Lincoln Journalism Research Day - Data Journalism
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Lincoln Journalism Research Day - Data Journalism

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  • Taking this opportunity to explore some of the issues associated with whatever this thing called “data journalism” is…
  • I’m not a journalist, and don’t have any form of journalism training. But I do have an interest in ICT, and from that have an interest in “communication”. <br /> <br /> Let’s start with an easy(?!) question - what is journalism? <br /> <br /> One way of answering that question is to list some of the functions, or attributed, associated with it – informing, educating, holding to account, watchdog function, campaigning, contextualising for a particular audience.
  • Sensemaking seems to me to be an important part of it… In part contextualisation, in part identifying the bits that make the difference, the bits that make it important, the bits that make it news that people need to know… <br /> <br /> …and often with a particular audience in mind.
  • Critical judgement.
  • Second question: what is data? National statistics, sports results, polls, financial figures, health data, school league tables, etc etc. <br /> <br /> Is a book data? Or a speech? What if I split a speech up into separate words, count the occurrence of each unique word and then display the result as a “tag cloud”, or word frequency diagram.
  • One way of thinking about data is that it is a particular sort of source, or a source that can respond to a particular style of questioning in a particular way. <br /> <br /> Another take on this is that many “data sources” are experts on a particular topic, experts that know a lot of a very particular class of facts.
  • One way of thinking about data is that it is a particular sort of source, or a source that can respond to a particular style of questioning in a particular way. <br /> <br /> Another take on this is that many “data sources” are experts on a particular topic, experts that know a lot of a very particular class of facts.
  • So what is data journalism? <br /> <br /> If I was to ask you, the members of a school of journalism, “is this or that news article ‘journalism’” I imagine one response might, “well…. It’s the output of a journalistic process.” <br /> <br /> But if I point at a map with some markers on it and ask: “is this map “data journalism”, you might answer: yes. Or at least, that’s what many of the early job ads for data journalists implied.
  • Sports journalism has sport as the topical contextual frame for some journalistic activity, <br /> <br /> Political journalism has politics as the topical contextual frame for some journalistic activity, <br /> <br /> Investigative journalism has a particular process as the contextual frame for some journalistic activity, a process that may be applied to particular topic areas. <br /> <br /> So for data journalism does “data” relate to the topic or the process? <br /> <br /> Where we focus on data outputs, then the implication is that the “topic” of data is the focus of the framing. But I think we need to reframe to consider the procedural role. <br />
  • So as a starting point, let’s frame the idea that data journalism is a process related epithet that implies one of the key sources in a journalistic activity is “data”.
  • By focusing on this notion of data journalism as relating to process, we can then start to explore with a little bit more criticality what the practice of data journalism might involve that identifies it as such. <br /> <br /> That is, how is practice influenced by the fact that it must engage with “data as a source”?
  • The inverted pyramid gives us one way of considering the data journalistic process, or at least identifying some of the steps involved in a data investigation. <br /> <br /> But there are many other ways of conceptualising the process – for example, finding stories and telling stories…
  • When it comes to finding stories, do we: <br /> <br /> want to find stories in a dataset we are provided with, or <br /> use data to help draw out a story lead we have already been tipped off to?
  • Anscombe’s Quartet is a toy dataset that first appeared in a 1973 paper by statistician Francis Anscombe. <br /> <br /> His paper – Graphs in Statistical Analysis – was based around the claim that “graphs are essential to good statistical analysis”.
  • But this is where we start to hit some stumbling blocks.
  • And a big stumbling block is one that is often denied in higher education, which is the provision of skills, as compared to “higher level conceptual or academic understanding”. <br /> <br /> There is an old saw that we become better writers through reading more. But how much time do you invest in reading charts? <br /> <br /> Really reading them? <br /> <br /> I came across this beautifully titled book a few weeks ago - “Making Sense of Squiggly Lines”. <br /> <br /> The blurb on the back summarises the situation well: “Data points are just words, but when connected with a squiggly line they tell a story”.
  • In an ideal world, the process would be simple: have data, get story.
  • But it’s not that simple. <br /> <br /> It’s more likely that we need to engage with the dataset to try to tease the stories out of it, or facts and relationships from it that we can used to support the claims we make in a narration of some sort of story that is at least supported by the data, or contextualises it in a narrative way that is hopefully “truthy”.
