2015 Data Storytelling Studio @ MIT

Story Finding and Visual Design

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 Visual Design Process

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).

Data Story Design for Food for Free

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.

Designing a Food for Free Data Mural

To kick off our class, we’ve been creating a data mural for Food for Free, a fantastic local non-profit with a large food-rescue program. Once we turned their data into a story, we spent a class turning that story into a visual design to paint as a mural! This was a fairly standard data dural process, though very short on time, and I tried some new things to connect the visual back to the data it started from!

The Story

We started off by reminding ourselves of the story we found in the data:

Seeding the Visual Design

First we did some word-webs to try and make some of the more abstract words concrete. This activity helps by giving us a visual vocabulary we can pull from while designing a image-based narrative. For this story, I choose to pull out the words impact, partner, waste, and security – we made a word web for each. Here’s the one the students made for impact:

Then we did our pass-around drawing exercise, to try and turn the data-driven story intro a visual narrative. The students made a ton of great drawings:

I introduced a few new wrinkles to this activity this time around. On the second-to-last pass, I asked students to look back at the word-webs and see if each of those key concepts was incorporated in the drawing in front of them. If one wasn’t, I asked them to try and add it. In addition, on the last pass, I asked students to look at the design and back a the initial data handout. If there was a piece of the narrative that could be linked-to and supported-by some of the data easily I asked them to add it. This change brought us back full-circle to the data we started with, and helped us keep the visual narrative connected well to the qualitative and quantitative data it came from.

Synthesizing a Mural Design

Looking at these all together, we saw some commonalities that we really liked:

Food for Free was represented by a truck a lot
the recipients of their services were almost always drawn as people, while the donors were drawn as buildings
there were a lot of roads being used a scaffolding to connect visual elements
there were a number of drawings that used plants to symbolize growing

After discussing these, and other observations, we decided to go with a tree as the central visual metaphor. The roots would be the donors; the trunk would bring food to the leaves where recipients could pick off fruit and eat it. Food for Free trucks would be like little ants, moving up and down the roots and tree. Here’s the super-rough sketch I put together during the discussion in class:

Next Steps

Over the next few days, my collaborator Emily will turn this into a polished design and we’ll prep a canvas for it. In the next class, we’ll paint it! We only have an hour and a half, so the design won’t be too complicated.

A Data Story about Food for Free

To kick off the semester we’ve welcome Sasha Purpura, the Executive Director of Food for Free, to share some of their food rescue data and information about food rescue and food insecurity in the US. Food for Free does food rescue, and other programs, in the area of Cambridge, MA. Students pored over a Food For Free Data Handout we created, looking for stories they might want to tell.

This is a bit of a departure for the data mural efforts, as all past ones have involved the community group themselves, and the people they serve. This one, however, is being designed by the students in the class, not Food for Free staff and program recipients. Work acknowledging, but not a barrier to the process. This mural is primarily an exercise for the students in the low-tech story-finding and visual-design; the secondary goal is to deliver something of use to Food for Free.

Each of the four teams found a story they wanted to tell:

As you can see, they ranged from very focused, to more broad. Two of them focused on Cambridge, while others looked at impact.

In abbreviated version of our story-selection process, we defined a criteria for selecting a story and then decided to go with a merged story that I proposed:

The data show that Food for Free is growing our work with local partners to have an even greater impact on the issue of food security in Cambridge. We want to tell this story because there is still food waste in the area and we want to bring on more partners to help us fulfill our mission.

Data, data everywhere

A laundry list of data I produce in a 24 hour period:

Times blinked
Breaths taken
Number of heartbeats
Blood pressure
Steps walked
Calories burned/consumed
Daily [insert important OR unhealthy vitamin/nutrient] intake
Cups of water drank per day
Energy/carbon footprint
Trash generated
Words spoken
Words typed (WPM?)
Number of people interacted with
Emails, texts, IMs read/written
Minutes on phone
Number of times glancing at phone
Cellular data usage
Internet data usage
Internet browsing data, ad trackers/cookies
Location services/geolocation data
Time spent doing things/where (i.e. calendar data)
Minutes (hours, really) of video watched (episodes of television, YouTube videos)
Minutes of music listening
Minutes spent surfing the internet
Minutes spent playing video games
Total time spent looking at screens
Money spent (cash/credit)
Purchases made/businesses they were made at
Apps used
Websites visited
Tabs open
Search engine queries
Links shared
Tweets written
Facebook likes
Social media impressions (likes, mentions, retweets)
Photons received
Hours slept
Visits to the bathroom
Weight gain/loss
Height gain/loss
Hours worked
Salary earned
Mail received
Word frequency (“actually”, “obviously”, “literally”, “classic”)
Emojis/stickers used
Lines of code written

A [snow] day of data

Note: For the sake of brevity, there are a few pieces of data that are generated continuously.

