Spatiotemporal Analysis of Urban Nighttime Light After China Lifted 3-Year-Old COVID-19 Restrictions

Abstract

China fully lifted restrictions of the COVID-19 pandemic on December 7, 2022. However, the influence of this policy on urban socio-economic activities is unclear. Remotely sensed nighttime light (NTL) data have been widely used in evaluating public policies in near real-time, therefore, this paper attempts to explore the spatio-temporal impact of liberalization on mainland China from the perspective of NTL data. Taking 25 cities with different development levels as representatives, based on daily Black Marble NTL product (VNP46A2), we obtained monthly and weekly averaged NTL images before and after liberalization, and calculated NTL changes in concentric rings to detect the spatio-temporal variations from the urban centers, which can reflect the urban vitality to some degree. Experiment results show that urban NTL radiances within 18 km from urban center are generally on the rise after the lifting of restrictions. The increase of NTL around urban core is the most significant, and the increase intensity decreases exponentially (R 2 >0.7) with the distance from city center or near-center for most cities. Furthermore, NTL radiance generally increases significantly within 1~2 weeks after the lifting in megacities, and different levels of cities responded differently. NTL of megacities and large cities generally changed more rapidly than small-medium cities. Moreover, although mainland China has experienced three years of pandemic control, urban development is still underway. The lifting of COVID-19 travel restrictions has brought back the recovery of urban economic vitality. These findings can provide insightful support to urban construction and policy guidance intervention after the pandemic.

Exploring the Impact of Public Health Emergencies on Urban Vitality Using a Difference-In-Difference Model

“…The repercussions of COVID-19 on society are extensive and enduring, touching on epidemiology [30], psychology [31], environmental concerns [32], and transportation [33]. In the aftermath of the outbreak, Geographic Information Systems (GISs) and remote sensing (RS) technologies have become pivotal in outbreak management, facilitating the monitoring and visualization of outbreak data [34], simulating virus transmission [35], and optimizing the distribution of medical resources [36]. Furthermore, GISs have been instrumental in analyzing the interplay between social behavior, the environment, and virus spread.…”