Why 'data mince'? Lucy explains:
"A long time ago, my brother went off to university and started learning to shop,
cook and stay alive on a student's budget. He found out that pretty quickly
he fell into a pattern; after a long day of lectures and coursework and
assignments he would realise it was time to cook dinner. He'd chop an onion
and start frying it, add some cheap beef mince to the pan ... and then realise
he hadn't decided what he was actually going to cook. Chilli? Spag bol? Maybe go all
out and make cottage pie? His basic fried-mince-and-onion was just the
starting point.
What we found when we started working on projects for different clients, no
matter how sophisticated the toolset or how complex the problem, was that we
almost always needed to start with the same initial collection of demographic
data; population, income, deprivation, working patterns and so on. This is
what we call our 'data mince'."
Working in the data science field in the middle of a global pandemic, there
are many opportunties to start building tracking dashboards and viral
infection models. But there are enough of those already, so we're focusing on
how we can provide something useful to anyone trying to manage service demand or
community responses to the current crisis.
We'll update this periodically as we find new and useful things to add to it,
but for now here are the basics:
- population,
- deprivation
- economic activity.
All of the data we use here is published under the Open Government Licence as open data.
We always find it useful to look at the distribution of ages
rather than the absolute values; the shape of a place's age
profile tells you a lot when you compare it to the wider
area. For example, Exeter and Plymouth stand out in this chart
because of the much higher numbers of resident young people
due to the universities. Torbay on the other hand has a profile skewed
towards the older age ranges, as many people retire to the area.
Use the dropdown to select different Devon districts.
Source: ONS population estimates.
If we were looking at something like vulnerability profiles, then both groups would be of interest. Outbreaks like the coronavirus are particularly concerning for the elderly and anyone with an underlying health condition, but young people are by no means immune. In fact a university environment can carry extra risks, e.g. dorms, house shares, group tutorials and lectures in spaces that see a lot of use.
Health isn't the only vulnerability factor; for many people self-employment,
freelance and contracting work and zero-hours contracts have become a standard
part of their income generation. To take a look at the potential numbers of
people affected we can use the ONS Annual Labour Survey data - latest is for
the year from October 2018 to September 2019.
Source: ONS UK Business Counts
These charts show two different attributes of the UK Business Counts data; the breakdown by legal status, which gives us the proportion of sole traders in a district, and the size band by number of employees so we can see where there's a high proprotion of businesses with, for example, fewer than 5 people working there.
Income and employment are far from the only measure of wellbeing - although they certainly make a
difference. The Indices of Multiple Deprivation (IMD) are updated every few
years and cover a wider range of measures including environment, barriers to
opportunity, health and crime.
The top-level index of deprivation is calculated from a combination of the
contributing indices, and assigns every small area (LSOA or Lower-level
Super Output Area) in England a score based on how deprived the model says
the area is in comparison to all the others. With all the LSOAs ranked in order,
each one is then assigned to a decile with a numeric score from 1 to 10.
Any LSOA with a decile score of 1 is therefore in the top 10% most deprived
nationally; and the higher the decile score, the less deprived.
This chart looks at how many LSOAs per district fall into which deciles, so we can see
that the more urban areas like Torbay and Plymouth have a far greater
proportion of LSOAs with lower decile scores.
Source: UK Government - MHCLG
Further reading