Digital and Computational Demography
At a Glance
Linking Social Stratification and Geographic Mobility through Geo-Referenced Data Collected via a Smartphone Application
Jorge Cimentada, Emilio Zagheni; in Collaboration with John Palmer, Jonathan Kent (both: Pompeu Fabra University, Barcelona, Spain)
Recent advances in the analysis of digital trace data and computational social science have enabled researchers to investigate questions at the intersection of geography, sociology, demography, and computer science. In particular, the collection of passive geo-located data has opened new opportunities for researchers to investigate old and new theories related to mobility, migration, and integration. A few notable examples include recent studies aiming to test the social isolation theory using GPS data from Twitter’s API. Due to the difficulty in collecting geo-located data, classic research on social stratification has only been able to concentrate on collecting survey data, somewhat neglecting the role of spatial mobility and its effects on social mobility. However, recent literature on this topic shows that social mobility is susceptible to the place where people are born within a country and by their mobility over space and time within a country.
The main goal of this project is to understand the ways in which different sociodemographic and economic groups have differential patterns of geographic mobility, with consequences in terms of exposure to other population groups, to pollution, and to green and cultural spaces. The project aims to do this by combining what is considered to be the best among each type of data source: survey data (providing rich information about individuals) and geo-located data (providing frequent temporal and spatial data). Using state-of-the-art methods and software, the project aims at recruiting, via online platforms, a large sample of respondents from a number of cities around the world to download the Space-Mapper application, which records their daily mobility patterns.
Not only will this strategy make the study cross-national in nature, but it also gives frequent granular snapshots of a person’s mobility patterns at a frequency that is unprecedented in social mobility research. Moreover, when participants install the Space-Mapper application, we will be able to follow them over time and continuously check their mobility patterns at different stages of their lives. Since our study will be based on an online non-representative sample, we will use the demographic information from the questionnaire to post-stratify and address the issue of non-representativity based on the main demographic variables used in mobility research: age, sex, social class, and immigration background.
The combination of passive geo-located data and data from an extensive questionnaire will allow researchers to understand social mobility from a new perspective. Moreover, the project aims to be an open-source initiative with the potential of unraveling the mobility of individuals at the levels of cities and countries around the world.
Internal Migration, Housing, Urbanisation, Migration