Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?

Abstract

COVID-19 has sparked a debate on the vulnerability of densely populated cities. Some studies argue that high-density urban centers are more vulnerable to infectious diseases due to a higher chance of infection in crowded urban environments. Other studies, however, argue that connectivity rather than population density plays a more significant role in the spread of COVID-19. While several studies have examined the role of urban density and connectivity in Europe and the U.S., few studies have been conducted in Asian countries. This study aims to investigate the role of urban spatial structure on COVID-19 by comparing different measures of urban density and connectivity during the first eight months of the outbreak in Korea. Two measures of density were derived from the Korean census, and four measures of connectivity were computed using social network analysis of the Origin-Destination data from the 2020 Korea Transport Database. We fitted both OLS and negative binomial models to the number of confirmed COVID-19 patients and its infection rates at the county level, collected individually from regional government websites in Korea. Results show that both density and connectivity play an important role in the proliferation of the COVID-19 outbreak in Korea. However, we found that the connectivity measure, particularly a measure of network centrality, was a better indicator of COVID-19 proliferation than the density measures. Our findings imply that policies that take into account different types of connectivity between cities might be necessary to contain the outbreak in the early phase.

COVID-19 Impact on Residential Preferences in the Early-Stage Outbreak in South Korea

“…It seems that controlled environments were more important than density itself. A larger, city-level analysis in South Korea found that spatial connectivity had bigger impacts on proliferating the COVID-19 pandemic than urban density [16]. Thus, people may seek controlled neighborhoods.…”

“…Another study conducted in the U.S. between February and April in 2020 found that a lower population density was associated with a decrease in the instantaneous reproduction number among 211 counties [15]. A study examining South Korean cities found that urban density was significantly associated with the COVID-19 infection [16].…”

Are larger cities more central in urban networks: A meta-analysis

“…More broadly, high network centrality enhances a range of metropolitan functions including culture and sports (Meijers et al 2016). High centrality can also lead to undesirable outcomes, for example, as highly central global cities could be susceptible to the spread of infectious diseases from distant places (Ali and Keil 2006;Jo et al 2021).…”

Impact of pre-pandemic travel mobility patterns on the spatial diffusion of COVID-19 in South Korea

“…Travel mobility patterns, which are also called travel connectivity, have also been investigated in relation to COVID-19 transmission. However, the previous studies empirically identifying the impacts of travel connectivity on COVID-19 transmission may also have some limitations, in that they have limited travel mobility patterns to specific modes of travel ( Hamidi and Hamidi, 2021 ; Teller, 2021 ), or measured the degree of connectivity through social network analysis ( Murano et al, 2021 ; Jo et al, 2021 ). In a study that conducted a simulation model of vulnerability to the spread of COVID-19 in Ohio, USA, Cuadros et al (2020) employed degree centrality indicators for each travel mode calculated through social network analysis as another travel mobility and identified that the spread of infection was faster in areas with high connectivity than in areas with low connectivity.…”

“… Teller (2021) emphasized that travel connectivity played a decisive role in disease spread, but also noted that density and other urban factors should be considered in the spread of infectious diseases. These studies have identified urban characteristics at the county level that influence confirmed COVID-19 cases ( Hamidi et al, 2020 ; Cuadros et al, 2020 ; Ehlert, 2021 ; Jamshidi et al, 2020 ; Jo et al, 2021 ). Padmakumar and Patil (2022) investigated the impact of COVID-19 on travel mobility trends in the Indian metropolitan cities and found that such socio-economic factors, such as income, vehicle registration, and employment at the city level, had a significant impact on its change.…”