Category: assignments

With the topic of food security in mind and the Food for Free organization as our focus, our class set out to tell a story using national and local data, as well as data specific to Food for Free. From these numbers, we were to eventually paint a mural. How were we to turn these facts and figures into something more digestible and visually appealing, while also creating impact?

Our design process began first with finding and understanding the data that we want to present. By talking with a representative from Food for Free, we were able to gain a deeper insight into how the organization fit in with regards to the food security space, to gain a bit more personal connection and context for the data sheets we were provided.

From this data, we entered the process of drawing connections between the sets of data. We focused on finding different story types, like ‘factoids’ that stand out, ‘interactions’ that seem to correlate, ‘comparisons’ that reveal differences, changes’ that are significant, or ‘personal’ stories that connect to people. Here we sought out types of stories similar to those described by Rosenbaum in the Data Journalism Handbook. We didn’t necessarily look for a whole cohesive narrative yet – like Barr, we were simply poking around with our dataset attempting to make connections.

Once our groups put together our final conclusions, we combined our thoughts to determine the ‘story’ that this dataset told: that “Food for Free is growing our work with local partners to have greater impact on food security in Cambridge.”

It was at this point that we moved onto actually visualizing our data-set-turned-story. Through the use of some mapping and drawing exercises, we were able to isolate a few common symbols that we wanted to use to represent our story. From here, we were able to sketch out a rough mural, an image representing growth and reuse, using the symbols we determined.

Notably, as a mural, our process lead to a different type of narrative visualization. Segel and Heer sought to systematically describe and review different forms of data visualization, and even came up with seven ‘genres’ to do so. However, our mural doesn’t really fit into any genre they specify. Most of the case studies they examine contain maps, charts, and graphs, which have their own artistic direction and are designed purposefully to craft an easily digestible visual message.

Our mural instead will not contain graphs or numbers, or even text for that matter. Instead, our process produced a form of data-visualization that leaves more for the viewer to interpret. From here, we can definitely realize that their are many uniquely creative ways to tell stories backed with data – in this case, with art!

To create our data mural about Food For Free, we stayed close to the principle of starting with data and finishing with a story. We began by gathering information from Executive Director Sasha Purpura, who spoke about the organization’s history and goals. We then looked at data about Food For Free’s reach, as well as statistics about food security in the United States. Guided by Rahul’s taxonomy of data stories, we came up with multiple stories and chose the one best suited to conveying Food For Free’s mission/impact.

I think this was an effective way to find the story, especially because many of us were unfamiliar with the organization – thus, starting with the data was essential.

From the story, we made rough sketches of potential murals. We identified the common visual themes and incorporated them into the final mural, which allowed many of our ideas to be included. When deciding whether or not to include certain elements, we used the criterion of the story we agreed upon before-hand. I think this gave our discussion a good amount of structure and the process went smoothly.

As for the mural we came up with, its dominant visual structure is the tree. The upward sweep of the roots into the trunk and ultimately the branches is the main storyline through which we communicate Food For Free’s mission, impact and plan for the future. We start at the root, where we immediately see Food For Free trucks beginning to transport food from donor organizations to the rest of the ecosystem/community.Our eyes are then drawn up to the branches, where we see the result of the organization’s work, represented by thriving children and adults in a tree house. Finally, we can examine the messaging interspersed in the mural as fruits and flowers.

In this way, the mural is author driven, which makes sense as our goal is to tell a strong story about Food For Free. This is consistent with Segel and Herr’s suggestion that such approaches are best when the goal is storytelling or efficient communication. There is no interactivity and while there are no linear “scenes” in this single frame, there is a strong linear progression suggested by the tree metaphor, in which you start at the root and make your way up.

The messaging, in addition to being matching the visual metaphor of the tree, is communicated through memorable factoids. This is preferable to overwhelming the audience with large quantities of information – as would likely be the case if we tried to put the raw data and tables we started with onto the mural — and is in line with Segel and Heer’s recommendation that such factoids are often better for a general audience.

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.

I really enjoyed the emphasis we placed on narrative throughout the data mural design process; I think Colin Ware is correct when he says the purpose of a visualization should be to “capture the cognitive thread of the audience” (12).  Our mural does so by combining simple visual language (people receiving food; trucks carrying food), which conveys concrete ideas best explained through pictures, with the more complicated language of metaphor.  Some ideas are hard to illustrate from first principles but easily explained using figurative language; the tree metaphor we use, with suppliers at the roots and Food For Free trucks delivering food to people at the tree’s leaves, uses a universal visual symbol (the tree) to explain Food For Free’s business model.  As with regular language, the grammar of visual metaphor is generative-we can combine it with other units of meaning to make a larger, still meaningful structure. As in the example on p. 7, we combine spatial logic (food moving from roots to leaves) with visual logic (the roots and leaves are linked by the roads the trucks travel) to explain how the food is transported from suppliers to those who need it.  We are using a single-frame narrative for our mural, with the result that we, the authors, are responsible for indicating a narrative thread within the finished product as we would not need to do with a film, slideshow, or comic strip.  However, we also have the freedom to include elements outside a single narrative without creating subsequent frames; we can add framing elements to fill in details of the story so the reader can explore the mural on their own terms once they have familiarized themselves with its overall arc, as Segel and Heer suggest.  Overall, our mural incorporates many elements of good visual design and, I hope, will be able to capture people’s attention and understanding.

