## 4 Discussion  
Human mobility changed during the COVID-19 pandemic because of various factors, including lockdowns, remote work, and increased concerns. The whole workflow and the results of this study shed light on the relationships between the geographic distributions of SED characteristics and the HMPs so that researchers could more accurately model the epidemiology of the virus and the impact of potential policy interventions to help identify the most effective non-pharmaceutical actions. Studying the disparities in HMPs due to SED characteristics is of great significance as it can help policymakers better understand why certain communities may have been more or less compliant with shelter-in-place orders. This understanding can then be used to develop targeted interventions and communication strategies that address the specific barriers to compliance within certain communities. Additionally, studying compliance disparities can help identify and address potential inequities in the enforcement of such orders, ensuring that all communities are treated fairly and equitably. Furthermore, understanding the SED factors that influence compliance with shelter-in-place orders can also inform future pandemic response efforts, helping to ensure that measures are designed in a way that maximizes compliance while minimizing negative impacts on communities. Overall, this study is essential for improving the effectiveness and equity of pandemic response efforts. This study investigated the human mobility patterns at the CBG level proxied by five selected human mobility indicators and discovered two iconic HMPs and their top \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Acronym**} & **Full-Time** & **Part-Time** & **Stay-at-Home** & **Home Dwelling** \\  & **Behavior** & **Behavior** & **Behavior** & **Time** \\ \hline pnoINS & \(-\)0.51 & \(-\)1.54 & \(-\)0.84 & \(-\)0.53 \\ pservOCP & \(-\)0.62 & \(-\)1.68 & \(-\)0.26 & 0.40 \\ pmovOCP & \(-\)1.04 & \(-\)2.27 * & \(-\)2.13 & 0.89 \\ pFOVERTY & \(-\)1.91 * & \(-\)1.49 & \(-\)4.14 * & \(-\)3.30 * \\ medINC & 3.20 ** & 3.76 ** & 1.26 & 1.51 ** \\ medHSV & \(-\)0.13 & 0.40 & 1.57 ** & 0.46 \\ pmHIGH & \(-\)1.51 & \(-\)2.73 * & \(-\)2.64 & \(-\)0.97 * \\ pHIGH & 0.51 & \(-\)1.85 & \(-\)0.93 & 1.23 \\ pWOUTCO & 0.09 & 0.05 & 0.00 & 0.07 \\ pWAH & \(-\)0.29 & \(-\)0.53 & \(-\)0.25 & \(-\)0.12 \\ pBLACK & \(-\)1.19 & \(-\)1.44 & 0.30 & 0.04 \\ pASIAN & 4.43 ** & 3.68 ** & 3.15 ** & 1.49 ** \\ pHISPAN & \(-\)0.86 & \(-\)1.04 & \(-\)0.72 & 0.78 \\ pFEMALE & 0.58 & \(-\)1.18 & 0.77 & 1.16 \\ pUNDER5 & 2.34 ** & 1.03 ** & \(-\)2.92 * & 2.27 ** \\ pmOVER65 & \(-\)4.67 * & \(-\)4.48 * & \(-\)3.57 * & \(-\)3.16 * \\ pFOVER65 & \(-\)0.98 & \(-\)0.39 & 0.96 & \(-\)0.60 \\ Intercept & \(-\)1.83 * & \(-\)0.16 & 1.39 ** & 0.46 \\ Accuracy & 78.19\% & 78.57\% & 76.47\% & 79.62\% \\ \hline \hline \end{tabular}  
* Top 3 negative values; ** Top 3 positive values.  
\end{table} 
Table 3: LDA analyses of the dichotomized social mobility groups using social-economic and demographic variables. important social determinants (either positively or negatively). The 2 HMPs are the HMP with a strong asymmetry peak tailing to right in the indicators of median home dwelling time and completely stay-at-home devices and the HMP with the emergence of strong weekly patterns after a sharp decrease in values in the indicators of full-time and part-time work behavior devices. Both HMPs tend to favor better political compliance to shelter-in-place orders and provide a greater temporal context than pure value changes over time. For example, using the pure value changes of group 1 and group 3 in Figure 1 would totally dedifferentiate the two groups from each other since both groups have similar beginning values and ending values, even though group 3 has the HMP with a strong asymmetry peak whereas group 1 does not.