Tag Archives: data journalism

Data journalism’s commissioning problem

Square peg in a round hole

Data journalism is still a square peg in a round hole when it comes to commissioning. Image by Yoel Ben-Avraham

Peter Yeung has a good point: why is it so difficult to get editors to pay for data journalism?

In a series of tweets we tried to find some answers.

Firstly, commissioning isn’t set up for data journalism. Editors instead try to fit it into established structures for commissioning text-based news and features, with the result that:

a) The pricing doesn’t reflect the work involved; and

b) Any interactivity and visuals become incidental to the process instead of integral.

And yet the value of data journalism has been repeatedly proven, and organisations are spending money on it: just not on commissioning. As Yeung added:

“I find it strange publications invest in data editors and journalists, but not data budgets”

The FT’s Martin Stabe suspected it wasn’t just a data journalism problem:

“This probably extends to lots of digital-only content, not just data journalism.”

A related problem is the lack of standardisation in data journalism: there is no equivalent to the payment by wordcount which print journalists have so long worked by.

Instead, organisations ‘insource‘ data journalism work to internal teams, either data teams or ad hoc teams formed from existing personnel (think the MPs’ expenses or Wikileaks investigations…

…Or they ‘outsource‘ data journalism work to external agencies etc.

This is a problem also highlighted by Alfred Hermida in his research into Canadian data journalism, ‘Finding the Data Unicorn‘: only one job title showed up four times “and that was the general reporter/journalist category.”

That’s our take. What about yours? Why isn’t data journalism properly commissioned? And how do freelance data journalists get work?

Related:

My latest data journalism ebook is now finished

Data journalism book Stories with SpreadsheetsMy third data journalism ebook, Finding Stories With Spreadsheets, is now finished. It’s a book which covers a wide range of spreadsheet techniques from basic calculations like proportions through to techniques for merging datasets, looking for errors and working with dates.

I’ve tried to cover all the functions used most commonly within data journalism, including some specific to Google Sheets, but if you know of any that aren’t mentioned, or have a problem which isn’t solved by the book, I’d love to know.

Likewise, many chapters have sample datasets to try the techniques out, but I’m always on the lookout for particularly illustrative datasets or examples.

I’ll continue to add to and update the book (one of the reasons I publish with Leanpub) as I come across new techniques and examples. Let me know if you want me to add anything.

What I learned at Jan Willem Tulp’s workshop at Tutki! 2016/NODA16

Jan Willem Tulp

Jan Willem Tulp’s workshop

In a guest post first published on her blog, Maria Crosas Batista sums up the key takeaways from a session at the Nordic investigative journalism conference Tutki! 2016 by Jan Willem Tulp, the data experience designer behind Tulp Interactive.

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Mapping tip: how to convert and filter KML into a list with Open Refine

Original image by GotCredit/Flickr
Original image by GotCredit/Flickr

If you are working with map data that uses the shapes of regions or countries, chances are you’ll need to work with KML. In this guest post (first published on her blog) Carla Pedret explains how you can use the data cleaning tool Open Refine to ‘read’ KML files in order to convert them into other formats (for example to grab the names of places contained in the file).

KML (Keyhole Markup Language) is the default format used by Google’s mapping tool Fusion Tables (Google bought the company which created it in 2004), but it is also used by other mapping tools like CartoDB.

The open source data cleaning tool Open Refine can help you to open, process and convert KML files into other formats in order to, for example, match two datasets (VLOOKUP) or create a new map with the information of the KML file.

What is the difference between XML and KML?

In this post, you will learn how to convert a KML file into XML and download it as aCSV file.

XML – Extensible Markup Language –  is a language designed to describe data and it is used in RSS systems.

XML uses tags like HTML, but there is a big difference between both languages. XML defines the structure of the information, whereas HTML focuses on other elements too, including their meaning and arrangement (even when it is not supposed to focus on appearance), and the importing of other code and media.

KML – Keyhole Markup Language – documents are XML files specific for geographical annotations. KML files contain the parameters to add shapes to maps or three-dimensional Earth browsers like Google Earth.

The big advantage of KML files is the users can customize the maps according to their data and without knowing how to code.

Image of a KML map in Google Fusion Tables

Image of a KML map in Google Fusion Tables

Convert a KML file to XML

You can find KML files using Google Tables search (make sure you have ‘Fusion Tables’ secleted on the left).

Type what you are searching for and add the word geometry or KML.

Captura de pantalla 2016-02-28 a las 19.59.01

Open the fusion table and check that it has shapes by looking for a ‘map’ view (normally this has its own tab).

You should be able to download the KML when looking at that map view by selecting File > Download.

Once downloaded, to convert the file, upload your KML in Open Refine (download Open Refine here) and click Next.KML 1

In the blue box under your data, select XML files.

KML2

Now in the preview you can see the XML file with the structure of the information.

If you want to create a map with your own data and the shapes in the KML file, you need to match the KML with your data.

The example I have used contains the shapes of local authorities in the UK. I want to match the shapes in one dataset (the KML file) with information in another dataset on which party runs each council.

The element both datasets have in common (and therefore the element which will be used to combine them) is the name of the councils. But you need to check that those elements are the same: in other words, are the councils named in exactly the same way in both datasets, including the use of ampersands and other characters?

Have a look at the XML preview and try to find the tags that contain the information you need: in this case, authority names. In the example the tags containing the authority name are <name></name>.

Hover over that element so that you get a dotted box like the one shown below. Click on that rectangle and wait until the process has finished.
Captura de pantalla 2016-02-28 a las 20.29.06

You should then see a column or columns as the picture shows.

Captura de pantalla 2016-02-28 a las 20.33.07

On the right hand side of the page, change the name of your file and click on Create a new project.

Once created, you now only need to export it. Click on Export and select the format you prefer.

KML 5

What originally was a KML file is now a filtered list with data ready to check and match against your other dataset.

Do you use Open Refine? Leave a comment with your tips and techniques or send it to me at @Carlapedret..

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That data journalism MOOC is now available on a new video training website

In 2014 I was part of a massive open online course organised by the European Journalism Centre (EJC) called ‘Doing Journalism with Data‘. If you missed it first time round (or never finished), the EJC has just relaunched that data journalism course as one of the courses on offer on their new dedicated video training platform, LEARNO.net. Continue reading

5 of the best: podcasts about data journalism

Image of podcast on mobile

Image by Carla Pedret©

Podcasts are a great way to listen to stories on the move, be entertained, or keep up with developments in a particular field. However, have you ever thought about using them to learn data journalism?

In this list, I have pulled together some of the best podcasts about data. Some are specifically about data journalism, whereas others approach data from another perspective.
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5 great data visualisation pieces from outside the newsroom

Some of the most interesting examples of journalistic data visualisation come not from newsrooms, but from creative agencies or companies. In a post first published on her datavis blog Dinfografia, Maria Crosas Batista outlines 5 of the best examples:

1. The Refugee Project

The Refugee Project GIF

Interactive map designed by Hyperakt & Ekene Ijeoma about refugees’ migrations since 1975. It includes historical explanations of some large movements and events to contextualise them. Continue reading

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