Project description

One of the best things about working as a Data Journalist in the BBC’s Data and Visual Journalism Unit is being able to work on projects about such a wide range of topics, and alongside different brilliant teams from across the organisation.

This year I’ve worked on a project with the business team which explained the British housing crisis in more detail than ever before, and I’ve explored earnings data for different jobs to see which industries are becoming more or less rewarding.

With the science and environment section I produced a story about pollution that provided more insight into localised data had previously been published in the UK.

I tracked and researched party leaders’ campaign visits during the 2017 General Election campaign, to try and gleam some insight into each party’s strategy and expectations for the politics team.

I worked on the Price of Football project with sport, now in its seventh year, giving football fans more detail on how costly it has become to follow their teams, big or small.

And with the world desk we looked at what was left of Mosul following the devastating two year IS occupation of the city, and forward to what challenges returning residents could expect to find as they sought to rebuild.

What ties these projects together is a strong public interest theme. Whether that’s allowing people to find out more about the world around them or telling people about worlds they might not see so much of.

It’s always about making the stories as relevant as possible to as wide as possible an audience. That’s why we try to integrate lookups into so many of our projects – it turns a national story into hundreds or even thousands of stories specific to individual readers.

What makes this project innovative?

In production terms the innovative parts of each project are generally the lookup modules or interactive maps. They enable us to tell all the interesting stories we want to tell, but to filter them only to the audiences that will be most interested in them.


In journalistic terms, one of the most important but simple innovations we took was taking inflation into account when analysing house prices. This isn’t typically done by other analyses of house price changes over time, which breeds into a misreading of the problems in the British housing market.


While it may be true that house prices in London are rising far faster than wages, making them unaffordable for first-time buyers, across the rest of the UK – particularly in the North East – thousands of peoples’ homes are worth less than they were when they bought them, meaning they’re trapped in negative equity. This doesn’t come out unless you adjust figures for inflation.


We made the same adjustment with the salary calculator, to tell people in real terms – the only terms that matter in real life – whether their specific job paid more or less than it did five years ago. It’s a little extra work that makes the story not only more interesting but also more useful for the audience.


We also try to make our work different from others by going to an extra level of detail. Things like house prices and pollution have all been explored before, but never before in as much detail as we went to.


I’m also proud of the interesting way in which we try to contextualise our stories to make the figures understandable. In the Price of Football calculator we spoke about how many fans spending the same amount of money as you would be needed to afford a top player’s transfer fee or weekly wage.


And for the house prices project we told people how much of a house they could afford to buy in the most expensive part of the country, and how many houses in the cheapest part of the country, in exchange for one average house where they are.

What was the impact of your project? How did you measure it?

Throughout the year most of these stories have received front-page billing on the BBC website and attracted many millions of page views. We measure this using Adobe comScore and two tools internal to the BBC, called Chartbeat and Telescope.


We can also tell how many people interact with the page and what particular bits they interact with most. By learning about this behaviour we have adjusted the way our projects are structured over the last year and are seeing an improvement in getting people to see the bits we most want them to see.


The time spent on the page was usually more than double what we expect from more traditional news stories on the site.


In terms of impact it’s always good when we can work with other BBC outlets on TV or radio to ensure we get as much coverage as possible for our news lines, and as many people as possible use the personalised content. This usually involves repackaging our content in different ways, but it’s worth doing this for the extra impact.


It also means more real people can be involved in our stories than we could possibly speak to ourselves.


We also try to create bespoke social content for most of our stories. The audience activity we’ve most noticed this year is people screenshotting particular parts of a story to share it themselves in higher volumes than using the share functionality on a page. This really helps to let us know that people are interested in what we’re doing and finding it useful.

Source and methodology

Most of the data for these projects is publicly available, from sites like the Office for National Statistics, the Land Registry or other national or international government websites.


Some projects, for example the pollution story, involved working with data that was made exclusively available for us by peer-reviewed academics.


And the data for the Price of Football project came from a BBC survey of over 100 football clubs.


In terms of methodology, all the projects are very much a team effort between journalists, data scientists, designers and developers, with lots of testing from all parties at the end.


We try and do as much of the original data exploration and analysis using R scripts so it can be easily checked by colleagues, and repeated if we want to examine different stories in a similar way, or the same story at a later date. When testing we often do the same calculations in Excel to see if we get the same results.


With inflation calculations over time, it’s important to consider how much inflation fluctuates even over the course of a single year. When we looked at house prices, we took the Consumer Price Index figure from the month the house was sold, to get a more accurately translated figure to just the average for the year.

Technologies Used

R and Google Sheets for data exploration and analysis. Carto and QGIS for mapping.

Project members

Nassos Stylianou, Ransome Mpini, John Walton. Joe Reed, Rosie Gollancz, Sumi Senthinathan, Luke Keast

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