Understanding the spatial patterns of social inequalities has been a longstanding concern in urban studies. Geodemographic classifications, which group neighbourhoods based on multiple social and physical dimensions, offer a useful tool for this purpose. However, most classifications rely on fixed single-scale administrative boundaries, while studies that adopt multiscale approaches often focus on a single dimension and cover only limited time periods. This limits our understanding of how urban social inequalities evolve over time and across spatial scales. In this study, we extend the geodemographic approach to incorporate multiple dimensions, time periods, and geographical scales, enabling a more comprehensive analysis of the spatio-temporal configuration of urban change. We develop multidimensional, multiscale, and longitudinal spatial profiles of residential contexts in the Metropolitan Agglomeration of Amsterdam (MAA) using bespoke neighbourhoods constructed from detailed population register data (1999–2022). Our results show that the interaction of socioeconomic status, migration background, life-course stages, and housing tenure provides a richer understanding of urban stratification than traditional models based solely on income or ethnicity. The longitudinal perspective reveals distinct timing differences in urban reconfigurations, such as gentrification and displacement, which emerge locally and consolidate more broadly over time. The multiscale approach highlights how patterns of urban change are scale-dependent, with large-scale dynamics, such as poverty suburbanisation and inner-city gentrification, coexisting with the formation of smaller enclaves in areas undergoing or at risk of change. These findings highlight the need for integrated multidimensional, temporal, and multiscale frameworks to better capture the evolving nature of sociospatial inequalities in cities.

Neighborhood effects research focuses on the residential neighborhood, assuming it as the main spatial context relevant to individual outcomes. Individuals, however, are mobile and visit various spatial contexts other than the residential neighborhoods. This article conceptualizes contextual exposures to socioenvironmental factors in daily activity spaces and their relationship with residential exposures. By introducing regression toward the mean, we argue that mobility-based contextual exposures are, on average, less extreme than residential exposures. Previous neighborhood effects studies therefore tend to underestimate actual spatial contextual effects when they misrepresent residential neighborhood effects as the total contextual effects. Despite improved measurement accuracy with the transition from residence- to mobility-based exposures, we suggest the complexities remaining in the estimation of spatial contextual effects from a geographic perspective. These complexities include a possibly limited extent of neighborhood effects regression across neighborhoods and asymmetrical dispersion of between-individual contextual exposures within each neighborhood.

Traditionally, studies of spatial inequalities only consider one single dimension, such as income, and one spatial scale - usually a neighbourhood determined by administrative boundaries. Although the existing literature increasingly recognises the multifaceted nature of inequalities in cities, this paper introduces a novel approach by integrating the multidimensional and multiscale perspectives to understand the evolution of social and spatial inequalities over time. Drawing on clustering techniques based on factor analysis and using individual-level geocoded register data from the metropolitan agglomeration of Amsterdam, our methodology classifies neighbourhoods by grouping detailed residential locations with similar socioeconomic, demographic and housing characteristics across multiple geographical scales. Through sequence analysis, we identify trajectories of neighbourhood change from 1999 to 2022, revealing patterns in the timing, duration, and sequencing of shifts across various dimensions. Our results bridge gaps in the multidimensional and multiscale neighbourhood classification literatures, providing a better understanding of how social inequalities interact and overlap in space. By examining the path dependence between different dimensions of spatial and social inequalities, this study provides insights into the processes that produce and reproduce social stratification in cities that may act at different geographical scales for different groups of people. Moreover, the rich and granular data paint a detailed picture of how residential contexts are segregated and how the trajectories of neighbourhood change are distributed spatially. This research offers an innovative framework for visualise and study the dynamic evolution of urban structures over time.

