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.

由于防疫政策（例如社交距离策略）和个人担忧， 2019 年冠状病毒病（COVID-19）导致城市网络发生重大变化。以前对城市网络的测量主要基于流量数据或使用模型（例如重力模型）根据统计数据进行模拟。然而，这些测量并不直接适用于在突发事件（例如 COVID-19）期间绘制定向城市网络。由于夜间灯光 (NTL) 数据提供了跟踪近乎实时的人类活动的独特机会，因此传统上仅用于常规情况的辐射模型被修改为在三种场景下使用 NTL 数据测量定向城市网络：常规场景（之前）上海封城期间）、COVID-19情景（上海封城期间）和极端情景（上海不参与）。与百度迁移指数相比，修改后的辐射模型在常规场景下达到了可接受的精度 0.74，在 COVID-19 场景下达到了 0.44。我们对长三角地区（YRDR）每种情景的城市网络进行的绘制表明，上海的封锁降低了上海与其周边城市之间的城市互动指数。然而，这导致以长三角周边城市为中心的城市互动指数上升。我们的研究结果表明，即使在极端情况下，长三角区域内的城市互动也具有弹性。考虑到 NTL 数据的长时间序列和全球覆盖范围，所提出的基于 NTL 的城市网络模型可以很容易地更新并应用于其他地区。