The coronavirus disease 2019 (COVID-19) has exposed and, to some degree, exacerbated social inequity in the United States. This study reveals the correlation between demographic and socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. census block group (CBG) level in the twelve most populated Metropolitan Statistical Areas (MSAs) and further investigates the contribution of these variables to the disparity in home-dwelling time that reflects the compliance with stay-at-home orders via machine learning approaches. We find statistically significant correlations between the increase in home-dwelling time (${\nabla }_{\mathit{HDT}}$) and variables that describe economic status in all MSAs, which is further confirmed by the optimized random forest models, because median household income and percentage of high income are the two most important variables in predicting ${\nabla }_{\mathit{HDT}}.$ The partial dependence between median household income and ${\nabla }_{\mathit{HDT}}$ reveals that the contribution of income to ${\nabla }_{\mathit{HDT}}$ is place dependent, nonlinear, and different given varying income intervals. Our study reveals the luxury nature of stay-at-home orders with which lower income groups cannot afford to comply. Such disparity in responses under stay-at-home orders reflects the long-standing social inequity issues in the United States, potentially causing unequal exposure to COVID-19 that disproportionately affects vulnerable populations. We must confront systemic social inequity issues and call for a high-priority assessment of the long-term impact of COVID-19 on geographically and socially disadvantaged groups.

2019 年冠状病毒病 (COVID-19) 暴露并在一定程度上加剧了美国的社会不平等。本研究揭示了人口和社会经济变量与从美国人口普查区组 (CBG) 级别的 12 个人口最多的大都市统计区 (MSA) 的大规模移动电话位置跟踪数据得出的家庭居住时间记录之间的相关性，并进一步调查这些变量对居家时间差异的贡献，反映了通过机器学习方法对居家订单的遵守情况。我们发现居家时间的增加之间存在统计学上的显着相关性（${\nabla }_{\mathit{热电偶}}$) 和描述所有 MSA 中经济状况的变量，优化后的随机森林模型进一步证实了这一点，因为家庭收入中位数和高收入百分比是预测的两个最重要的变量 ${\nabla }_{\mathit{热电偶}}.$ 家庭收入中位数与 ${\nabla }_{\mathit{热电偶}}$ 表明收入对 ${\nabla }_{\mathit{热电偶}}$是地方相关的、非线性的，并且在不同的收入区间下是不同的。我们的研究揭示了低收入群体无法遵守的居家订单的奢侈性质。居家令下的这种反应差异反映了美国长期存在的社会不平等问题，这可能导致对 COVID-19 的不平等暴露，从而对弱势群体产生不成比例的影响。我们必须面对系统性的社会不平等问题，并呼吁对 COVID-19 对地理和社会弱势群体的长期影响进行高度优先评估。