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.

Researchers often use register data-based measures of neighborhood characteristics to estimate neighborhood effects. However, the underlying causal mechanisms might be based on the perception of such characteristics. The mismatch between the measures and perceptions is likely influenced by individuals’ characteristics and attitudes. This paper investigates the relationship between the measured percentages of low-income and foreign background neighbors and their perceptions. Using Dutch register data merged with survey data, we found that higher education is associated with the underestimation of both characteristics, but after controlling for individual institutional trust it becomes less significant to the perceived percentage of foreign background neighbors, and insignificant to that of low-income neighbors. Individuals with lower institutional trust are likely to overestimate both percentages. Older age, greater wealth and greater social embeddedness are associated with underestimation of both characteristics, and higher household income with the underestimation of the share of low-income neighbors. We find that trust in democratic and public institutions is associated with biased perceptions of social reality, which can be an important finding for the spatial inequality studies. Our results also suggest that urban research would benefit from augmenting administrative datasets with surveys and interviews.

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).

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.

Background
The genetic and environmental aetiology of autistic and Attention Deficit Hyperactivity Disorder (ADHD) traits is known to vary spatially, but does this translate into variation in the association of specific common genetic variants?

Methods
We mapped associations between polygenic scores for autism and ADHD and their respective traits in the Avon Longitudinal Study of Parents and Children (N = 4,255–6,165) across the area surrounding Bristol, UK, and compared them to maps of environments associated with the prevalence of autism and ADHD.

Results
Our results suggest genetic associations vary spatially, with consistent patterns for autistic traits across polygenic scores constructed at different p-value thresholds. Patterns for ADHD traits were more variable across thresholds. We found that the spatial distributions often correlated with known environmental influences.

Conclusions
These findings shed light on the factors that contribute to the complex interplay between the environment and genetic influences in autistic and ADHD traits.

Neighbourhood effects studies typically investigate the negative effects on individual outcomes of living in areas with concentrated poverty. The literature rarely pays attention to the potential beneficial effects of living in areas with concentrated affluence. This poverty paradigm might hinder our understanding of spatial context effects. Our paper uses individual geocoded data from the Netherlands to compare the effects of exposure to neighbourhood affluence and poverty on educational attainment within the same statistical models. Using bespoke neighbourhoods, we create individual neighbourhood histories which allow us to distinguish exposure effects from early childhood and adolescence. We follow an entire cohort born in 1995 and we measure their educational level in 2018. The results show that, in the Netherlands, neighbourhood affluence has a stronger effect on educational attainment than neighbourhood poverty for all the time periods studied. Additionally, interactions with parental education indicate that children with higher educated parents are not affected by neighbourhood poverty. These results highlight the need for more studies on the effects of concentrated affluence and can inspire anti-segregation policies.

The concept of urban living is evolving, and there is a growing interest in creating smaller, more connected, and hyperlocal neighbourhoods, where everything people need is within a 15-minute walk or bike ride. This paper challenges the concept of the ‘15-minute city’ as a panacea for urban ills, by exploring the history of utopian urban planning and regeneration aimed at creating sustainable, inclusive and vibrant communities by desegregating disadvantaged groups. Specifically, we examine social mixing policies, which are recurring top-down interventions that pathologise concentrated urban disadvantage. We trace the evolution of these policies in Europe from the Garden City movement to post-war social housing redevelopment to the current 15-minute city, which we consider to be social mix by stealth. While such policies can reduce the degree of concentrated disadvantage in the short term, they tend to be ineffective in the long term, as deprived neighbourhoods often remain so despite attempts to make them more diverse. The paper argues that the 15-minute city would be implemented through de facto social mix actions at the neighbourhood level, which are insufficient to address the deeper structural issues that perpetuate spatial inequality and deprivation. We propose that longitudinal and comparative analyses, combined with ‘right to the city’ perspectives, should be considered in future research and policymaking to understand – and more importantly address – why urban renewal initiatives that aim for equitable outcomes at the neighbourhood scale ultimately fail to deliver.

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

In the UK the socio-economic impacts of the COVID-19 mitigations over the course of the pandemic (March 2020 to the time of writing in January 2022) have been experienced unevenly and with differential intensities at both the regional and local scales. Using individual-level geocoded data (from the Understanding Society: UK Household Longitudinal Survey COVID-19 study) linking people to the places in which they live, we consider the regional and local disparities in the risks and outcomes of financial hardship as a result of early stage mitigations. This paper provides direct evidence from the UK of a concentration of vulnerabilities in areas of high deprivation, undermining the capacity of individuals within those areas to shelter from economic shocks. Furthermore, the geography of financial hardship appears largely compositional–attributable to the pre-existing characteristics of individuals within regions and neighbourhoods, rather than being explicitly driven by the spatial contextual effect of their social or physical environments. This has implications for UK regional economic policy, and the Levelling Up agenda in particular. It is not the regions and neighbourhoods that give rise to COVID-19 hardship per se, but the concentration of individual disadvantages of the people living within them. The persistence of compositional dis/advantages means that there is a need not only to direct ameliorative packages to the individual but also to use local areas as places where the (regional) Levelling Up agenda can break long-term place trajectories that lock in existing disparities which in turn yield unequal financial opportunities and outcomes in periods of crisis.

