Classifying the Older Population

Ageing in Place Classification (AiPC)

The population of England is ageing. By 2041, approximately 26% of the UK's population will be aged 65 and over, with those aged 50+ likely comprising around half the adult population1. This demographic change represents a significant challenge and as such developing places that are suitable for residents to 'age in place' will be one of the principal goals for policy makers over the coming decades.

In this project, funded by Nuffield Foundation, the researchers have created a bespoke multidimensional geodemographic classification of the older population in England (those aged 50+) and demonstrated its utility. Such a geodemographic classification can provide a unique policy resource that captures the social and spatial heterogeneity of the older population in England by combining traditional and novel data sources.

Based on extensive literature review and consultations with our panel of experts, the researchers identified nine inter-connected domains (People, Housing, Work and education, Mobility, Financial Security, Digital, Health, Outdoor Space and Living Environment and Civic participation) for which measures were then generated, reflecting characteristics of older people and the places in which they live. The study used a methodology based on previous geodemographic classifications, such as the OAC2. A robust clustering algorithm, called 'k-means', was implemented to organise small geographical areas (Lower Super Output Areas) into categories (clusters) that share similar attributes across space.

The Ageing in Place Classification (AiPC) resulted in 5 'Supergroups' and 13 nested 'Groups' as shown in Table 1 below. The characteristics of all clusters have been examined and given descriptions and names (Pen Portraits) depicting their key characteristics. The Pen Portraits are available from the CDRC website.

Table 1: AiPC supergroups and nested groups

Source: Yang, Dolega and Pollock-Darlington (2022)

The AiPC classification is also visualised on an interactive web map hosted on the Consumer Data Research Centre (CDRC) platform. Users are able to zoom in and out, pan around and identify particular features such as individual clusters, LSOAs or postcodes and accompanying pen portraits.

Figure 1: AiPC visualisation on an interactive CDRC map

The utility of AiPC - case studies

Research shows that general-purpose geodemographic classifications can be successfully applied to provide evidence-based policy guidelines and interventions. As such a more detailed understanding of the geography of the ageing population's characteristics and dwelling environments is essential to better target interventions and allocate resources. Using the AiPC the researchers were able to investigate the utility of the developed geodemographic classification through a series of research questions relating to the three following themes:

1) Neighbourhoods – develops a realistic alternative understanding of the 20-minute city for the ageing population using Liverpool City Region (LCR) as a case study. The study shows how the accessibility for essential services varies across various AiPC clusters and how the interpretation of the 20-minute city 'narrows' when limited mobility/walking pace amongst older citizens is accounted for as shown in Figure 2 below.

Figure 2: Accessibility score for "Standard Walking Speed" and "Reduced Walking Speed" groups of people aged 50+

2) Housing - explores how the AiPC can support understanding of the ageing population satisfaction with their dwellings. It generates first-of-its-kind small area estimates (SAE) based on English Housing Survey (EHS) data and examines how the housing satisfaction estimates vary across the AiPC clusters (Figure 3).

Figure 3: Accommodation dissatisfaction across AiPC supergroups

3) Society - develops a small area estimation (SAE) model of the levels of loneliness across the 50+ population based on the English Longitudinal Study of Ageing (ELSA) survey data and examines how the implementation of the AiPC classification can enhance these estimates.

More details on building the Ageing in Place Classification and its utility explored in the three abovementioned case studies are available from the official research report here.

Notes:

1. ONS (2018) Overview of the UK Population: November 2018. Office for National Statistics, Available at: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/articles/overviewoftheukpopulation/november2018

2. Gale, C. G., Singleton, A., Bates, A. G., & Longley, P. A. (2016). Creating the 2011 area classification for output areas (2011 OAC). _Journal of Spatial Information Science, 12, 1-27.

3. Ageing in Place Classification is available at:
   https://data.cdrc.ac.uk/dataset/ageing-place-classification-aipc