Ageing in Place Classification: Creating a geodemographic classification for the ageing population in England

Abstract

Population ageing is one of the most significant demographic changes underway in many countries. Far from being a homogenous group, older people and their experiences of ageing are diverse. A better understanding of the characteristics and geography of the older population, including the older workforce, is important. It allows policymakers and stakeholders to better adapt to the opportunities and challenges that the ageing population brings. This paper describes the implementation of the Ageing in Place Classification (AiPC) in England. AiPC is a multidimensional geodemographic classification, and it employs a wide range of spatially representative attributes of older people’s sociodemographic characteristics and their living environment at the small area level. The openly available product provides valuable insights that can be implemented in both local and national contexts, in particular to improve service delivery and inform targeted policy interventions. AiPC is readily updateable with the arrival of new Census data; the concept and framework are also transferable to other countries.

Clustergram: Visualization and diagnostics for cluster analysis

“…Another implementation has been explored in a blog post by Galili (2010) experimenting with the minimal (as well as unpackaged and unmaintained) R version, that was later incorporated in the EcotoneFinder package (Bagnaro, 2021). Since the first release of clustergram in November 2020, the package has been used in at least seven academic publications, ranging from the classification of geographical areas based on form and function Samardzhiev et al, 2022), geodemographics (Yang et al, 2022), clustering of the latent representation from convolutional neural networks (Singleton et al, 2022), classification of high Arctic lakes (Urbański, 2022) to facility reliability assessment (Stewart et al, 2022) and genomic data science (Ma et al, 2022). Since none of these directly cite the software, it is likely an incomplete overview.…”

How Active Are European Seniors—Their Personal Ways to Active Ageing? Is Seniors’ Activity in Line with the Expectations of the Active Ageing Strategy?

“…These include post-retirement lifestyle changes, distance from family, mobility, new technologies [16], anxiety, fears, inability to organise/plan leisure time [5,31,38], but also personality and behaviour patterns realised during working life [23,39]. These factors point to yet another aspect to be considered in AA: seniors are a heterogeneous group with diverse views and experiences of ageing [5,40].…”

A neighbourhood Output Area Classification from the 2021 and 2022 UK censuses

“…Applied to the UK, such classifications have provided nationwide snapshots of neighbourhood conditions on census nights since the 1970s (Charlton et al, 1985; Vickers & Rees, 2007; Webber, 1977). These depictions have laid the foundations for recent Office for National Statistics (ONS) Output Area Classifications (Gale et al, 2016; Vickers & Rees, 2007), and have spawned extensions to bespoke geodemographic applications within a variety of fields including education (Singleton & Longley, 2009), ageing populations (Yang et al, 2023), health (Petersen et al, 2011), public safety (Anderson, 2010; Ashby & Longley, 2005; Gulma, 2022) and digital equity (Longley et al, 2008; Singleton & Longley, 2015). Although most application‐specific geodemographic classifications make use of a wide range of administrative or consumer data sources, most remain heavily reliant upon census data for specification, estimation and testing of classifications (Harris et al, 2005; Stillwell, 2017).…”