There is a longstanding interest in the causes and consequences of socio-spatial inequalities in cities. A large literature has emerged on so-called neighbourhood effects, which seeks to understand how living in neighbourhoods of concentrated poverty affects a range of individual outcomes, such as health, income, education and general wellbeing (Galster, 2012). The literature on neighbourhood effects has developed rapidly in the last three decades. It is now common practice that studies of neighbourhood effects use geocoded longitudinal individual-level data and employ a variety of (often econometric) approaches in an attempt to reduce bias from non-random sorting into neighbourhoods (Knies et al. 2021). Studies of neighbourhood effects have also increasingly looked to incorporate more personal geographic contexts replacing ‘off the shelf’ administrative units with bespoke neighbourhoods (Johnston et al., 2005; Andersson & Malmberg, 2014; Petrović et al. 2022). The most common example of bespoke neighbourhoods are egohoods – neighbourhoods placing everyone at the centre of their own personal residential space (Hipp & Boessen, 2013). More recently, multiscale approaches have been used, whereby neighbourhood characteristics are measured at multiple scales of bespoke neighbourhoods (Petrović et al., 2022). It has been argued that for both theoretical and empirical reasons, the term ‘neighbourhood effects’ should be replaced by the more encompassing term ‘spatial context effects’, as many of the assumed spatial effects are not confined to residential neighbourhoods and the contestable meaning of neighbourhood distracts (Petrović et al. 2019).

Emerging longitudinal research on the relationship between commuting mode and psychological wellbeing draws exclusively from cities in developed countries and the findings are not consistent. Our study contributes to the evidence base from urban China, where rapid urban growth has raised great concerns for urbanites’ commuting problems and psychological stress risks. Drawing upon the China Health and Nutrition Survey (2006–2015), we followed a quasi-longitudinal design to examine changes in commuting mode and the associations with long-term psychological stress. Crucially, the neighbourhood-level urbanicity scale was incorporated to analyse geographic variations in the commuting-stress relationship over time. The results show that maintaining car commuting and long-duration active commuting were associated with lower levels of psychological stress, while long-duration motorised commuting trips by car or public transport were predictive of higher stress levels. Moreover, high-urbanicity areas involved more active commuting trips and short motorised commuting trips, which were beneficial to long-term psychological wellbeing. In contrast, the commuting-related stress risks were noticeable in medium urbanicity areas, where the commuting duration by public transport was extremely high. Based on the socio-institutional context of urban growth in China, we recommend that urban governments should change the focus from expanding urban development land to improving urban amenities and urbanites’ wellbeing.

Research on the experienced utility of commuting time is dominated by an individualistic view of choice concerning the trade-offs between long commutes and job- or housing-related benefits. The widely discussed phenomenon of the commuting paradox shows that individuals systematically report worse subjective wellbeing as commuting time increases over time, indicating the incomplete trade-offs and net disutility for long commutes at the individual level. This paper takes a household perspective and conducts one of the first longitudinal studies on the gendered relationship between commuting time and subjective wellbeing in China. Drawing upon the China Health and Nutrition Survey between 2006 and 2015, we used seemingly unrelated regression models and fixed-effect models not only to compare the within-individual effect but also to investigate the spill-over effect of commuting time on life satisfaction between matched samples of husbands and wives. We additionally examined the role of preschool-aged children and co-residence with their grandparents in the gendered commuting-wellbeing relationship. The results supported the individual-level commuting paradox, considering that both partners had lower levels of life satisfaction with the increase of their own commuting time. Interestingly, husbands’ life satisfaction was more negatively affected by wives’ commuting time than vice versa, while wives’ commuting utility was more related to the great time pressure from childcare and the social support from extended family members. Our research findings have implications for urban planning and governance policies which aim at mitigating job-housing mismatch, delivering accessible childcare services and transforming gendered social norms.

