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The migration of people affects the geographical distribution of the population and the demographic composition of areas over the short, medium and long terms. To recognise and respond to the corresponding needs and challenges, including consequences for service provision, social cohesion and population health, there is a continuing need to understand migration patterns of the past and present. Area classifications are a useful tool to simplify the inherently complex data on migration flows and characteristics. Yet, existing classifications often lack direct migration measures or focus solely on cross-sectional data. This study addresses these limitations by employing Group-based Multi-Trajectory Modelling (GBMTM) to create a longitudinal, migration-specific classification of Great Britain's wards from 1981 to 2011, using six migration indicators. Using UK census data, we reveal six distinct migration clusters that highlight the rapid growth in studentifying neighbourhoods, the continuous influx of migrants into inner cities, and a noticeable North-South divide in terms of movers’ tenure enforced by persisting income selectivity. Additionally, the geographical distribution of clusters shows a common pattern in urban areas irrespective of size or location. The longitudinal perspective of our GBMTM classification highlights trends and changes in migration patterns that are not well reflected in either the general purpose or the cross-sectional migration classification that we used as comparators. We conclude that the method presented and the classification generated offer a novel lens on migration and provide new opportunities to explore the effects of migration on a variety of outcomes and at various scales.

BACKGROUND AND OBJECTIVES: Evolutionary research on the sex ratio at birth (SRB) has focused on explaining variability within and between populations, and whether parental fitness is maximized by producing daughters or sons. We tested predictors of SRB in a low-income setting, to understand whether girls differ from boys in their likelihood of being born into families with the capacity to invest in them, which has implications for their future health and fitness. METHODOLOGY: We used data from a cluster randomized control trial from lowland rural Nepal (16 115 mother-child dyads). We applied principal component analysis to extract two composite indices reflecting maternal socio-economic and reproductive (parity, age) capital. We fitted mixed-effects logistic regression models to estimate odds ratios of having a girl in association with these individual factors and indices. RESULTS: The SRB was 112. Compared to the global reference SRB (105), there were seven missing girls per 100 boys. Uneducated, early-marrying, poorer and shorter mothers were more likely to give birth to girls. Analysing composite maternal indices, lower socio-economic and reproductive capital were independently associated with a greater likelihood of having a girl. CONCLUSIONS AND IMPLICATIONS: In this population, girls start life facing composite disadvantages, being more likely than boys to be born to mothers with lower socio-economic status and reproductive capital. Both physiological and behavioural mechanisms may contribute to these epidemiological associations. Differential early exposure by sex to maternal factors may underpin intergenerational cycles of gender inequality, mediated by developmental trajectory, education and socio-economic status.

Background: Women's early marriage (, Leverhulme Trust (RPG-2017-264) and DFID South Asia Hub (Grant Number: PO 5675).