Our project for FOOD FOR FREE started with identifying a possible story based of the dataset provided by the association.
Not only the data was backed up by a short presentation by their director, which gave a helpful insight and personal experience as well as stressing out some key elements of their story.
As stated in the Segel & Heer reading, traditional narrative visualizations are usually based out of the data itself. The data reveals the story. In our case it was slightly different, as it was great to get an insider perspective and the director’s testimonial as a start.
How to turn the dataset into a story? Our methodology was a mix of different techniques and broken down into different steps. First, we found different storylines and then identified keywords, and from keywords then created mind maps that circled those latter.
The Director’s speech gave us some hints for our storylines: some focus on the City of Cambridge, the results of their labor, the impact of the association on the food served, the increase of produced food, etc.
We translated data into one or three sentences for most and then merged it into one paragraph. Our word webs became rough drawings, and we got our mural skeleton : a tree-like with trucks making connections between buildings as donors and people as recipients.
The story we came up with is simple as we are doing a mural, limited interaction and minimum messaging. And we didn’t not use the common comparative techniques as mentioned in the Data Journalism handbook eg as proportion example, internal or external comparison or change or over time.
The goal of our visualization is to “engage with reader in finding and telling their own stories in the data”. So the story we came up for FFF is not only reader-driver or author-driven it stays in the middle of this spectrum. Our messaging is clear, engage with the reader. However there is no direct interactivity unless there is a “call to action” in our mural something like a “contact us” for instance.