Understanding the careers cold spots – and a note on maps

Before the Christmas break we (at the Careers & Enterprise Company) published the cold_spots_report_2016. This analysis allows The Careers & Enterprise Company to understand where more career support is needed and to direct our resources towards these areas.

Much of the hard work was done before I arrived in the initial development of the model in 2015 and this year the Institute for Employment Studies ran the updated analysis. I project managed and wrote some narrative around each of the data sources. It’s interesting, and I urge you to look at the exec summary.

In this blog I’m writing a note on the map, and how we use data to define abstract concepts.

Gif – cold spots (areas with the least cold spots removed first)


The gif shows the cold spots at Local Enterprise Partnership level and removes the warmest areas first in layers before building the map back up. In the report we argue that the map ‘presents a picture of the country that will be familiar to many policy makers and practitioners. The coldest areas are found in rural parts of the country, particularly in the coastal regions and post-industrial areas, while the warmest areas are in London and the home counties’. To show this I’ve included below three more maps that tell a similar story.



Sorry but all the maps are in different colours and at different levels of geography (Local Enterprise Partnerships, local authority districts, and parliamentary constituencies – If you ask me where I’m from I’ll say Somerset, a ceremonial county – geography is tricky).

The first three maps have all been used The Careers & Enterprise Company to allocate resource to boost careers and enterprise provision. The fourth map is that old chestnut the Brexit map. Excepting map 4, these maps are used to define abstract concepts including:

  1. areas in need of career support
  2. disengagement from education
  3. a lack of social mobility

In this context, map number four Brexit, is not an abstract concept (Brexit means Brexit!) however, in the aftermath of the Brexit vote a range of commentators scrambled to find the data that would best explain an area’s voting preferences, that is to say, to define what being brexity is all about. Another example of the scramble to use data to define an abstract concept is the current emphasis on JAMs (just about managing). Theresa May recently expressed her displeasure at Civil Servant’s attempt to operationalise a definition saying ‘I’m talking about ordinary working people, for whom life is a bit of a struggle. They may be holding down two or three jobs in order to make ends meet. In a job, but worried about job security. Owning a home, but worried about paying the mortgage… you can’t just box them into a simple descriptor category. Which is why I get frustrated when Whitehall tries to do that.’ I can see both sides. Concepts like social mobility and JAMs make intuitive sense to us but if you are to target spending eventually you need to find a way to approach the problem using some science.

Our approach has been to first define the concept. What is the problem you are trying to address? The process then involves building a basket of indicators that together explain the phenomenon and then assigning a weight to each indicator. I think it works really well and I’m interested to hear what you think.

Bonus map number 5

I recently made a visualisation of earnings by local authority using (NOT OFFICIAL) statistics from the ONS (they insist we emphasise that they are not official). The map (an interactive visualisation so actually 10 maps in one) tells a similar story to those above. It’s very stark to see how the proportion of high earners is highest in London and dissipates towards the coast.



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