Residential self-selection studies argue that pre-existing travel-related attitude overshadows the role of changes in residential built environment in (re)shaping travel behaviours. Our study contributes to this self-selection argument by including family- and job-related life events as another self-selection source, and accounting for the reverse causality from built environment to travel attitude as opposed to the attitude-induced self-selection. Using a two-wave sample of 1,038 Dutch residents before and after the relocation, we developed structural equation models to investigate longitudinal relationships between changes in residential built environment and job-housing distances, the occurrence of life events, and changes in commuting mode choices and preferences pre-post relocation. Results supported residential self-selection arising from pre-existing preferences for car and public transport commuting, while residents lowered the active commuting preference after moving to a more suburban neighbourhood. Life events concurrent with residential relocation, such as childbirth and job changes, also underlay greater demand for car use.

Movie shown during BK Expo: 'MAPS. New Cartographies, New Narratives' (5 December 2023 - 18 January 2024).

Working from home (WFH) was prevalent among previous daily commuters during the COVID-19 pandemic and is expected to continue in post-COVID-19 society. By using WFH enforced by the UK government during the pandemic as a real-world experiment, our study investigates the relationship between switching from commuting to WFH, and subjective wellbeing (SWB). Particular interest lies in determining the extent to which this relationship depends on homeworkers' commuting behaviours prior to the COVID-19 outbreak. The data we used is from a COVID-19 panel survey on UK workers who were born in 1970. Results from the between-individual analysis and fixed-effect analysis show that the transition to WFH was not indicative of life satisfaction but it was conducive to affective wellbeing in the short term. This positive effect on affective wellbeing became insignificant after specific experiences of WFH were taken into account; that is, positive homeworking experiences were a result of strong social support, healthy daily lifestyles and stable financial circumstances during the pandemic. Crucially, the impact of switching to WFH on SWB was moderated by the pre-pandemic commuting behaviour. Previous long-distance commuters (one-way commuting distance >30 miles) reported better affective wellbeing when they could work from home, while commuters who had frequently walked or cycled to work had worse SWB outcomes after switching to WFH.

There is no theoretical reason to assume that neighborhood effects operate at a constant single spatial scale across multiple urban settings or over different periods of time. Despite this, many studies use large, single-scale, predefined spatial units as proxies for neighborhoods. Recently, the use of bespoke neighborhoods has challenged the predominant approach to neighborhood as a single static unit. This article argues that we need to move away from neighborhood effects and study multiscale context effects. The article systematically examines how estimates of spatial contextual effects vary when altering the spatial scale of context, how this translates across urban space, and what the consequences are when using an inappropriate scale, in the absence of theory. Using individual-level geocoded data from The Netherlands, we created 101 bespoke areas around each individual. We ran 101 models of personal income to examine the effect of living in a low-income spatial context, focusing on four distinct regions. We found that contextual effects vary over both scales and urban settings, with the largest effects not necessarily present at the smallest spatial scale. Ultimately, the magnitude of contextual effects is determined by various spatial processes, along with the variability in urban structure. Therefore, using an inappropriate spatial scale can considerably bias (upward or downward) spatial context effects.

The relationship between commuting behaviours and subjective wellbeing has been fascinating scholars of different disciplines. Especially in the last decade, longitudinal research designs have made great progress in identifying causality in the commuting-wellbeing relationship by focusing on within-individual variations over time. However, the results from longitudinal research are far from consistent and, therefore, questions remain unanswered regarding the association of motorised and long commuting journeys with subjective wellbeing in the long term. The aim of this literature review is to account for why these inconsistencies occur and to provide some avenues for future longitudinal research. We achieve this by developing theoretical conceptualisations of the commuting-wellbeing relationship from an interdisciplinary perspective, which drives the subsequent critical review of empirical longitudinal evidence based on nation/city-wide panel surveys, intervention experiments and relocation events. We recommend furture research to distinguish and integrate different processes that lead to changes in commuting behaviours, including environmental changes, information or participatory interventions, and the event of residential relocation together with other life events and long-term processes in life. This processual thinking will enrich the temporal scope of longitudinal research and contribute to a better understanding of the interdependent relationship between daily commuting behaviours and long-term subjective wellbeing.

