Mapping deprivation in Brighton (a first, faltering attempt)

For a few reasons I decided to keep out of the way at CityCamp Brighton. But I was still keen to try to do something with a bit of my time.

Paul Colbran and the folk at Brighton and Hove released some data around postcodes and deprivation at the beginning of the conference, so I thought I’d have a look at it and see if I could get anything useful from it. The data is presented on this page, here. It’s a biggish spreadsheet, with a lot of fields that take a bit of getting used to!

I’m no expert with data, but I imported it into Google Refine – which allows you to call APIs and augment the fields with other information. I added some Lat Long coordinates – so that I could have a look at mapping the most deprived areas.

What this revealed was that there are quite a few ‘dead’ postcodes – those being, essentially, dead locations that the API can’t help you to locate. While there are Eastings and Northings, for a novice these are harder to work with – and because they are not a universally recognised system (albeit very accurate) they are not as easy to automatically map. On Saturday night I manually inputed data for the first set, but I wimped out on the Sunday and simply left the locations out.

As it happens the vast majority, I think, are located close to the areas that are revealed in the map, below. I chose to work with the 10 per cent most deprived wards, based on the assessment of deprivation made in 2007 – the ‘index of multiple deprivation 2007 overall LSOA score’. There’s an explanation of what this is here, but essentially it’s a combination of seven different aspects of deprivation – including (according to Wikipedia) ‘deprivation, employment deprivation, health deprivation and disability, education skills and training deprivation’.

Because there is some data missing – and because of the hit-and-miss nature of this kind of first stab at using the data – my map SHOULD be taken with a pinch of salt. There are, for example, a couple of fishy-looking results (deprivation on a golf course?). SO THIS IS NOT SCIENCE!!. Nonetheless, as an exercise it has been useful in proving that, with relatively little preparation, it’s possible to begin to interrogate the data and understand more about the city – and its needs.

A map presented by Anthony Zacharzewski in his introduction to CityCamp Brighton suggested that there is much deprivation intermingling with more affluent areas – I think these are called ‘pockets’ in the trade. These don’t really show up in the data that I’ve used. This might be because deprivation can be measured in a variety of ways, but it may also be because there are different degrees of deprivation. The postcode data that I looked at distinguishes, by way of illustration, between ‘the 10 per cent most deprived’ and the 20 and 30 per cent most deprived. Since I went for the most narrow definition, it is almost certainly the case that a broader range would elicit a more complex picture of where deprivation in the city is located.

There are a few things that I think come out of this:-

1). There’s a need to revise and work on cleaning up the data – particularly the postcodes – which would certainly help the council.

2). There’s an opportunity for the city itself (i.e. not just the council) to work together to explore what deprivation means, where it is and how it can be tackled that good (not the use of a very lazy positive adjective) data can help to provide.

3). There are some important questions that need to be asked about need – particularly in the location of resources and services – that mapping of deprivation is particularly useful at helping to reveal. While the council may have been considered, traditionally, to be best-placed to do this, I think it makes sense that if we start to broaden who is able to explore and consider this kind of information, we will be more likely to come up with better ideas on how to go about dealing with these problems.

4). I feel there’s a responsibility on those who push for open data to start using it as soon as it appears – even if it is only to decide that it can’t be used and to feed that information back to government. It’s only by working on the data – demonstrating that it’s useful or that it’s not – that we can help those who want to help us in winning the argument that this stuff really matters.

Better get back to Google Refine!

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