## 2 Materials and Methods
We also named the four groups under each metric using the order of their group means so that time-HMP-group one denoted the group with the smallest mean, which was generated by applying DTW-SOM on the median home-dwelling time variable and, similarly, time-HMP 4 represented the HMP (cluster center) of the time-HMP-group 4, which had the largest mean under the same metric. This nomenclature would allow us to more easily refer to various groups generated by different human mobility metrics and explain our hypothesis that HMPs offer fine-grained mobility profiles and, therefore, are superior to mean values in investigating the complex relations between human mobility and other socioeconomic and demographic factors. We downloaded 17 social, economic, and demographic variables for all CBGs in California from the 2014 to 2018 American Community Survey (Table 1). For each mobility indicator, we related the HMPs discovered by DTW-SOM to the 17 SED factors using two complementary approaches: one univariate and one multivariate. In the univariate approach, we used one-way ANOVA to quantify the variation in each SED factor across the 4 HMP groups. In the multivariate approach, we assigned the 4 HMP groups for each human mobility indicator into two groups, consisting of one group that had a typical HMP of interest versus the other group that did not, due to the following reasons: (1) The 4 HMPs of each mobility indicator discovered by DTW-SOM were not all equally representative. Some of them were much less populated than others (i.e., group 2 in the delivery behavior HMP groups only had 569 CBGs whereas the largest group 1 had 14,650 block groups); (2) Meanwhile, it would be very tedious to discriminate the differences between each two out of the groups. Thus, we decided to focus on the typical HMPs that we had the most interest in and dichotomized the groups according to whether they had that kind of HMP or not. For example, we had two groups that were derived based on the DTW-SOM results of the full-time work mobility indicator. One group contained CBGs showing an HMP of strong weekly patterns with periodic peaks during weekends, whereas the other one consisted of CBGs that did not show such an HMP. The detailed dichotomization schema is listed below:Full-time work behavior HMP groups (group 1 vs. others): HMP in group 1 showed lower values in 2020 than in 2019 (negative changes indicated by the centroid curve below the zero-baseline all the time), and the most apparent sharp decline took place right after the pandemic period started. Most importantly, strong weekly patterns with periodic peaks during weekends started to emerge with this HMP after the pandemic period started. We decided to group other HMP groups together because they ed no such HMP (group 2 showed a vaguely similar HMP but looked more closed compared to the rest of the groups), and we were interested to see what other SED variables contributed to the emergence of weekly patterns in such HMP. * Part-time work behavior HMP groups (group 1 vs. others): The dichotomization schema and HMP profiles here were similar to what we conducted for full-time work behavior groups. * Completely stay-at-home HMP groups (group 3, 4 vs. group 1, 2): HMPs in groups 3 and 4 here both have an obvious asymmetry peak pattern (even though at different levels), which increased at the beginning of the pandemic period and gradually decreased moving forward. In comparison, the other groups had either no or very modest HMP throughout the whole period. * Home dwelling time HMP groups (group 3, 4 vs. group 1, 2): The dichotomization schema and HMP profiles here followed what we conducted for completely stay-at-home groups. We then ran LDA to find the linear combinations of the SED factors that separated the two groups. The direction and magnitude of the LDA coefficients could be used to identify the factors that contribute the most, either positively or negatively, to distinguishing the two groups.