Contextual poverty refers to high proportions of people with a low income in a certain (residential) space, and it can affect individual socioeconomic outcomes as well as decisions to move into or out of the neighbourhood. Contextual poverty is a multiscale phenomenon: Poverty levels at the regional scale reflect regional economic development, while meso-scale concentrations of poverty within cities are related to city-specific social, economic and housing characteristics. Within cities, poverty can also concentrate at micro spatial scales, which are often neglected, largely due to a lack of data. Exposure to poverty at lower spatial scales, such as housing blocks and streets, is important because it can influence individuals through social mechanisms such as role models or social networks. This paper is based on the premise that sociospatial context is necessarily multiscalar, and therefore contextual poverty is a multiscale problem which can be better understood through the inequality within and between places at different spatial scales. The question is how to compare different spatial contexts if we know that they include various spatial scales. Our measure of contextual poverty embraces 101 spatial scales and compares different locations within and between municipalities in the Netherlands. We found that the national inequality primarily came from the concentrations of poverty in areas of a few kilometres, located in cities, which have different spatial patterns of contextual poverty, such as multicentre, core-periphery and east–west. In addition to the inequality between municipalities, there are considerable within-municipality inequalities, particularly among micro-areas of a few hundred metres.

This thesis has developed alternative methods of operationalising neighbourhoods at multiple spatial scales and used them to advance our understanding of spatial inequalities and neighbourhood effects. The underlying problem that motivated this thesis is that many empirical studies use predefined administrative units, and often this does not align with the underlying theory or geography. Despite the extensive literature on neighbourhood effects and, more generally, on sociospatial inequalities, spatial scale remains an under-analysed concept. As a response to this research gap, this thesis takes a multiscale approach to both theory and the empirical analysis of neighbourhood effects, highlighting the multitude of spatial processes that may affect individual outcomes of people. To operationalise this, we created bespoke areas (centred around each residential location) at a range of one hundred scales representing people’s residential contexts, primarily in the Netherlands but also in multiple European capitals. Using microgeographic data and a large number of scales combined with small distance increments revealed subtle changes in sociodemographic characteristics across space. In doing so, we provided new insights into ethnic segregation, potential exposures to poverty, and neighbourhood effects on income, all in light of the fundamental issue of spatial scale: The analyses of sociospatial inequalities are substantially affected by the scale used to operationalise spatial context, and this varies within and between cities and urban regions. The aim of this thesis was therefore not to find a single, ‘true’ scale of neighbourhood, but to acknowledge, operationalise, and better understand the multiplicity of spatial scales.

Theory behind neighbourhood effects suggests that people’s spatial context potentially affects individual outcomes across multiple scales and geographies. We argue that neighbourhood effects research needs to break away from the ‘tyranny’ of neighbourhood and consider alternative ways to measure the wider sociospatial context of people, placing individuals at the centre of the approach. We review theoretical and empirical approaches to place and space from diverse disciplines, and explore the geographical scopes of neighbourhood effects mechanisms. Ultimately, we suggest how microgeographic data can be used to operationalise sociospatial context, where data pragmatism should be supplanted by a theory-driven data exploration.

Contextual poverty is a multiscale phenomenon which affects socioeconomic outcomes of people as well as individual decisions to move in or out of the neighbourhood. Large-scale poverty reflects regional economic structures. Mesoscale concentrations of poverty within cities are related to city-specific social, economic and housing characteristics. Exposure to poverty at small spatial scales influences individuals through social mechanisms such as role models or social networks. At all these scales, poverty is a relative phenomenon, defined based on a certain local or national standard. Particularly smaller spatial scales of exposure to poverty are often neglected, largely due to the lack of data. Register data for the full population of the Netherlands, geocoded to 100m by 100m grid cells, makes it possible to consider a wide range of scales. However, altering scale yields different empirical results, as stated within the modifiable areal unit problem (MAUP). Our measure of contextual poverty, therefore, embraces a range of spatial scales of contexts and compares different places within and between cities, revealing different spatial patterns of multiscale poverty.