  • One of the ways I like to work with data is to have a conversation with it – asking questions of it and then further questions based on the responses I get.
  • Sometimes it looks at first as if we have data in a form where we might be able to do something with it – then we realise it needs cleaning and reshaping. <br /> <br /> For example, in this case we have percentage signs contaminating numbers, data organised in separate sections – but how do we get a “well behaved” view over data from all the wards – and different sorts of data: votes polled per candidate versus the size of the electorate in a particular ward for example. <br /> <br /> Walkthrough: http://blog.ouseful.info/2013/05/03/a-wrangling-example-with-openrefine-making-ready-data/
  • But this is where we start to hit some stumbling blocks.
  • And a big stumbling block is one that is often denied in higher education, which is the provision of skills, as compared to “higher level conceptual or academic understanding”.
  • Tidying data – or cleaning data – or more colloquially, “wrangling data” – refers to the process we need to engage in to turn a dataset we have found into one that is useable. <br /> <br /> Many published datasets are horrible. <br /> <br /> Really horrible. <br /> <br /> They don’t work as we might want or expect them to in the applications we tend to have to hand.
  • Take producing data visualisations, for example: have data, produce visualisation. <br /> <br /> No. <br /> <br /> That’s like saying: have two hours of rambling conversation with source, have 200 word story with strong quotes. <br /> <br /> No. Just: no. <br /> <br /> It doesn’t work like that. <br /> <br /> Yes, there are powerful charting tools available BUT they require the data to be clean and tidy and to be in the right shape for the tool. But it typically isn’t.
  • We have to wrangle it. <br /> <br /> Now wrangling is a technical job, and arguably a job for technicians – higher apprentices of the journalistic world – not graduate journalists. <br /> <br /> But I think out journalists are going to have to learn the equivalent of some machining in the mechanical world.
  • Just by the by, I didn’t draw those block diagrams, I wrote them.
  • I “wrote” these charts – you can see how at the top. That code – applied to a suitably shaped version of a dataset known as Anscombe’s Quartet. <br /> <br /> The data has been reshaped to 3 column format: a column for the x values, that are plotted on the horizontal x-axes; a column for the y values, that form the vertical y-axes; and a column for the groups, which specify which panel, or facet, each point should be plotted in. <br /> <br /> The code defines the construction of those charts. Exactly. There is no magic. At least, no other magic.
  • One of the first datasets I played with was MPs’ expenses data. Here are a couple of ways I started to chat with it – imagine talking to someone whop knows about *all* the expenses claims put in by every MP over a parliamentary session… (The charts were created using an online interactive tool developed by IBM called Many Eyes.) <br /> <br /> The bar chart Is ordered, for a particular expenses area, by total amount for each individual MP. <br /> <br /> The block histogram shows how many MPs made a total claim in particular expenses area of a particular binned value. (A ‘bin’ is a range.)
  • Critical judgement – it applies to data too...
  • One of the things to mention about mapping data mapping and visualisation techniques is that they often tells us things we already (think we) know; in that sense, they are not news. But they may also tell us things we know in new, visually appealing ways. And by making use of such ‘confirmatory’ visualisations and displays we can build confidence within an audience that they know how to interpret these sorts of representation.
  • As the audience becomes comfortable reading the charts and making sense of data, when there is something new or surprising in the data, the surprise manifests itself in the reading of the data or chart. <br /> <br /> For journalists working with data, developing a sense of familiarity with how to interpret and read data when it is just confirming what you already know helps to refine your senses for spotting things that are odd, noteworthy, or newsworthy. <br /> <br /> Taking a little bit of time each day to: <br /> <br /> read charts as if they were stories; <br /> look behind the data to find original sources, such as polls or data containing news releases, and then compare the original release with the way it is reported, paying particular attention to the points that are highlighted, and how the data is contextualised; <br /> <br /> will help you develop some of the skills you will need if you want to be able to identify, develop and treat some of the stories that your specialist source that is data can provide you with, of only you ask…
  • A scatterplot is another very powerful sort of chart – we can plot two sorts of value against each other to see if there are any groups, or trends. <br /> <br /> Some scatterplot tools allow you to size or colour nodes according to further dimensions. Colouring nodes by group (if sensible groups exist) can also help you see whether particular groups are clustered or group together in particular areas of the chart.
