Segregation or integration? Exploring activity disparities between migrants and settled urban residents using human mobility data

Abstract

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.

Exploring temporal and spatial reliability of individual activity space measurements using massive mobile phone data

“…It means that some novel measurements, such as the methods of integrating traditional survey data and massive location‐aware data, need to be developed in the future. Second, the research sample only contains the commuters, because their activity spaces exhibit better temporal regularity and more representative environment exposure, but other population groups in the city, such as migrants (Gao et al, 2021; Ta et al, 2021), are also important in urban studies. Thus, the temporal and spatial reliability of individual activity space measurements for expansive samples needs to be examined in the future.…”

“…However, Holliday et al (2017) found that 12 days of GPS monitoring were needed to reliably estimate physical activity time in many locations, and Zenk et al (2018) recommended a minimum of 14 days of valid GPS data to estimate activity spaces. In addition, the spatial patterns of activity space characteristics may be influenced by built environment factors, such as urban form, transport services, and transport network accessibility (Sharmeen & Houston, 2019; Ta et al, 2021), while only a few studies examined spatial disparity in the size, structure, and variability of activity spaces (Babb et al, 2017; Gao et al, 2021; Hirsch et al, 2014; Zhang et al, 2019). As a result, knowledge about the temporal and spatial reliability of individual activity space measurements is limited.…”

Towards a new paradigm for segregation measurement in an age of big data

“…Some studies have found that women (Ta et al, 2021), low-income people (Tao et al, 2020), older adults (Zhang et al, 2019), immigrant minority groups (Järv et al, 2015), or public housing residents (Wang & Li, 2016) have difficulties in participating in activities, leading to smaller activity spaces and less time spent out of the home. In contrast, higher income (Farber et al, 2012), the male gender (Kwan, 1999), car ownership (Gao et al, 2021;Ta et al, 2016), and being employed (Zenk et al, 2011) are positively associated with larger activity spaces. However, the evidence is inconsistent and even controversial.…”

“…A range of indicators from different dimensions have been developed to depict activity spaces and indicate the abilities to participate in employment or other activities. These studies primarily focused on the size of activity spaces (Jones & Peble, 2014;Järv et al, 2015), the number of conducted activities (Gao et al, 2021;Tao et al, 2020), activity types (Kamruzzaman & Hine, 2011) and the time spent on different activities (Zhang et al, 2019).…”

“…As illustrated in Fig. 1, the framework provides a much fuller evaluation by integrating spatial co-location, temporal pattern, accessibility, activity diversity, and social interaction (Gao et al, 2021). Spatial co-location suggests uneven spatial distribution among different social groups across the urban space.…”