Spatiotemporal Heterogeneities in the Causal Effects of Mobility Intervention Policies during the COVID-19 Outbreak: A Spatially Interrupted Time-Series (SITS) Analysis

Although there has been a growing interest in causal inference in geography studies, few studies have incorporated spatiotemporal heterogeneities with causalities. This study conceptualizes different patterns of spatiotemporal heterogeneity in the causal effects of policy interventions and develops a spatially interrupted time-series (SITS) quasi-experimental design to causally infer how the treatment effects of mobility control policies during the early stages of the COVID-19 outbreak vary across space and time, based on a five-month mobile phone big data set from Shenzhen, China. The modeling results reveal and distinguish significant temporal, spatial, and spatiotemporal heterogeneities in the policies’ causal effects. For example, we observed an abrupt decrease of 2.8 km in travel distance as a result of the first-level response to public health emergencies (i.e., FLR) and a decrease of 0.5 km as a result of the closed-off management of residential communities (i.e., COM), accounting for 44.6 percent and 7.2 percent of the baseline level before the pandemic, respectively. Such mobility reduction effects decayed at a rate of 0.033 km per day after the FLR and 0.076 km per day after the COM. For both policies, the abrupt effects were significantly larger in neighborhoods with a higher residential density and land-use mixture, lower average age, higher income, and higher marriage rate, whereas the gradual effect of the FLR decayed faster in similar compact neighborhoods. These findings demonstrate the importance of incorporating spatiotemporal variations with causality inference for fine-grained policy assessments, which can help policymakers determine when and where to implement which policies to mediate the mobility and the spread of the pandemic and plan for resilient neighborhoods in the postpandemic era.