Where do people meet? Time-series clustering for social interaction levels in daily-life spaces during the COVID-19 pandemic

Abstract

The persistence of spatial segregation with respect to income and race is well documented. However, assessment of spatial segregation in daily activities 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 temporal analysis by conducting K-means time-series clustering using mobile phone data to examine social interaction levels among various sociodemographic groups during the COVID-19 pandemic. Selected sociodemographic variables are assessed among the identified time-series clusters. We find a strong association between sociodemographic characteristics and social interaction levels, potentially leading to disparate exposures to the risk from COVID-19. Socially disadvantaged populations tend to be more segregated from other groups in daily activities, and the COVID-19 pandemic increases the disparity. Low-income and ethnic minority populations became more isolated from Whites and the more economically affluent 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.