The project will build on a unique combination of datasets which includes:

(a) Infotechnological Mobility Observatory (IMO)

(b) smartphone tracking data; complemented by

(c) GIS layers on activity site characteristics; and

(d) post-tracking survey.

For methods we will use an indices-based SEG approach, GIS-based analytical methods, longitudinal modelling, methods for analysing space-time behaviour for individuals, and social network analysis. Our case study area is Tallinn urban region.

Our research population consists of people living in the urban region, but their activitity space may reach out of it (including cross-border commuting). All domains as discussed above will be studied: residence, work, school, and leisure. We will focus on the socioeconomic and ethnic SEG. Education, occupation, and (household) income serve to measure socioeconomic status. Country of birth, mother tongue, and self-defined ethnicity measure ethnicity.

The project is based on the following three integrated work packages (WP):

WP 1: Vicious Circles of SEG: the macro-level perspective

This WP provides a macro-level analysis of SEG in different domains and builds a contextual database for studying individual level sorting and contextual effects in WP 2 and WP 3.

WP 2: Vicious Circles of SEG: a longitudinal micro-level analysis

This WP focuses on how the sorting of individuals into activity sites and exposure to others in those activity sites contributes to the (re)reproduction of SEG between different domains over the life course and across generations.

WP 3: Vicious Circles of SEG: a micro-level analysis of the activity space

The censuses and registers allow for the study of individual trajectories in different domains, but we still miss important pieces of information when it comes to understanding how SEG is (re)produced: (a) we do not always know whether members of different ethnic and socioeconomic groups are co-present, it means in the same activity sites at the same time; and (b) with whom they interact there. Guided by a time-geographic research perspective (Hägerstrand 1970; Silm et al 2018) and enriching it with new innovative smartphone tracking data, we aim to detect the co-presence of different ethnic and socioeconomic groups in different activity sites.


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