The Ageing in Place Classification (AiPC) classifies population aged 50 years old and older in England. The geodemographic consists of two tiers Tier 1, the Supergroups, contains five clusters providing the most generic descriptions of the older population (aged 50 and over) and their living environments. Tier 2, the Groups, further differentiates within the five clusters of Tier 1 giving an additional 13 clusters.
The classification is based on multiple high quality data sources including ONS 2011 Census, British Population Survey (BPS), Access to Healthy Assets & Hazards (AHAH), NHS English Prescribing Data (EPD), Registered Patient (RP). It contains all the 32,844 LSOAs in England classified by the AiPC. Each row/LSOA is assigned to a AiPC Supergroup name and code and a Group name and code.
The AiPC is the first-of-its-kind bespoke geodemographic classification in England targeting the population aged 50 years old and older. Source: Consumer Data Research Centre
You can access the online map for free from CDRC (Consumer Data Research Centre) Mapmaker platform here.
The paper describing the approach of creating AiPC is open access and freely available here.
You can have access to different formats of AiPC and documentations from CDRC (Link), these include:
- Pen Portraits
- Data Profile
- Ageing in Place Classification (.csv file)
- AiPC GeoPackage
- AiPC Shapefile
This GitHub repository contains the codes (in R) for creating the AiPC, focusing on the two-tier clustering analysis and profiling.
- AiPC code 1 - pre-clustering.R: R code for pro-clustering (preproccessing), Raw inputs to normalised and standardised inputs;
- AiPC code 2 - clustering.R: R code for clustering analysis - preprocessed data to cluster results;
- AiPC code 3 - profiling.R: R code to create cluster profiles (e.g. radar plots) for AiPC supergroups and groups
- ancillary 1 - clustergram.ipynb: Python code for creating clustergram at different tiers.
This research is funded by Nuffield Foundation; grant ref: WEL/44091.2
The BPS (British Population Survey) data in this research has been provided by the Consumer Data Research Centre (CDRC), an ESRC Data Investment, under project ID CDRC 119, ES/L011840/1; ES/L011891/1. AHAH (Access to Healthy Assets & Hazards) dataset, broadband download speed data and CDRC Residential Mobility index are also provided by CDRC and have been used in this work.
We would like to thank Ordnance Survey for providing the Point of Interests (POI) data, and thanks to the Department for Transport for the JTS (Journey Time Statistics) dataset. The Median House Price Dataset from ONS has also been used in this work. Thanks to the Department for Communities and Local Government for providing the English Index of Multiple Deprivation (IMD) 2019 dataset, where several variables in the IMD are included in this work for classification. The English Prescribing Data (EPD) and the associated Registered Patients Data are openly available from NHS Business Services Authority and NHS Digital. Thanks also go to Department for Business, Energy and Industrial Strategy for providing the fuel poverty data, which is the Low Income Low Energy Efficiency indicator.