Data: in the eye of the beholder

A few weeks ago I was having breakfast at Primrose Cafe in Clifton. The sun was shining, the radio was on too loud, the place was crowded as usual and the conversation was almost flowing. In the midst of all this my companion made the point that there seemed to be more beautiful people in Clifton than there were, say, in Bedminster, and didn’t I think so? I looked around, and as the source of the comment was a single man, I tried to spot and remember how many young, slender, brunettes we had passed on our way.

He insisted that it wasn’t just about young women so I asked if it was related to age, are there more young people in Clifton? is it the clothes, the brushed hair, the jewelry, the make up, the colour of the skin, were there more white people? At this point he started to get a tad defensive at the suggestion that I might be calling him either shallow or racist or both. We didn’t get very far as he insisted he knew what beautiful meant and he didn’t have to explain it while I persisted with the thought that he should learn to quantify these abstract notions.

There’s always a chance that we were both somewhat wrong and right at the same time but I’ll stick to arguments that favour my own particular biases as this will be quicker.

“Nothing is considered to be beautiful by all peoples everywhere” says Desmond Morris. “Every revered object of beauty is considered ugly by someone, somewhere … There is so often the feeling that this, or that, particular form of beauty really does have some intrinsic value, some universal validity that simply must be appreciated by everyone. But the hard truth is that beauty is in the brain of the beholder and nowhere else” (pp 421-2).

Morris goes on to write of how humans are master-classifiers of information. When it comes to identifying beautiful and ugliness then he suggests that we have an internal classification and according to the properties we assign to this category we call something beautiful when it excels in those particular qualities and ugly where it doesn’t (p423).

This is where data comes into it because if we can identify characteristics it means that we can measure them and compare Bedminster and Clifton. I didn’t go ahead and measure them but I do know that when I think of people or places as beautiful or scummy or amazing or poor etc that there are plenty of biases that underline the concepts.

There are also plenty of sites which make data available on locations and which already provide categories. is a website that uses demographic information to provide snapshots of areas. 1.4 miles separate the Royal York Crescent in Clifton from West St in Bedminster but in terms of household income, interest in current affairs and education there are vast worlds of difference.

Bedminster, West St

Family income, educated to degree level and interest in current affairs are all high in Clifton whereas in Bedminster family income and educated to degree level are medium and interest in current affairs is below medium.

I’m using demographics and as examples of what data can add to meaning. There is a lot of information about data journalism at the moment and how it’s the new big thing and that can’t be a bad thing since apparently, “a lot of journalists are innumerate and a lot don’t know much about history” (CJR). What I think it comes down to is adding a meaning where facts just aren’t enough and by the way, without context, facts may be sacred by they are rarely enough.

When the Guardian advertises its credentials in promoting the West Country and suggests that Bristol featured in their [readers’] top ten UK cities in the 2009 Guardian and Observer reader Travel Awards you would probably not need help to figure out that Clifton features more than Bedminster. If you weren’t from the South West or Bristol, however, there is a fair amount of data out there that would help you figure it out and that’s the beauty of it.

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