Due to the difficulty of tracking large numbers of new migrants, how their daily activity behaviors differ from those of settled residents has not been well investigated, leading to a lack of understanding of new migrants' integration. Meanwhile, existing research largely emphasized residential space and ignored other activity disparities. To obtain a more comprehensive picture of urban segregation, we identified new migrants and two settled urban groups from two kinds of human mobility data. A S-T-A-D-I interactive framework was proposed to measure segregation from multiple activity dimensions, including spatial colocation, temporal coexistence, accessibility, activity diversity, and social interaction. Two-scale analysis of spatial colocation patterns reveals residential segregation by both residential location and housing type, suggesting the effectiveness of the mobility data in profiling socioeconomic groups. The temporal disparity in undertaking activities was unveiled by identifying temporal coexistence patterns. Moreover, the groups presented significant inequality in accessibility owing to the use of different travel modes, leading to a notable disparity in activity diversity. Jointly determined by the disparities in space, time, and diversity, the three groups generated a high level of self-segregation, and new migrants and transit users presented very low interaction potentials with the car group.