Our paper titled “Exploring the spatial disparity of home-dwelling time patterns in the U.S. during the COVID-19 pandemic via Bayesian inference” has been accepted for publication by the Transactions in GIS.
Abstract: In this study, we aim to reveal hidden patterns and confounders associated with policy implementation and adherence by investigating the home-dwelling stages from a data-driven perspective via Bayesian Inference with weakly informative priors and by examining how home-dwelling stages in the U.S. varied
geographically, using fine-grained, spatial-explicit home-dwelling time records from a multi-scale perspective. At the U.S. national level, two changepoints are identified, with the former corresponding to March 22, 2020 (nine days after the White House declared the National Emergency on March 13) and the latter corresponding to May 17, 2020. Inspections on the U.S. state and county level reveal notable spatial disparity in home-dwelling stages, presumably resulting from the discrepancies in political partisanship, COVID-19 severity, social distancing compliance, re-opening policy, and industry distribution. A pilot study in the Atlanta Metropolitan area at the Census Tract level reveals that the self-quarantine duration and increase in home-dwelling time are strongly correlated with the median household income, echoing existing efforts that document the economic inequity exposed by the U.S. stay-at-home orders. To our best knowledge, our work marks a pioneering effort to explore multi-scale home-dwelling patterns in the U.S. from a pure data-driven perspective and in a statistically robust manner.
