Where Do People Meet? Time-Series Clustering for Social Interaction Levels in Daily-Life Spaces during the COVID-19 Pandemic

Keywords: Segregation; social interaction; time-series clustering; sociodemographic characteristics; COVID-19
Session Type: Virtual Paper Abstract
Day: Monday
Session Start / End Time: 2/28/2022 09:40 AM (Eastern Time (US & Canada)) - 2/28/2022 11:00 AM (Eastern Time (US & Canada))
Room: Virtual 3

Abstract

The assessment of spatial segregation in daily-life places beyond residence and work locations is challenging due to the limited availability of human movement data. With the ubiquitous availability of mobile phones and location-based service applications, human movement data has become widely available. It is now possible to explore spatial interactions and assess the extent of social segregation in daily activity spaces. Using Los Angeles County as our case study, we perform a trend-driven analysis by conducting K-means time-series clustering using fine-grained mobile phone data to examine social interaction levels among various sociodemographic groups during the COVID-19 pandemic. Selected sociodemographic variables are then assessed among the identified time-series clusters. We find a strong association between sociodemographic characteristics and social interaction levels, which potentially leads to disparate exposures to the risk from the COVID-19. Socially disadvantaged populations are more segregated from others, while the stay-at-home order aggregates such disparity of segregations. Low-income and ethnic minority populations became more isolated during the COVID-19 pandemic. Policies that aim to encourage social interactions and mitigate segregation effectively should further consider people’s sociodemographic variables and relevant neighborhood characteristics.