Neighbourhood effects research identifies a variety of mechanisms through which residential context may influence individual socioeconomic status. These contextual influences operate at different spatial scales, and, what is more, the scale may not remain constant in various settings. Yet, most empirical studies use predefined administrative units, often at only one scale, as proxies for neighbourhood, and look at single cities. Bespoke neighbourhoods, delineated around each person, sometimes at a few spatial scales, challenge the predominant understanding of neighbourhood as a single static unit. This paper examines to which extent the estimates of neighbourhood effects vary when residential context is measured at various spatial scales in different urban settings, and what are potential assets and fallacies of bespoke neighbourhoods for studying contextual effects. Using individual-level geocoded data for the whole population of the Netherlands, we delineated areas of 101 different sizes around each residential location. We measured socioeconomic compositions of these areas, and modelled their effects on personal income to observe changes of the effect over distance. We found that the contextual effects vary over scales in different ways in different urban settings, with the biggest effect not necessarily operating at the smallest spatial scale. This means that using an
inappropriate spatial scale can considerably bias the results of neighbourhood effects models and that the geographical setting is crucial for the scalar variability of contextual effects. Ultimately, the paper identifies potential fallacies and assets of multiscale bespoke neighbourhood for understanding residential contexts and their effects on individual socioeconomic status.

Theory behind neighbourhood effects suggests that different geographies and scales affect individual outcomes. We argue that neighbourhood effects research needs to break away from the tyranny of neighbourhood and consider alternative ways to measure the wider socio-spatial context of people, placing individuals at the centre of the approach. We review theoretical and empirical approaches to place and space from a multitude of disciplines and the geographical scopes of neighbourhood effects mechanisms. Ultimately, we suggest ways in which micro-geographic data can be used to operationalise socio-spatial context for neighbourhood effects, where data pragmatism should be supplanted by a theory-driven data exploration.Unexpectedly, children who lived in an affluent neighbor-hood throughout childhood were most likely to engage in delinquent behavior.

Contextual poverty is a multiscale phenomenon which affects socioeconomic outcomes of people as well asindividual decisions to move in or out of the neighbourhood. Large-scale poverty reflects regional economicstructures. Meso-scale concentrations of poverty within cities are related to city-specific social, economic andhousing characteristics. Exposure to poverty at small spatial scales influences individuals through socialmechanisms such as role models or social networks. Particularly these smaller scales are often neglected,largely due to the lack of data. Register data for the full population of the Netherlands, geocoded to 500m by500m grid cells, makes it possible to consider a wide range of scales. However, altering scale yields differentempirical results, as stated within the modifiable areal unit problem (MAUP). Our measure of contextualpoverty, therefore, embraces a range of spatial scales of contexts and compares different places within andbetween cities, revealing different spatial patterns of multiscale poverty.

Appreciating spatial scale is crucial for our understanding of the sociospatial context. Multiscale measures of population have been developed in the segregation and neighborhood effects literatures, which have acknowledged the role of a variety of spatial contexts for individual outcomes and intergroup contacts. Although existing studies dealing with sociospatial inequalities increasingly explore the effects of spatial scale, there has been little systematic evidence on how exposure to sociospatial contexts changes across urban space, both within and between cities. This article presents a multiscale approach to measuring potential exposure to others. Using individual-level register data for the full population of The Netherlands and an exceptionally detailed multiscalar framework of bespoke neighborhoods at 101 spatial scales, we measured the share of non-Western ethnic minorities for three Dutch cities with different urban forms. We created individual and cumulative distance profiles of ethnic exposure, mapped ethnic exposure surfaces, and applied entropy as a measure of scalar variation to compare potential exposure to others in different locations both within and between cities. The multiscale approach can be implemented for examining a variety of social processes, notably segregation and neighborhood effects.