Exploring socioeconomic factors’ impact on human mobility during the COVID-19 pandemic

Abstract

Human mobility decreased worldwide during COVID-19. Most countries issued policies limiting or banning non-essential travel. Sweden issued no such policies and instead relied on non-binding recommendations. Sweden’s unique response makes it an interesting country for mobility studies. International studies have found that mobility has not reduced equally between socioeconomic groups during COVID-19. This inequality is potentially harmful since mobility during COVID-19 has been linked to an increased risk of infection and death. In this thesis, we explore how mobility has changed during COVID-19 for differ ent socioeconomic groups in Västra Götaland region, Sweden. This is achieved by building a mobility model from 250 million geolocation records generated from phone apps. The data was collected during October–November for both 2019 and 2020 Five mobility metrics are calculated from the mobility model, namely: radius of gyration, trip distance, trips between regions, visitation frequency, and location temporal profile. Additionally, five socioeconomic clusters are created by clustering 1000 areas based on their official socioeconomic data, including income and education. The socioeconomic groups are then defined as all users living within a cluster. Finally, for each metric, the mobility change between the years is compared for the socioeconomic groups. We found that mobility decreased during the pandemic in Sweden but to a lesser degree than in countries that issued lockdowns. Consistent with the literature, our study also observed differences in pre-pandemic mobility between the different socioeconomic clusters. Those differences, however, decreased during October-November 2020 compared to 2019. We conclude that the reduction in mobility is primarily driven by individuals with high pre-pandemic mobility, as most of the reduction was observed among the more mobile clusters and at the 50th and 75th percentile of the metrics. We also found that the most socially disadvantaged cluster had the lowest reduction in mobility, while the wealthiest cluster had the largest. This finding is consistent with our earlier conclusion, since the socially disadvantaged cluster had low pre-pandemic mobility, while the wealthy cluster had high mobility. We conclude that socioeconomic factors had a larger effect on pre-pandemic mobility than on the reductions in mobility during the observed months of the pandemic.