Understanding Mobility in Sierra Leone During COVID-19 Using Call Detail Records

Abstract

Call Details Records (CDR) can provide an essential resource for understanding mobility patterns in data-poor environments, while often applied towards transportation applications. This thesis seeks to use CDR to understand the impact of government policies during fast-moving public health threats. To test the usefulness of CDR data, we apply it towards two different research questions: 1) How did human mobility patterns change after travel restriction policies during COVID-19, and how was that related to their socioeconomic status? 2) How did travel policies and socioeconomic status impact mobile users' accessibility to services during COVID-19? A big data analysis pipeline is developed to answer these two questions. For the analysis of mobility patterns changes, a series of mobility metrics are generated, including radius of gyration, purpose of trips, regularity of movement, and motif types. Then the mobile users are clustered into four typologies based on the metrics to determine how different groups of people changed their travel behavior during COVID-19. For measurement of impacts of travel policies and socioeconomic status on mobile users' accessibility to services (i.e., food and healthcare), accessibility metrics including travel distance, the rate of discretionary trips, the entropy of trip duration, and the cumulative number of food /healthcare services are derived. Then users' accessibility behaviors are classified as four distinct types to inform more target and effective policies. From our analysis, we find 1) the travel activities decreased hugely during the lockdown period and rebounded partly during the travel restriction period. 2) Users living in more impoverished areas generally needed to travel long before the COVID- 19 but decreased their travel behaviors hugely during the lockdown. 3) Users of higher socioeconomic status are less likely to be influenced by travel restrictions to obtain food/ healthcare resources, while travel policies easily influence users of lower socioeconomic status. This thesis interprets Sierra Leone's accessibility and mobility via multiple perspectives, providing analysis to support the local government in coping with the pandemic. The big data analysis pipeline created in this thesis can also be applied to future research in other data-poor countries. The research can be integrated with other research fields such as epidemiology, sociology, and economics to provide more information to inform policy decision-making.