Silk is a quick and easy way to create data visualisations, so we’ll take a closer look at it in the rest of this masterclass.

There are plenty of other tools for finding, filtering and visualising data, and we deal with a number of them in depth in Chapter 13 of Multimedia Journalism. Silk is a worthy addition to the platforms discussed there.

Please note that while the tuition in this and other masterclasses is self-contained, it is designed to dovetail with the wider tuition, background information and general principles of good journalism discussed in depth in the book.

You can buy the book, as a print or ebook product, here for the UK

and here for the USA

On to Silk then.

Silk has a guide: How to use Silk for journalism, here: https://www.silk.co/help/tutorials/how-to-use-silk-for-journalism

It takes you step-by-step through the creation of a data journalism project using data from the Tampa Bay Times which reveals what percentage of the money raised by a range of charities goes on admin, rather than helping those it was set up to support.

Silk lets you practise with other data it provides. You can access this sample data, when you are creating your story, by clicking on the option to ‘Use sample data’. A range of options appear.

in 2 use silk sample dataset

I picked the Successful kickstarter tech and journalism projects

When it loads, this information appears under the Grid option in Explore. That’s the option Silk recommends if you have images with each item (or datacard) in your data.

If you click on the Datacard tab you can scroll through and see what information each datacard contains. That will give you some ideas about how you want this data visualised. For example, projects are categorised, their geographic location is given, and what funding they have.

So mapping is possible. But map the information under country and you discover one location pin represents 147 individual projects or datacards.

Can you subdivide location into cities?

You can, by changing the filter from Country to Location, which makes for a more informative map. Zoom in on, for example, the US, and you get individual pins which can be hovered over to reveal details of a project or projects in a given city, and clicked on to take you through to the relevant datacard. Taking Minneapolis, for example, clicking on the location pin reveals four projects and you can access each one from the information revealed.

I’ve published that visualisation here: http://goo.gl/gzGl9M

Here it is embedded (you may find it easier to zoom in before exploring the visualisation) :

You can click on a pin and see all the projects in that city, and then scroll through them and, if you like, click on any that interest you and be taken to the individual data card.

Under the Bars visualisation you also get each individual project listed independently, and you can add tags such as the sum pledged and percentage of funds required, so that when you hover over a listing that information is displayed.

This listing goes from the project with the largest sum raised at the top and drops down from there.

I’ve also published that visualisation here (scroll down past the map visualisation to find it) : http://goo.gl/uwULBJ

What you need to think about with each visualisation you create is – as you would with any piece of journalism in any format – what is the audience you seek to serve and what do they need to know?

Get that straight and then you’ll know what you need to tell them.

If you wanted to make the point that almost a quarter of the money pledged goes to a handful of projects, this pie chart would illustrate that at a glance:

with 2 pie

I’ve published that visualisation here: http://using-silks-sample-datasets-658738.silk.co/

It’s worth running through some of Silk’s other sample data, to get an idea of what you can do with the platform.

Next: Data Journalism Project 3: Guardian data and Silk visualisation – how busy is your rail station?