This is a draft from a book chapter on data journalism (part 1 looks at finding data; part 2 at interrogating data; part 3 at visualisation, and 4 at visualisation tools). I’d really appreciate any additions or comments you can make – particularly around tips and tools.
Wikipedia defines a mashup particularly succinctly, as “a web page or application that uses or combines data or functionality from two or many more external sources to create a new service.” Those sources may be online spreadsheets or tables; maps; RSS feeds (which could be anything from Twitter tweets, blog posts or news articles to images, video, audio or search results); or anything else which is structured enough to ‘match’ against another source.
This ‘match’ is typically what makes a mashup. It might be matching a city mentioned in a news article against the same city in a map; or it may be matching the name of an author with that same name in the tags of a photo; or matching the search results for ‘earthquake’ from a number of different sources. The results can be useful to you as a journalist, to the user, or both.
Why make a mashup?
Mashups can be particularly useful in providing live coverage of a particular event or ongoing issue – mashing images from a protest march, for example, against a map. Creating a mashup online is not too dissimilar from how, in broadcast journalism, you might set up cameras at key points around a physical location in anticipation of an event from which you will later ‘pull’ live feeds: in a mashup you are effectively doing exactly the same thing – only in a virtual space rather than a physical one. So, instead of setting up a feed at the corner of an important junction, you might decide to pull a feed from Flickr of any images that are tagged with the words ‘protest’ and ‘anti-fascist’. Continue reading