Human mobility connects urban dwellers and neighborhoods and impacts social equity. An in-depth understanding of human mobility helps to enhance urban resilience. However, limited research has focused on mobility resilience. Building on previous research, this study looks at the neighborhood connectivity enabled by urban mobility. We analyze the aggregated mobility patterns in Boston through the coupling of network structure and social characteristics. Geocoded twitter data combined with socioeconomic datasets were used to create a mobility-based urban network. Through the quantitative analysis, we found that the social segregation in Boston shapes its mobility network. Network communities identified by the Louvain modularity algorithm are often self-containing, meaning that their residents are more likely to move within their communities. A multinomial regression reveals that spatial racial and income segregation has a strong impact on the dynamic segregation of the network. The beneficial network characteristics –e.g. higher density and well-connected motifs– are less present in areas with bolder presence of minorities. Thus, the resilience state is not equitable among neighborhoods of different income levels and races, indicating that the resilience measures of urban networks need to be adapted according to sociodemographic characteristics.

人口流动性将城市居民和社区连接起来，并影响社会平等。对人员流动的深入了解有助于增强城市的适应能力。但是，有限的研究集中在移动弹性上。在先前研究的基础上，本研究着眼于城市流动性带来的邻里连通性。我们通过网络结构和社会特征的耦合来分析波士顿的总体流动模式。经过地理编码的Twitter数据与社会经济数据集相结合，用于创建基于流动性的城市网络。通过定量分析，我们发现波士顿的社会隔离塑造了其流动网络。通过Louvain模块化算法确定的网络社区通常是自包含的，这意味着其居民更有可能在其社区内移动。多项式回归表明，空间种族和收入隔离对网络的动态隔离有很大影响。在少数群体大胆存在的地区，有益的网络特征（例如，较高的密度和良好连接的主题）较少出现。因此，不同收入水平和种族的邻里之间的复原力状态不平等，这表明城市网络的复原力措施需要根据社会人口统计学特征进行调整。