Case study: Visualising the Output Area Classification
Visualising the Output Area Classification
Geodemographics have long been used by major retailers (such as Boots, and M&S) to understand their customers, and some councils are already using commercial neighbourhood classifications, such as Mosaic and ACORN, to better understand their areas and citizens. There is also a freely available open-source alternative created by the Office for National Statistics and Leeds University - the Output Area Classification (OAC). OAC has been incorporated by Local Futures into its Local Knowledge system, providing new ways to visualise OAC and so analyse the make-up of communities within a particular area.
Why neighbourhood classifications are important
With the new Comprehensive Area Assessment coming into effect, councils and their service partners need to show a collective understanding of their communities. Although the detail of CAA is still evolving, the new guidance lays out the expectation that local partners must show they understand their community - in particular, the needs and aspirations of vulnerable groups - and create local priorities that reflect those groups' needs and aspirations, seek to improve their customers' experience of services, and tailor services to local needs.
The Improvement and Development Agency and the Local Government Association have published guidance documents on Customer Insight, encouraging councils and their partners to develop better ways of understanding citizens, customers and communities.
" 'Customer insight' is nothing new. Councils have always gathered a lot of information about their communities... But often that information remains untapped or fragmented across different parts of our councils. Meanwhile, new tools, techniques and sources of insight have become available. Although many of these innovations have been pioneered in the private sector, they are being rapidly adopted by leading councils for public ends"
OAC's uses & value
Geodemographics classifications such as OAC can help local councils and partnerships to answer such questions as
- How can I easily summarise the characteristics of our differing local neighbourhoods?
- What are the profiles of the users of our various services?
- Are there big variations in take-up of services according to type of neighbourhood?
- How can we target our resources where they are most needed - for example, healthy eating initiatives, policing, and recycling?
- Do we need to relocate some of our council facilities to areas in most need?
- Which other partnerships across the country have a similar mix of population?
The examples below show some of the ways in which OAC can be displayed and analysed using Local Knowledge. In combination they provide a powerful and graphical means of bringing the typologies to life, for a range of different partner uses:
Maps: Colours have been allocated to the different neighbourhood typologies and displayed at Census Output Area (OA) level. This allows the diverse character of communities to be displayed. The supporting key contains hypertext links to technical background papers that provide a full explanation/description of the different typologies. Other geographical layers can be added, allowing individual wards to be profiled.
Bar charts: Two different types of bar chart are illustrated. The first shows the proportion of population within the different OAC groups. These are colour coded, for ease of use, to match the map. They show the dominance of particular groups and absence of others. The charts can be created for regions, sub-regions/Counties, districts, wards and lower layer super output areas across the country, highlighting the very different make-up of local communities. Bar charts can also be created which show the proportions in comparison to the national average.
The second 'stacked bar' shows the proportion of the local population within the different OAC groups. This particular example shows the make-up of communities at a ward level within North Tyneside. Similar charts can be created for regions and sub-regions, providing a DNA profile of local communities.
The radar charts can also display the nearest statistical neighbours for any combination of indicators. The second chart shows North Tyneside's statistical nearest neighbour on the proportion of the population in each OAC Supergroup. Areas such as Salford, Thurrock and Stoke-on-Trent have a similar profile, in terms of the make-up of local communities. This sort of analysis can be used to develop communities of interest or form benchmarking clubs.
User case studies
Since these tools have been added to Local Knowledge, researchers have been applying visualisations in a variety of settings. In one example, from a London Borough, policy users have been conducting research into place shaping and using the outputs from Local Knowledge to improve their understanding of local communities. They have also used the outputs as a starting point for further work, to understand the impact on groups at risk of poverty and deprivation and for informing customer insight. Another example, from a West Midlands council, has shown the benefits of OAC in comparing the profiles of different wards, to develop new community areas or localities. Alongside other analysis, the OAC bar charts have revealed which areas are most similar to one another and would be likely to fit together as cohesive localities.
This is the first time OAC has been made available in this way, allowing analysis of regions and localities across Britain. As such it allows OAC to be used not only for customer insight purposes, but as a tool for analysing the diverse make-up of citizens and communities. This has allowed OAC to be shared with a much wider range of stakeholders, with the different combinations of outputs helping to inform a strategic conversation about the nature of local communities.