Daily Mobility Patterns: Reducing or Reproducing Inequalities and Segregation?

Abstract

Theory states that residential segregation may have a strong impact on people’s life opportunities. It is unclear, however, to what extent the residential environment is a good representation of overall exposure to different people and environments. Daily mobility could reduce the negative effects of segregation if people change environments and/or become more mixed. They could also enhance existing segregation patterns if daily mobility produces more segregated environments. This article uses mobile phone data to track daily mobility patterns with regard to residential segregation. We test the extent to which patterns differ between residents in immigrant-dense areas and those from areas with a greater proportion of natives. Results suggest, in line with previous research, that daily mobility patterns are strongly segregated. Phones originating from more immigrant-dense areas are more likely to (1) remain in the home area and (2) move towards other immigrant-dense areas. Hence, although mobility does mitigate segregation to some extent, most people are mainly exposed to people and neighbourhoods who live in similar segregated environments. These findings are especially interesting given the case study areas: two medium-sized Swedish regions with relatively low levels of segregation and inequality and short journey distances.

Leisure mobility changes during the COVID-19 pandemic – An analysis of survey and mobile phone data in Sweden

“…We record the antenna locations and duration of stay (in 5-min intervals) for each pseudonymised phone over a 24 h window separately for each of the three years (see also Hedman, Kadarik, Andersson, & Östh, 2021 ; Östh, Shuttleworth, & Niedomysl, 2018 ). MIND contains a scrambled IMSI code (unique identifier for each SIM-card) that can be used to separate users.…”

“…However, the precision of the location is conditioned on the density of antennas, which means that phones in urban areas and in proximity to infrastructure hubs are localized with greater precision compared to phones in rural areas, since the density of antennas is greater in population dense areas. In this study, we estimate the location of phones using two main steps: We record the antenna locations and duration of stay (in 5-min intervals) for each pseudonymised phone over a 24 h window separately for each of the three years (see also Hedman, Kadarik, Andersson, & Östh, 2021 ; Östh, Shuttleworth, & Niedomysl, 2018 ). MIND contains a scrambled IMSI code (unique identifier for each SIM-card) that can be used to separate users.…”

The geography of connectivity: a review of mobile positioning data for economic geography

“…A majority of the works that were found through our systemic literature review was produced by or in collaboration with researchers in fields such as computer or information sciences. Whilst there has been some works produced within the fields of social science, such as Ahas et al (2015), Eagle et al (2010), Hedman et al (2021), andÖsth et al (2018), they are a minority in our sample. Nevertheless, these researchers have produced insights in a number of social phenomena with relatively straightforward incorporation of mobile phone data in the methodological framework.…”

“…A set of studies used mobile phone data to assess questions of segregation, which leaves a clear trail in communication and mobility patterns (Hedman et al 2021;Östh et al 2018). For instance, Cottineau and Vanhoof (2019) found that mobility range and diversity tended to decrease in cities with large levels of segregation.…”

“…Peng et al 2017a, b;Singh and Ghosh 2017), literacy (e.g. Blumenstock et al 2015;Schmid et al 2017), development, or segregation and poverty (e.g., Hedman et al 2021;Hernandez et al 2017;Östh et al 2018). Furthermore, some studies have developed methods for analysing mobile phone data together with socio-economic factors, using aggregated data.…”