Today marks the launch, by DCLG, of a new mapping tool and open dataset for you to explore and understand the nation’s wellbeing at the neighbourhood-level. We’ve also incorporated wellbeing data in our Local Authority Dashboard, for you to view alongside a selection of other DCLG statistics on housing, deprivation, and local government finance.
This builds on the first set of new, experimental wellbeing statistics released in July by the Office for National Statistics
As the ONS describes in its statistical release, the data provide new insight in to “how people think and feel about their own lives”. When used alongside the more traditional economic measures, this data can help us to better understand the nation’s problems, issues and progress with resolving them.
Since July, my analyst colleagues at DCLG have been busy modelling ONS’s data at neighbourhood-level. I’ve provided further background below on how they did this. We think the local modelled estimates are a useful resource for local authorities and councillors, and their local communities and residents.
We think of the data as a sort of lens, through which anyone can view wellbeing in their local area, and compare/contrast our estimates against their own local data and knowledge. We’d love to hear your views on if/how/where the data is useful to you, in your local area: feel free to e-mail wellbeing@communities.gsi.gov.uk or leave a comment on my blog.
We’re also very interested in any software tools that use our data alongside other sources, in new and innovative ways. As an open data geek, I think the most exciting aspect is that the underlying estimates are also available as full, 5-star LinkedData. You’ll find the datasets on our opendatacommunities site
If you’re a software developer or innovator, you can develop queries to retrieve the data via our API, and display it in your own application. This extends to any dataset held in opendatacommunities. That means you could write queries which blend and combine wellbeing with other data: for instance, to retrieve wellbeing and deprivation data for a particular postcode, or local authority.
To get started, please take a look at our developer documentation. We’ve also provided some example queries, such as this one (http://opendatacommunities.org/datasets/wellbeing-lsoa-life-satisfaction-mean ), in the “API” tab on the dataset about ‘Life Satisfaction’.
Modelling the results – technical background
The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation in wellbeing between neighbourhoods.
These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. DCLG analysts used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood.
These estimates are not the actual survey responses of people living in those areas. This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results. As such, DCLG encourage local areas to use these estimates alongside their own local knowledge and data.
The maps reflect the differences in wellbeing observed among the 56 ACORN Types and the unique ACORN profile of each neighbourhood. The method assumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, we made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety.
Requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.
Open Data Blog 
Interesting, would be cool to get some correlations here as well. Such as wellbeing and crime and wellbeing and house prices, I have hunch on what to expect would be interested in actually results.