Understanding changes to human mobility patterns in Ontario, Canada during the COVID-19 pandemic
Abstract
Transportation research has shown that socio-demographic factors affect people’s mobility patterns. During the COVID-19 pandemic, some of these effects have changed in accordance with changing mobility needs adapting to the pandemic, including restrictions on in-person gatherings, closure of in-person businesses and working from home. We investigate two gaps in current knowledge in this area of transportation research: to what extent the association between socio-demographic factors and mobility metrics have changed, and how these associations vary across geographic space. We also investigate how closely Ontario’s Public Health Unit boundaries, based on which pandemic restrictions were applied, reflect actual travel regions, and how travel regions changed throughout changing pandemic restrictions. We used aggregate de-identified cell tower location data to measure mobility metrics and to determine flow-based travel regions. Mobility metrics were modeled with socio-demographic data from the 2016 Canadian Census using a linear regression model and a geographically weighted regression model. We find that certain associations between socio-demographics and mobility have changed from what we have previously observed before the pandemic, and we can see the variation of these associations across space. Our flow-based travel regions computed using the Cluster Leading Eigenvector algorithm show that mobility became more localized when pandemic restrictions were in place, but that regionally-targeted restrictions did not necessarily reflect observed travel regions. These findings will improve our understanding of how socio-demographic factors affect mobility patterns in different communities, and demonstrates the importance of measuring these associations at a more fine-grained level using models that consider spatial variation to best reflect the nature of these associations.