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Modeling Commuter Mobility in Stockholm: A Spatial Panel Approach Using Mobile Phone Data
(Springer Heidelberg, 2026) Fischer, Manfred M.; Osth, John; Toger, Marina; Turk, Umut
This paper examines the sociodemographic and socioeconomic determinants of regional commuter mobility in the Greater Stockholm Area using a heteroscedastic spatial Durbin panel data model estimated via Bayesian Markov Chain Monte Carlo methods. Drawing on mobile phone-derived origin-destination flows from the MIND database, the analysis exploits unusually fine spatial and temporal granularity across a balanced panel of 675 regions over the period 2018-2023. A k-nearest neighbor spatial weight matrix (k = 18), selected via Bayesian model comparison, captures the topological structure of interregional connectivity. By modeling spatial lags in both the dependent and independent variables, the framework enables explicit recovery of direct (own-region) and indirect (spillover) effects from scalar summary measures of the matrix of partial derivatives - providing robust posterior inference on how sociodemographic and socioeconomic conditions propagate through space. This approach addresses a key limitation of conventional non-spatial methods, which risk producing biased estimates by ignoring spatial interdependence. Empirical results confirm that spatial spillovers predominate over direct effects, with educational attainment and car ownership emerging as the principal determinants of commuter mobility, while age composition plays a comparatively modest role. These findings underscore that evaluating direct effects in isolation systematically underestimates the broader societal returns to mobility-enhancing regional policies.
Citation - WoS: 7
Citation - Scopus: 8
Introducing a Spatially Explicit Gini Measure for Spatial Segregation
(Springer Heidelberg, 2023-06-14) Turk, Umut; Osth, John
This paper proposes an alternative measure of economic segregation by income that utilizes the Gini index as the basis of measurement. The Gini Index of Spatial Segregation (GSS) is a ratio of two Gini indices that compares the inequality between neighbourhoods to the inequality between individuals at the macro-level where neighbourhoods are nested. Unlike earlier measures of segregation found in the literature, the GSS uses individualized neighbourhoods, which can be defined as an area constituted within a radius or as a population count method around an individual geo-location, depending on the population density and proximity among individuals in the study area. The GSS can measure residential segregation by any continuous variable for both radii and k-nearest neighbours (knn with and without a decay factor) approaches to bespoke neighbourhoods. Therefore, it is sensitive to the spatial configuration of the area, easy to compute and interpret, and suitable for comparative studies of segregation over time and across different contexts. An empirical application of the index is illustrated using data from Sweden that covers the entire population for 1994, 2004, and 2014. We demonstrate how the definition and scale of the neighbourhood influence the measures of economic segregation. Overall, the GSS offers a flexible and robust framework for measuring segregation that can be used to inform policy decisions and research on inequality.
Citation - WoS: 14
Citation - Scopus: 17
How Much Does Geography Contribute? Measuring Inequality of Opportunities Using a Bespoke Neighbourhood Approach
(Springer Heidelberg, 2019-03-30) Turk, Umut; Osth, John
To what extent an individual is successful in a variety of outcomes is the result of multiple factors such as (but not limited to) parental background, level of education, discrimination and business cycles. Factors like these also indicate that the success in life can be attributable to factors that both take individual-level merits into account but also to structural factors such as discrimination and contextual effects. Over the last decades, a growing interest in decomposing and categorising factors that affect the life chances of individuals has led to the formation of inequality of opportunity as a research field. This paper builds upon this growing literature, which amounts to quantify the contribution of factors that lie beyond the control of individuals to the total inequality observed in different spheres of life. Using rich Swedish longitudinal register data, we are able to follow individuals over time and their educational attainment during upbringing and later labour market outcomes. In difference from other inequality of opportunity studies, we make use of an egocentric neighbourhood approach to integrate the socio-economic composition of the parental neighbourhood in an inequality model and illustrate its contribution to the total inequality in both outcomes quantitatively. Using multilevel regression analyses, we show that the parental neighbourhood is highly influential in educational attainment and remains so for market outcomes even years after exposure.

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