Our story-finding and visual design process for the Food for Free mural was an interesting contrast to my ongoing data design process for an upcoming environmental health community meeting.

I’m currently in the process of designing data shirts for individuals who participated in an environmental health study and contributing to the overall data story that will be told at a community meeting in the next few weeks.  There are a few notable differences I’ve seen between our processes:

(1) The environmental health process is much slower than the Food for Free design process.  I’d attribute this to the acceleration of the Food for Free process and the complexity of the environmental health data.  much much larger.  The data cleaning and data culling step has been months in the making for the environmental health data.

(2) The environmental health process involves more independent individual work, with occasional reports to the group and group brainstorming sessions.  The balance in the Food for Free process was the reverse: we worked occasionally as individuals, but more often in small groups or as a whole class.

(3) The Food for Free story is more narrative based than the environmental health data story which is more exploratory.  Again, this is partially a function of the data and aims–for the environmental health data we are providing personalized data to each individual while keeping the foundational design static.  The limits the space for individualized storytelling.  But, the community messaging section of the environmental health reportback is more narrative based like the Food for Free story.

(4) The environmental health artifacts land more in the “science” than “story” aesthetic.

There are also a few similarities:

(1) Neither process directly involves “users” (e.g. study participants or Food for Free recipients) for sustained periods of time.  Some user testing was done for the environmental health artifacts, but users were not part of the design team.

(2) Both the mural and the data shirts are static non-interactive single frame designs.  Some of this is a function of the chosen medium (e.g. interactivity is more challenging with physical, not digital, objects).  A second part of the environmental health reportback involves online materials that include many of the components mentioned in Segel (consistent visual platform, multi-messaging, details on demand…)

 

Colin Ware opens chapter 7 of his book Visual Design for Thinking with a thought experiment. He asks the reader to try to imagine the following sentence with only images: “If halibut is more than ten euros a kilo at Good Food, go to the fish market on 5^th Street.” Some things are best said with words, while others are said more aptly with pictures.

Murals have to be easy and quick to read in order to get a message across to its audience. When class met on Thursday, February 19, we had already identified the story we needed our data mural would convey, focusing on the impact that Food for Free has on food security in the Cambridge community due to the organization’s partnerships with local businesses. Our challenge, then, was how to tell this story visually.

The word web activity—in which we added words onto a network and then devised icons to represent these words—was useful in devising images to be used in our final mural. Next, the pass around activity helped us weave the imagery together. These two exercises were useful in identifying a common visual vocabulary to convey our message.

The imagery we were most drawn to was a plant metaphor, with the roots referring to the locations Food for Free rescues from, and the branches or leaves indicating the organizations that the food gets distributed to. According the Colin Ware, this is pattern perception, in which meaning is made by the relationships between the visual elements, from the roots to the leaves.

The data mural we designed was, in the words of Colin Ware, a “single-frame narrative.” It did not have to deal with information flow across multiple panels, and the complex and agonizing layout and continuity concerns described in the Segel & Heer article. We seem to have skipped over many of the most challenging aspects of narrative design by choosing to portray snapshots of data rather than a process that has a beginning and end.

It’s not that we didn’t try to tell stories of change over time; we wanted to show the growth of FFF, its impact in the community, and the flow of food from sources to sinks. But we did this by overlaying information into a single image, rather than trying to represent different states with arrows or other flow control tools. For example, we overlaid increasing widths of the central trunk of the road-tree to show the flow of food increasing over the years.

I thought this was appropriate for a few reasons: 1) because we were trying to convey a symbolic message more than to explain the details of a technical process, which might have been done more clearly with panels, arrows, etc., and 2) (much more wishy-washily) because dividing up our space with barriers or blanks seemed out of line with the themes of togetherness/community/cyclic-ness we were trying to cultivate. Our goal did not have to explicitly include simplicity/good pedagogy, since we picked a pretty small set of data to represent, and the process we were representing was also not a complicated one. Therefore, we could pack information quite densely into the space allotted to us without fearing confusion or loss of our audience, and make a single image meant to spin out all of the desired cognitive threads in our viewers.

We ended up focusing most of our energies on integrating symbols with each other in an intuitive and evocative way, working at least one level of abstraction up from the actual creation of symbols; designing symbols is a hard problem in itself, and we had limited time and artistic skills. For a few-hour design exercise, I thought we did a good job of creating an image that conveys positive and pertinent messages at all levels of viewer attention; the tree in a circle, visible at a glance, evokes sustainability; the trucks traveling up the tree trunk convey succinctly what the organization does; and the people benefiting from the tree (picking fruit, playing in its branches) evoke community.

