The Sustainable Response Strategy to COVID-19: Pandemic Urban Zoning Based on Multimodal Transport Data

TL;DR

This paper introduces a novel, low-cost urban zoning method based on multimodal transport data and community detection algorithms to optimize COVID-19 pandemic control while minimizing social disruption.

Contribution

It develops a refined pandemic zoning approach using multimodal transport data and improved community detection, addressing limitations of existing methods and reducing impact on residents.

Findings

Modularity of 0.4185 indicates effective zoning.

Only 24.8% of travel connections are cut off.

Method reduces pandemic control costs and social disruption.

Abstract

Since the outbreak of COVID-19, it has rapidly evolved into a sudden and major public health emergency globally. With the variants of COVID-19, the difficulty of pandemic control continues to increase, which has brought significant costs to the society. The existing pandemic control zoning method ignores the impact on residents'lives. In this study, we propose a refined and low-cost pandemic control method by scientifically delineating zoning areas. First, a spatial interaction network is built up based on the multimodal transport travel data in Nanjing, China, and an improved Leiden community detection method based on the gravity model is used to obtain a preliminary zoning scheme. Then, we use spatial constraints to correct the results with the discrete spatial distribution. Finally, reasonable zones for pandemic control are obtained. The modularity of the algorithm results is 0.4185, proving that the proposed method is suitable for pandemic control zoning. The proposed method is also demonstrated to be able to minimize traffic flows between pandemic control areas and only 24.8% of travel connections are cut off, thus reducing the impact of pandemic control on residents'daily life and reducing the cost of pandemic control. The findings can help to inform sustainable strategies and suggestions for the pandemic control.