI Yingying, YUAN Jing. 2022. Risks and the prevention and control deployment of COVID-19 infection along the border of Ruili City based on the AHP-entropy weight method. Remote Sensing for Natural Resources, 34(3): 218-226. doi: 10.6046/zrzyyg.2021299

Citation:

LI Yimin, WU Bowen, LIU Shiyi, LI Yingying, YUAN Jing. 2022. Risks and the prevention and control deployment of COVID-19 infection along the border of Ruili City based on the AHP-entropy weight method. Remote Sensing for Natural Resources, 34(3): 218-226. doi: 10.6046/zrzyyg.2021299

Risks and the prevention and control deployment of COVID-19 infection along the border of Ruili City based on the AHP-entropy weight method

1. School of Earth Sciences, Yunnan University, Kunming 650500, China；
2. Remote Sensing Geological Engineering Research Center for Domestic Gaofen Satellite of Yunnan Universities, Kunming 650500, China

Received Date: 18 September 2021

Revised Date: 15 September 2022

Publish Date: 21 September 2022

Abstract

Abstract

Although the COVID-19 pandemic has been contained in China presently, it remains a major threat to the international environment. The border areas of China remain at high risk of COVID-19 infection, including Ruili, an important port city on the border between China and Myanmar, which still faces great challenges in pandemic prevention and control along the border. This study analyzed the topographic, traffic, and basic factors of Ruili using the GIS technology, the remote sensing technology, and the AHP-entropy weight method and identified locations with high risks of the pandemic along the border, aiming to achieve more scientific the pandemic prevention and control. The results showed that the high-risk areas in Ruili that need major pandemic prevention and control were in the southwestern and southern zones near the border and had the following characteristics: ① gentle terrain with high fractional vegetation cover; ② convenient transportation and proximity to water systems; ③ high settlement density. To achieve a complete observation of the border, a total of 35 prevention and control points were deployed based on the set covering location model combined with the ArcGIS viewshed analysis. They were divided into 22 primary, 8 secondary, and 5 tertiary prevention and control points, of which the importance of pandemic prevention and control increased gradually. This study can provide references for improving the pandemic prevention and control capacity of border areas.
- prevention and control along the border /
- Ruili City /
- COVID-19 pandemic

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References

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