Interestingly, our story-finding began with Sasha Purpura telling us the mission of Food For Free. The goal here was not to have a story given to us, but to help us find a story in the data. “Start With the Data, Finish With a Story” mentions the importance of of having a “clearly defined objective in querying the data”. After understanding the context from Sasha’s story, we could pose questions and see if they were supported by the data. In this way, the data guided the mural’s story.

By going through Rahul’s taxonomy for data stories, our approach was similar to that laid out in “Data Stories”. Specifically, we looked at changes over time, associations, and comparisons. While it was not as common, we also used the “blacklist” approach described in “Start With the Data, Finish With a Story”. For example, my group looked at the quotes, which were quite positive, trying to identify something that recipients still needed. Our examination indicated that there isn’t much dairy rescued, which could be an interesting story to pursue.

At the end of story-finding, we wrote the story in a couple short sentences. “Start With the Data, Finish With a Story” says that a data story should “hit [readers] with a headline figure that makes them sit up and take notice”. By writing the story so concisely, we were able to make our mural communicate the “headline” of the story.

Since it is not interactive, our data mural is primarily author-driven. However, as ceriley mentioned in the post below, it is partly reader-driven because the reader may choose what to focus on. The mural design contains a number of small components (ex. trucks coming up roots, donor names on the buildings on bottom, actions of individual people) that are not as immediately noticeable as the tree itself. Unlike other visual narrative styles, such as infographics, the mural does not prescribe an order for the reader to follow. Instead, the reader is free to examine each component in any order. In this way, we can argue that it is partially reader-driven.

Although it is not a video, since our design contains several components, we need a way to preserve continuity as the user looks at different parts of the mural. Continuity is maintained through the tree – all the components (ex. trucks, donors, people) are interacting with the tree in some way.

Our story-finding process began with looking at a bunch of data connected by the fact that it related to Food For Free, and we needed to construct a meaningful message about their organization, supported with their actual impact on the local communities. This initial search was summed up in something the Data Journalism Handbook briefly mentioned — “key terms don’t always give you what you want, sometimes you have to sit back and think about what  you’re really asking for.” We couldn’t just look at the data and pull out the biggest numbers or the most shocking facts, although that was a starting point to get ideas flowing in groups. Eventually, we had to step back and see how the data was interconnected to create a larger, more powerful story; one where someone who sees our mural “should almost be able to read the story without having to know that it comes from a dataset.”

When we were actually designing the mural, it felt like our group work revolved around the design principles in the Segel & Heer reading without actually mentioning them by name. In brainstorming the words and associated symbols, we figured out visual mechanisms to highlight particular story elements and how much messaging to include in our final mural design (solely pictures or which statistics to include). When we did the pass-along drawings and gathered as a group to work out a draft for the final design, we figured out how to combine different elements to visually structure both the mural and the narrative to make sure we were conveying the story effectively.

I’d be curious as to what other people think about the author-driven/reader-driven nature of designing a mural, because it seems to be a little bit of both. On the one hand, we curated its development and created the story we wanted to tell — the community viewing the mural isn’t going to see the pages of data about Food for Free unless they actively search for them. On the other hand, once the mural is painted, it is entirely out of our hands and is up to the viewers’ interpretation. Even though we tried to highlight certain ideas, it’s up to the people who look at the mural to notice the details (or not) and start conversations about it (or not).

 

We blended a number of different techniques to facilitate our exploration of the data story behind Food for Free’s mission. In contrast to the paradigm of “start with the data, finish with a story” that is described by the Data Journalism Handbook, we started the design process for our data mural by listening to Sasha Purpura, Executive Director of Food for Free, talk about the ethos and mission behind Food for Free, and the impact that the organization has had thus far in the local community. By listening to her first-hand account of the story behind “Food for Free”, we seeded our data exploration with a big picture idea of how Food for Free operates and its role in addressing a well-defined and critical piece of the broader issue of Food Security.

After that, we jumped into a few collaborative design exercises to openly explore the available data and hone the data story that we are directly expressing through our data mural. First, we employed a framework to develop data stories through identifying interesting factoids/outliers within the data, examining surprising correlations and interactions between different data points, analyzing how the data changes over time, revealing comparisons within the data, and also leveraging what we know about our local community to infuse the data story with personal experiences. These techniques are very similar to those described in the Data Journalism Handbook for developing context for data stories.

As a class, we discussed the individual data stories that emerged from our exploration, and developed visual designs through a group drawing exercise. Similar to some of the design strategies highlighted by Segel & Heer in their framework for narrative data visualization, we eventually developed a cohesive design to visually structure the data story, and highlighting specific motifs and patterns within the data itself. Since our data mural fits within the author-driven genre of narrative visualization, it provides a visual metaphor telling a focused story about Food for Free. It differs from many of the the case studies presented by Segel & Heer since it lacks the aspects of interactivity and transition that are typically present in data visualizations presented with a more reader-driven perspective.