Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?

Abstract

In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey’s test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014–2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures.

Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the United States during the COVID-19 Pandemic

“…Following the work by X. Huang, Li, Lu, et al (2020), representativeness is defined as the ratio between the median daily device count and the CBG's population (from the American Community Survey [ACS] 2020;2014-2018. As shown in Figure 3, the representativeness of one MSA differs from that of another, with the Dallas MSA (Figure 3D) showing the highest representativeness and the San Francisco MSA (Figure 3K) showing the lowest.…”

“…Following the design by X. Huang, Li, Lu, et al (2020), we included and recoded twenty-one demographic and socioeconomic variables from five major categories: (1) economic status; (2) race and ethnicity; (3) gender, age, and household type; (4) education; and (5) transportation. Numerous studies have proven that these variables are, to some degree, associated with participation in out-of-home activities (Kuppam and Pendyala 2001;Farber and P aez 2009;Morency et al 2011).…”

“…Given the voluntary nature of stay-at-home orders in the United States, there is a collection of evidence that reveals disparate responses from different communities even under the same order (Chiou and Tucker 2020;X. Huang, Li, Jiang, Ye, et al 2020;X. Huang, Li, Lu, et al 2020).…”

“…Similar patterns were also found at a much finer scale: A census tract-level study by Chiou and Tucker (2020) revealed that high-income earners generally spent more time at home during the stayat-home orders (the access to high-speed Internet plays a vital role). In addition to the contribution of financial factors, the disparity in response can be explained by other quantifiable demographic and socioeconomic variables, such as racial and ethnic composition (Czeisler et al 2020;X. Huang, Li, Lu, et al 2020), educational attainment (Gray et al 2020), and commuting modes (X.…”

Mobility-Based Contact Exposure Explains the Disparity of Spread of COVID-19 in Urban Neighborhoods

“…These are important factors to be taken into account since the Centers for Disease Control and Prevention specifically highlights via its famous 6-feet-15-minutes rule for a close contact 23 . While recent studies have considered the distribution of trips by dwell time 24 and crowd density based on non-residential square footage 25 at certain trip destinations, these have been studied in isolation as time series trends but not as a comprehensive measure of exposure.…”