Kienast-von Einem Caroline, Panter Jenna, Reid Alice
MRC Epidemiology Unit University of Cambridge Cambridge UK.
Department of Geography University of Cambridge Cambridge UK.
Popul Space Place. 2023 Oct;29(7):e2694. doi: 10.1002/psp.2694. Epub 2023 Jul 14.
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 U.K. 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.
人口迁移在短期、中期和长期内都会影响人口的地理分布以及各地区的人口结构。为了认识并应对相应的需求与挑战,包括对服务提供、社会凝聚力和人口健康的影响,持续了解过去和现在的迁移模式至关重要。区域分类是一种有用的工具,可简化有关迁移流动和特征的固有复杂数据。然而,现有的分类方法往往缺乏直接的迁移衡量指标,或仅关注横截面数据。本研究通过采用基于群体的多轨迹建模(GBMTM)来解决这些局限性,利用六个迁移指标对1981年至2011年英国的选区进行了纵向的、针对迁移的分类。通过使用英国人口普查数据,我们揭示了六个不同的迁移集群,这些集群凸显了学生化社区的快速增长、移民持续涌入内城区,以及由于持续的收入选择性而导致的迁移者居住期限方面明显的南北差异。此外,集群的地理分布在城市地区呈现出一种共同模式,而不论城市规模或位置如何。我们的GBMTM分类的纵向视角突出了迁移模式中的趋势和变化,而这些在我们用作比较的通用或横截面迁移分类中都没有得到很好的体现。我们得出结论,所提出的方法和生成的分类为研究迁移提供了一个新的视角,并为探索迁移在各种规模上对多种结果的影响提供了新的机会。