## 1 Introduction
Those factors of high spatial and temporal variations reflect how the temporal patterns of a human mobility variable are more detailed and, as such, more appropriate to describe human mobility changes in comparison with the summary statistics (i.e., means). Thus, more advanced methods than calculating the mean values of the variable are needed to discover the human mobility pattern (HMP) in an aggregated form of a certain region. [PERSON] et al. [14] pioneered a work using a K-means time series clustering algorithm to distinguish different HMPs at the census block groups level using the variable of the median home dwelling time provided by SafeGraph. The three types of HMPs identified via their methods in the Metropolitan Atlanta area affirmed substantial intra-county spatial heterogeneities and pointed to the existence of extensive gaps in the effectiveness of social distancing measures between socially disadvantaged groups and others. Similar studies focused on the HMPs of other mobility variables as traveled distances and visitation rates, and also confirmed that the speed, depth, and duration of social distancing in the United States were heterogeneous [15]. Efforts to socially distance may be more burdensome for some socially disadvantaged communities than others, which would potentially exacerbate disparities in COVID-19-related health outcomes. Researchers, with the help of SafeGraph data, found that individuals in high-income neighborhoods increased their days at home substantially more than individuals in low-income neighborhoods and that residents of low-income neighborhoods were more likely to work outside the home compared to residents in higher-income neighborhoods [16]. These findings point out the fact that lower-income people have faced barriers in complying with state physical distancing policies. However, these analyses were based on the national average HMPs of people within different income quintiles and ignored both the geographical variations inside those income quantile groups and the impacts from other social, economic, and demographic (SED) variables. In addition, several studies have found correlations between compliance with human mobility restriction orders and several social characteristics of local populations. [PERSON] et al. [17], for example, found that the combination of \"having both high income and high-speed Internet appearsto be the biggest driver of propensity to stay at home\" using data that tracks 20 million mobile devices and their movements during the first few months of the early pandemic. [PERSON] et al. [18] concluded that the lower compliance with stay-at-home orders among low-income groups in the 12 most populated metropolitan statistical areas in the United States was likely due to the fact that these groups could not afford to comply with such orders. This research inspired us to develop intelligent HMP discovering methods to study these SED inequalities at finer geographical scales because they are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts. In this study, we used a dynamic-time-warping self-organizing map (DTW-SOM) clustering algorithm to classify the HMPs in CA census block groups, which has been well tested in clustering time series with asynchronous patterns [19]. We would like to address and solve this asynchronous pattern issue using DTW-SOM, as it allows for small tolerance on time shifting for HMPs. For example, some places might be faster in response to the shelter-in-place orders by a few days than others [11], but if their HMPs are similar in shape, we would assume that these places had similar compliance with orders. We noted that different human mobility variables might present unique and even contrasting characteristics. Thus, we ran the DTW-SOM on five different human mobility variables from SafeGraph to identify the HMPs of each variable and to reveal the multifaceted nature of human mobility.