The event will mix industry speakers and experts with practical sessions: there’ll be drop-in sessions on getting started with data journalism, an information security ‘surgery’, and some speakers have been asked to focus on practical skills too.
On top of all that, attendees will have the opportunity to nominate skills they want to learn – we’ll put on workshops for the most popular topics!
After winning two prestigious data journalism awards since launching in 2015, the Peruvian medium Convoca has launched its first crowdsourcing campaign to build a global community around its investigations. Nuria Riquelme spoke to founder Aramis Castro about the project.
Convoca has become a reference point for data journalism in South America. With a team of around ten people including system engineers, computer technicians and journalists, led by Milagros Salazar, a professional with over 15 years journalistic experience, they have pioneered data journalism in Peru. Continue reading →
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?
My 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.
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.
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.
In the blue box under your data, select XML files.
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.
You should then see a column or columns as the picture shows.
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.
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..