Suburban commuting and activity spaces: using smartphone tracking data to understand the spatial extent of travel behaviour

Abstract

This study explores the spatial extent of daily mobility by analysing the activity spaces of suburban commuters. It deepens knowledge of the transport‐related consequences of functionally segregated areas within metropolitan regions, detecting the most significant factors (personal and environmental) affecting the size of activity spaces of people with a suburban commute. Additionally, a comparison between new and traditional calculation methods of activity spaces has been carried out. To enable this, an app was developed for smartphones enabled with a global positioning system (GPS) in order to obtain accurate tracking data for 233 members of the Autonomous University of Barcelona in the Metropolitan Region of Barcelona, Spain. Results show that spatio‐temporal factors together with socioeconomic factors, such as the professional role, are strong determinants of the size of activity spaces. Moreover, differences between public and private transport modes of commuting were minimal, proving the potential of public transport as a non‐restricting means of transport even in suburban environments. Finally, the comparative analysis between calculation methods highlight that new methods produce more realistic representations of the spatial extent of everyday life, and different sets of explanatory factors emerge for activity spaces measured in different ways.

A Relational Perspective on Everyday Mobility in the Barcelona Metropolitan Region: Individual and Household‐Related Differences in Daily Travel Time

“…Second, results also confirm that social status is tightly linked with larger temporal investments in daily trips, and especially in large metropolitan areas in which residences and workplaces are being progressively separated (Vich et al . ). In this sense, our results point out that education level goes hand in hand with greater travel time expenditures, and being employed is also seen as a factor linked with more mobility activity (Barnes & Davis ), although the effect is not entirely statistically significant.…”

Activity spaces in studies of the environment and physical activity: A review and synthesis of implications for causality

“…Samples were studied for all age groups with most drawn from adult (n = 22) populations. Some targeted females (Rodríguez et al, 2012; Rodriguez et al, 2015), lower income participants (Hirsch et al, 2016; Franke et al, 2017), university members (Kamruzzaman et al, 2011; Vich et al, 2017), e-bike owners (Plazier et al, 2017), or those living in subsidised housing (Colabianchi et al, 2014). Most studies were solely cross-sectional in design (n = 43) and four assessed activity spaces in relation to an intervention (Kamruzzaman et al, 2011; Kosaka et al, 2014; Chaix et al, 2017; Tribby et al, 2017).…”

Advanced Smartphone-Based Sensing with Open-Source Task Automation

“…Today, there are already publications about smartphone sensing methods (SSMs) on topics of ecosystem services in geo- and citizen science [ 5 , 6 , 7 ], human activity, health, and well-being in medicine and sports science [ 8 , 9 , 10 , 11 , 12 ], urban microclimate in meteorology [ 13 , 14 ], air pollution and noise in geography [ 15 , 16 , 17 ], mobility and human movements in transport planning [ 18 , 19 , 20 ], and social interaction and behavior in social science [ 21 , 22 , 23 ]. …”