The aim of this study is to investigate in what ways the spatial context matters for the life course trajectory status in terms of vulnerability. In particular, it explores the impacts of spatial contexts aggregated from life course trajectories. It uses a longitudinal micro-dataset, 1990–2019 from Statistics Sweden, to analyze the relationship between geographical context constructed by aggregated life course trajectories, and individual life course trajectories. A latent class analysis (LCA) is employed to identify life courses and examines how these trajectories are influenced by individualized neighborhoods. The findings show that spatial context plays a significant role in shaping individuals' life course trajectories of vulnerability: (1) being in any sort of trajectory of vulnerability, (2) four transitional categories of vulnerability. Residing in a context of Families in distress was associated with higher odds of experiencing vulnerability. Other factors such as country of birth, gender, earlier vulnerability also impact life course trajectory status. This study contributes to answering the question if poor neighborhoods make their residents poorer, posed by Jürgen Friedrichs in the late 1990s. The study goes beyond merely measuring cross-sectional single variable residential segregation patterns as context, offering valuable insights into consequences, supporting planning for geographic equality of opportunity.

This article examines the meaning of local contexts in the formation of young adults’ life trajectories and horizons of opportunities in southern Stockholm. Our investigation draws on latent class analysis (LCA) of people aged 25–59 years, which reveals typical latent life courses among the individuals. The local setting mapped with typical life courses is interpreted as an indication of habitus, further examined in interviews with young adults 17 to 19 years old and about to finish upper secondary school who actively consider different life plans. By combining Bourdieu's and Wacquant's social theories with Massey's conceptualization of space and place, our analysis illuminates four spatial processes of habitus formation: (1) broader social structures, where southern Stockholm is polarized in accordance with affluence and vulnerability; (2) symbolic images and perceptions that make school locations attractive or invisible; (3) young adults’ meetings and non-meetings-up that take place within and in between school, home and leisure activities; and (4) the different ‘layers’ of local settings, where social networks are interlinked differently to national organizations and institutions and provide young people with different horizons of opportunity. The combination of theories facilitates a mixed-methods approach that contributes to neighbourhood studies by uncovering multiple ways ‘place’ is embedded in the formation of trajectories.

There are few reasons to believe that social segregation is restricted to the working age population. Still, attempts to analyse social segregation among old age individuals have been lacking. The purpose of this paper is, therefore, to explore the extent to which old age individuals who follow different sociodemographic trajectories are geographically segregated. We analyse residential segregation among older age adults, based on an identification of typical sociodemographic life courses in longitudinal, geo-coded, register data for three life phases: young old, 65-79 years, middle old, 75-89 years and, oldest old, 85-99 years. The results show great life-course heterogeneity in these age groups. In each life phase, we distinguish seven different trajectory types that also display distinct geographical patterns. The first group of trajectories is characterized by a high proportion of married individuals with high income concentrated in suburban, single-family housing areas. The second group consists of trajectories with many widows/widowers in small-scale, apartment areas. The third group consists of singleton trajectories in metropolitan areas. The fourth group is overrepresented in low-income areas and consists of trajectories including individuals of nonsurvival in the life phase. The fifth group is composed of trajectories with married or widowed low-income individuals in owner-occupied areas mainly found in rural areas. Thus, there is pronounced geographical variation in what type of neighbourhood life old age individuals live. This indicates that social segregation in the old age population deserves to be given more attention in geographical research.

Studies of neighbourhood effects typically measure the neighbourhood context at one specific spatial scale. It is increasingly acknowledged, however, that the mechanisms through which the residential context affects individual outcomes may operate at different spatial scales, ranging from the very immediate environment to the metropolitan region. We take a multi-scale approach to investigate the extent to which concentrated poverty in adolescence is related to obtained education level and income later in life, by measuring the residential context as bespoke neighbourhoods at five geographical scales that range from areas encompassing the 200 nearest neighbours to areas that include the 200k+ nearest neighbours. We use individual-level geocoded longitudinal register data from Sweden and the Netherlands to follow 15/16-year-olds until they are 30 years old. The findings show that the contextual effects on education are very similar in both countries. Living in a poor area as a teenager is related to a lower obtained educational level when people are in their late 20s. This relationship, however, is stronger for lower spatial scales. We also find effects of contextual poverty on income in both countries. Overall, this effect is stronger in the Netherlands than in Sweden. Partly, this is related to differences in spatial structure. If only individuals in densely populated areas in Sweden are considered, effects on income are similar across the two countries and income effects are more stable across spatial scales. Overall, we find important evidence that the scalar properties of neighbourhood effects differ across life-course outcomes.

In this paper we analyse spatial and temporal variation in the risk of intensive care unit (ICU) admission for COVID-19 in Sweden. The analysis is based on geocoded and time-stamped data from the Swedish Intensive Care Registry (SIR). We merge this data with a classification of Swedish neighbourhood cluster types constructed from multi-scalar measures of socio-economic and country of birth segregation (Kawalerowicz and Malmberg in Multiscalar typology of residential areas in Sweden, 2021 available from https://doi.org/10.17045/sthlmuni.14753826.v1). We examine 1) if residence in more socio-economically deprived or diverse neighbourhood cluster types was associated with a higher risk of ICU admission for COVID-19, 2) if residence in more affluent neighbourhoods was associated with a lower risk of ICU admission for COVID-19, and 3) how these patterns changed over time during the three first waves of the pandemic. While the highest overall risk was associated with residence in urban disadvantage coupled with diversity, models where neighbourhood cluster types were interacted with waves reveal that the highest risk was associated with living in a neighbourhood cluster type characterised by rural town disadvantage coupled with diversity under the 3rd wave (February 2021–June 2021). Residence in such a neighbourhood cluster type was associated with a four times higher risk of ICU admission, compared to the reference category of living in a homogeneous rural neighbourhood cluster type with average levels of deprivation under wave 1. Looking at disparities within each wave we found that residence in most affluent urban areas was at first associated with a slightly higher risk of ICU admission for COVID-19 as compared with the reference category of living in a homogeneous rural neighbourhood cluster type, but under waves 2 and 3 this risk was no longer statistically significant. The largest inequalities between different neighbourhood cluster types could be seen during the 1st wave. Over time, the risks converged between different neighbourhood cluster types.