Impacts of COVID-19 on urban networks: Evidence from a novel approach of flow measurement based on nighttime light data

Abstract

The coronavirus disease 2019 (COVID-19) has caused significant changes in urban networks due to epidemic prevention policies (e.g., social distancing strategies) and personal concerns. Previous measurements of urban networks were mainly based on flow data or were simulated from statistical data using models (e.g., Gravity model). However, these measurements are not directly applicable to the mapping of directional urban networks during unexpected events, such as COVID-19. Since nighttime light (NTL) data offer a unique opportunity to track near real-time human activities, the radiation model, traditionally used for routine situations only, was modified to measure directional urban networks using NTL data under three scenarios: the routine scenario (before the Shanghai lockdown), the COVID-19 scenario (during the Shanghai lockdown), and the extreme scenario (without Shanghai's participation). When compared with the Baidu migration index, the modified radiation model achieved an acceptable accuracy of 0.74 under the routine scenario and 0.44 under the COVID-19 scenario. Our mapping of each scenario's urban networks in the Yangtze River Delta Region (YRDR) shows that the Shanghai lockdown reduced the urban interaction index between Shanghai and its surrounding cities. However, it led to an increase in the urban interaction index centered on the periphery cities of YRDR. Our findings suggest that urban interactions within YRDR are resilient, even under extreme scenarios. Considering the long time series and global coverage of NTL data, the proposed NTL-based urban network model can be readily updated and applied to other regions.