  • Maps can be used to pull out different sorts of relationships – for example, plotting markers in the centre of each MP’s ward coloured by the total value of travel expenses claim in a particular area, we can easily see whether or not an MP is claiming an amount significantly different to MPs in neighbouring wards. In this case – travel expenses – we might expect (at first glance at least) a homophilitic effect – folk a similar distance away from Westminster should presumably make similar sorts of travel claim? At second glance, we might then start to refine our questioning – does ward size (in terms of geographical area) or rurality have an effect? Does an MP travel to and from home more than neighbours (or perhaps claim more in terms of accommodation in London?)
  • Sometimes we need to provide quite a lot of explanation when it comes to making sense of even a simple data visualisation – “what am I supposed to be looking at?”
  • The other way of using data is to tell stories. But what does that even mean…?
  • The other way of using data is to tell stories. But what does that even mean…?
  • In passing, it’s worth mentioning that one thing statistics does is help provide context. <br /> <br /> Is this number a big number in the greater scheme of things? Is this thing likely to happen by chance or is there a meaningful causal relationship between this thing and another thing? <br /> <br /> The chart in the corner is a reminder about how surprising probabilities can be. The chart shows the probability (y-axis) that two people share a birthday (the number of people is given on the x-axis). The chart shows that if there are 23 or more people in a room, there is more than a 50/50 chance that two of them will share a birthday (that is, share the same birth day and month, though not necessarily same birth year). <br /> <br /> How many people are in the room? If it’s more than 23 – I bet that at least two people share a birthday (at least in terms of day and month).
  • One of the first datasets I played with was MPs’ expenses data. Here are a couple of ways I started to chat with it – imagine talking to someone whop knows about *all* the expenses claims put in by every MP over a parliamentary session… (The charts were created using an online interactive tool developed by IBM called Many Eyes.) <br /> <br /> The bar chart Is ordered, for a particular expenses area, by total amount for each individual MP. <br /> <br /> The block histogram shows how many MPs made a total claim in particular expenses area of a particular binned value. (A ‘bin’ is a range.)
  • The other way of using data is to tell stories. But what does that even mean…?

Lincoln Journalism Research Day - Data Journalism Lincoln Journalism Research Day - Data Journalism Presentation Transcript

  • Data Journalism Having Conversations with Data Tony Hirst Computing and Communications The Open University
  • What is journalism?
  • [sensemaking] View slide
  • InvertedPyramid View slide
  • What is data?
  • [a particular type of source]
  • Data is just another type of source…
  • What is data journalism?
  • Data journalism is investigative
  • Journalism that uses data as one of the sources
  • (We don’t have to produce a map or a chart in the final piece)
  • The practice of data journalism
  • find stories tell stories
  • find stories
  • Anscombe’sQuartet
  • THE BLOCKERS
  • Reading Charts Blocker
  • “Data points are just words, but when connected with a squiggly line they tell a story” Christopher Brown, “Making Sense of Squiggly Lines”, 2011, ISBN 978-0-9832593-1-2
  • “Conversations with data”
  • ouseful.info - A Wrangling Example With OpenRefine: Making “Oven Ready Data”
  • THE BLOCKERS
  • Skills Blocker
  • Tidying data
  • KnowThyTools…
  • Data Distributions Outliers
  • InvertedPyramid
  • Data can confirm what we think we know
  • Data can surprise us and force us to rethink what we think we know
  • Trends and (anti)correlations...
  • Data makes most sense when contextualised
  • THE BLOCKERS
  • Skills Blocker
  • [statistics] (the art of looking at one number in the context of other numbers)
  • Asking Questions of Data
  • Data Distributions Outliers
  • We need to teach people how to ask questions
  • We need to teach people how to read charts
  • SQL was developed as a navigational aid
  • intitle:yarl filetype:pdf inurl:http://www.justice.gov.uk/do wnloads/publications/inspectorate -reports/hmipris/immigration- removal-centre-inspections/
  • intitle:yarl filetype:pdf inurl:http://www.justice.gov.uk/do wnloads/publications/inspectorate -reports/hmipris/immigration- removal-centre-inspections/
  • SELECT * FROM searchindex WHERE title LIKE “%Yarl%” AND filetype=‘pdf’ AND url LIKE “http:.. /%”
  • Reproducibility
  • tell stories
  • …but that’s another story…
  • tony.hirst@open.ac.uk @psychemedia blog.ouseful.info Anyquestions?