SPATIO-TEMPORAL ANALYSIS OF URBAN ECONOMIC RESILIENCE DURING COVID-19 WITH MULTILAYER COMPLEX NETWORKS

Abstract

The COVID-19 pandemic has impacted the economic growth of almost every country, with many industries facing operational difficulties, and the failures of a large number of restaurants, in particular, have extensively tested the resilience of urban economies. The gastronomy business is one of the most decentralized and location-based consumer business in urban, which is highly related to the economic attributes of cities. However, there are few studies on quantitative analysis of urban economic resilience through the opening and closing of restaurants. Understanding and planning for the aftermath of the COVID-19 may not only minimize detrimental effects but also provide insights into the economic recovery policies. This study analyzes the phenomenon of restaurant failures after the pandemic in Shenzhen, China via percolation in multilayer complex networks. We identify the closed restaurants through data mining, and construct the human mobility network through mobile phone location data, aggregating origin and destination points into grids. We then embedded the restaurants’ Points of Interest (POIs) into the grids, creating an additional restaurant network layer. By considering spatial interactions between restaurants, we constructed a geographical proximity network for restaurants in each grid. Finally, Using these multilayered nested networks, we analyzed the pandemic’s impact and the occurrence of critical phenomena related to restaurant closures under lockdown policies through percolation in multilayer complex networks. As a result, this study found that the severity of the pandemic significantly increased the probability of restaurant failures, with cascade and critical phenomena. However, implementing precise lockdown measures can effectively lower the probability of restaurant closures. These results highlight the effectiveness of accurate lockdown policies in striking a balance between epidemic prevention and economic development, contingent upon the correct identification of high-risk areas. This finding suggests that policy makers and public health departments need to balance policy effectiveness with interventions in economic activities in order to increase the resilience of urban economies during the pandemic.