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.

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 chapter explores multi-scale estimation methods as an important future direction for segregation research in China. We explain how these recently developed methods help address many longstanding problems in traditional index-based segregation research and open up new avenues of research on Chinese cities. We explain the conceptual framework underpinning multilevel analysis in the form of a series of propositions that capture the theoretical basis and outline why a multilevel approach to segregation is advantageous. We then illustrate how this approach can be applied to China using census data on Shijiazhuang, the capital city of Hebei Province. We use the model to consider segregation of different ethnic groups and of migrants versus non-migrants. We conclude with a discussion of our findings and our thoughts on future directions for research and the implications for policy.

The neighbourhood in which people live reflects their social class and preferences, so studying socio-spatial mobility between neighbourhood types gives insight into the openness of spatial class structures of societies and into the ability of people to leave disadvantaged neighbourhoods. In this paper we study the extent to which people move between different types of neighbourhoods by socio-economic status in different inequality and segregation contexts in four European countries: Sweden, the Netherlands, the UK (England and Wales), and Estonia. The study is based on population registers and census data for the 2001–2011 period. For England and Wales, which has long had high levels of income inequalities and high levels of socio-economic segregation, we find that levels of mobility between neighbourhood types are low and opportunities to move to more socio-economically advantaged neighbourhoods are modest. In Estonia, which used to be one of the most equal and least segregated countries in Europe, and now is one of the most unequal countries, we find high levels of mobility, but these reproduce segregation patterns and it is difficult to move to less deprived neighbourhoods for those in the most deprived neighbourhoods. In the Netherlands and Sweden, where income inequalities are the smallest, it is the easiest to move from the most deprived to less deprived neighbourhoods. The conclusion is that the combination of high levels of income inequalities and high levels of spatial segregation tend to lead to a vicious circle of segregation for low-income groups, where it is difficult to undertake upward socio-spatial mobility.

Based on data from the 1980s, Sassen’s influential book ‘The Global City’ interrogated how changes in the occupational structure affect socio-economic residential segregation in global cities. Here, using data for New York City, London and Tokyo, we reframe and answer this question for recent decades. Our analysis shows an increase in the share of high-income occupations, accompanied by a fall in low-income occupations in all three cities, providing strong evidence for a consistent trend of professionalization of the workforce. Segregation was highest in New York and lowest in Tokyo. In New York and London, individuals in high-income occupations are concentrating in the city centre, while low-income occupations are pushed to urban peripheries. Professionalization of the workforce is accompanied by reduced levels of segregation by income, and two ongoing megatrends in urban change: gentrification of inner-city neighbourhoods and suburbanization of poverty, with larger changes in the social geography than in levels of segregation.

Analyses of health over time must consider the potential impacts of ageing as well as any effects relating to cohort differences. The British Household Panel Survey (BHPS) and Understanding Society longitudinal studies are employed to assess trends in mental ill-health over a 26-year period. This analysis uses cross-classified multilevel models in an exploratory, non-parametric approach to evaluate age and cohort effects net of each other. Mental ill-health evidences an initial worsening trend as people age which then reverses and exhibits improvement in late-middle-age, before declining again in the latter stages of life. There were less defined cohort trends. The modelling technique also reveals the relative importance of the temporal contexts in relation to inter- and intra-individual effects on mental ill-health, demonstrating that the ageing and cohort dimensions explain little variation compared to these more dominant within and between influences. Ultimately, we suggest that researchers would benefit from wider use of this exploratory modelling strategy when evaluating underlying health trends and more research is now needed to explore potential explanations of these baseline trajectories.

Understanding how inequalities are transmitted through generations and restrict upward spatial mobility has long been a concern of geographic research. Previous research has identified that the neighborhood in which someone grows up is highly predictive of the type of neighborhood he or she will live in as an independent adult. What remains largely unknown is the relative contribution of geography compared to the contribution of the family context in forming these individual life outcomes. The aim of this article is to better understand the role of the spatial–temporal contexts of individuals in shaping later life outcomes, by distinguishing between inherited disadvantage (socioeconomic position) and spatial disadvantage (the environmental context in which children grow up). We use a sibling design to analyze the neighborhood careers of adults after they have left the parental home, separating out the roles of the family from that of the neighborhood in determining residential careers. We employ rich Swedish Register data to construct a quasi-experimental family design to analyze residential outcomes for sibling pairs and contrast real siblings against a control group of “contextual siblings.” We find that real siblings live more similar lives in terms of neighborhood experiences during their independent residential careers than contextual sibling pairs but that this difference decreases over time. The results show the importance of geography, revealing long-lasting stickiness of spatial–temporal contexts of childhood.

Investigating biologically plausible mechanisms for the embodiment of context is a key thoroughfare for progressing health geographies of place. Expanding knowledge of bio-processes such as epigenetics is providing a platform for appreciating the dynamic embedding of social relations in bodies over the lifecourse, and so to tracing the development of health inequalities. By providing a geographic lens on the biosocial, health geographers have key contributions to make regarding the theorisation of place. We put forward the exposome as a holistic framework in which to situate a biosocial health geography, placing ideas of dynamic exposure, plasticity and temporality as central.

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.