Misleading Data Visualisation: Defra’s TB Maps

UPDATE: Since I wrote this, Defra have been in touch to say that they have removed the maps from their website and are changing the data included on them, which is good. The challenge of how to present these data remains though.

Maps are powerful tools. They can be a great way of presenting (or visualising as people like to call it these days) data and statistics.  But as geographers know, maps are not always what they seem – and these maps (link no longer working) from Defra are a surprisingly good example of that.

It all looks nice and shows the advance of TB across the country – that part may be true. But actually, the maps are misleading. Here’s why:

The maps are supposed to represent the number of cases of TB. Only, the asterix tells us that it’s actually only about confirmed cases of TB – that’s cases where visible lesions have been found. In the new even more confusing jargon, these are “OTF withdrawn” cases. Or is it?

Below the picture you can see the number of reactors. Only, this is the total number of reactors – not the number of confirmed reactors (OTFW). Its a bit of a muddle – what is the map actually showing? If it’s about confirmed cases then why not give the actual number of confirmed TB reactors? That number would actually be a lot lower than the figures presented. I don’t have the figures of confirmed reactors (i.e. reactors with visible lesions) to hand, but for herd breakdowns figures show that around half are OTFW – although this varies by region: in Dyfed OTFW breakdowns account for 36% of all breakdowns, whilst in Gloucestershire its around 60% - quite an impressive difference considering both are supposed to be high incidence areas.

Personally, I think this whole debate about confirmed/unconfirmed reactors is not handled very well. Sometimes Government likes to treat unconfirmed reactors in the same way as confirmed cases (as is the case here), all go under TB restriction, and even when no visible lesions are found, Government likes to tell farmers that doesn’t mean they don’t have TB. At other times, they are treated separately – for example, the work on badger culling ignores unconfirmed cases, AHVLA only visit farms to conduct epidemiological investigation on confirmed cases, and the rules for going clear are different. If one of the purposes of the change to the OTF jargon was to help farmers understand the difference between confirmed and unconfirmed cases, then I think that misses the point about the reasons why farmers distrust science and the government.

Whatever the figures are, the shading on the maps is also confusing. I’m bound to point this out as a geographer –the maps are committing a gross ecological fallacy. Some of the later maps (2006, 2010) are essentially suggesting that every farm in the south west is under TB restrictions. We know this is not the case: even in the high incidence areas the number of breakdown farms is relatively low: Defra’s own stats show that for last year the % of herds with a confirmed (OTFW) breakdown is around 9% in Devon, 12% in Gloucestershire, and 4% in Dyfed. When you look at the numbers of farms under TB restrictions it’s higher, but not by too much: around 19% in Devon, 28% in Gloucestershire and 14% in Dyfed. So to suggest that all farms in these areas have TB as these maps do is misleading. But the maps also fail to distinguish between high/low incidence areas of TB. For example, looking closely you might think that an area like South Wales (low incidence) has the same kind of TB problem as West Wales, where the problem is much greater.

None of this is to say of course that TB in these areas isn’t a problem or needs to be reduced. It may also seem to be a bit of pedantry – those “in the know” understand the limitations of presenting the data in these ways. But this is the point: these maps aren’t meant for people in the know: they are an exercise in communicating science to people “not in the know”. And the main reason why you’d want to do that is to justify your actions to those people – to convince them that what you are doing is right.  So they are not simply objective representations: they have a purpose. Normally, in cases like these, I’d say something like this is a good example of why people end up not trusting the Government. However, there are similarities here with the use of statistics by other organisations involved in TB: the NFUs public attitude survey and the Badger Trust’s use of TB statistics being other examples. And when you take all three cases together you can see there’s a wider purpose to the use of TB statistics, found also in many other studies of policy making. Simply, it’s to repeat a well known fact in policy studies: statistics help to objectify claims, frame debate and advance the interests of different organisations.