Things like:

Checking email (email read data, email read receipts) – I check my email extremely frequently.
Pedometer data (Health data on iPhone 6)
Checking social (Facebook, snapchat, GroupMe, Slack)

Main log:

Wake up – check smartwatch for daily notifications
In-depth email checking on iPad
Check Facebook (generate view data)
Send a snapchat (read receipts)
Check Groupme (read receipts)
Google search (“Is the T running today”)
- Google Chrome anonymous browser statistics generated
Make a payment with MITFCU debit card
Tap ID on senior house, added to visitor log
Check email
Google search (variations on “seinfeld episodes”)
Send text messages
send Facebook messages
Watch Seinfeld episodes (Generate views on website)
Send Facebook messages
Download episodes of ‘Better Call Saul’ (Generate internet traffic statistics, views)
Send Text messages
Open Computer (generating usage statistics)
Using browser (usage statistics)
Searching Piazza (view data)
Search email (search results
Login to 6.815, change password (view data, sign-in log, change in db)
Writing code (IntelliJ usage statistics)
Git commits and pushes (creating commits, trackable history online)
Watching ABC with xfinity on-campus (login log, TV view information) (Fresh off the Boat)
Purchase a pizza (Creating an order, payment log on MITFCU)
Tap ID for EC (Entry log)
Tap ID for Senior house (Entry log)
Watch Youtube (view data, added to recommendations for user)
Visit a blog (view data)
Play music on Spotify (usage statistics, playback logs)

A Day of Tracking down Data

Date: 2/11/2015 (Wed)

7am

check time using my iphone and go back to sleep

8:45am

Waken up by alarm, snoozed the alarm, drank water (~three gulps out of a pitcher)
Ate a yogurt, two eggs for breakfast. While eating, look outside through window and realized it started to snow again. i.e. gathered information about today’s weather.
Attempted to check the weather/temperature using iphone, but it took too long (perhaps it was also trying to get out of its sleep mode too) and I didn’t really need/want to know the temperature, so I closed my phone.

9:00am

check time again and rush down to BC gym for morning exercise.

9:03am

Started biking: the machine kept track of the calories, resistance, speed, heart rate, muscle used throughout my workout.

10:10am

Checked the time, logged into gmail using my iphone
Took an elevator to get back to 5th floor.

10:19am

Used bathroom: some system in the dorm must have been keeping track of how much water I used, the electricity used to light the bathroom and heat up the water.

11:10am

Bought a cup of coffee and payed using my credit card.

11:15am

Swiped my ID in order to get into the Athena Cluster
Logged into the computer using my ID and password

11:30am

Logged into my gmail, read emails, filter out spams, added two important events to attend on the google calendar.

12:18pm

Create a google doc to record this document

1:10pm

Used my BOA online account to make transfer between my accounts
Bought lunch from Anna’s and used my credit card to pay
Checked my Facebook and replied to messages: FB kept track of not only the messages I wrote but also in which city and at what time I wrote the messages

2:20pm

In 6.033 Lecture, I answered an in-class vote

5:00pm

Swiped my ID to get into the Margarat Cheney’s room

5:30pm

Used an elevator in Rotch
Used a printer to print a problem set for 6.045 and readings for 6.033

6:50pm – 9pm

Answered an application survey on Piazza Career
Emailed a9 interviews and answered their questions
Searched on google a bunch of things (such as ‘what is UNIX’, ‘Trailing dot in DNS’, and ‘Regular Expression distributive?’)
Logged into Stellar to check psets and readings

10pm-11pm

Swiped my ID to get back into my dorm
Signed on the “get-better” card for Peggy
Set up an alarm for tomorrow morning
Practiced some javascript and saved the script on my laptop’s memory
Posted a short daily write-up on the blog
Listened to Pandora: marked “thumbs-up” or “thumbs-down” to indicate my preference based on which Pandora recommended the subsequent songs

What Do We Want to Learn?

To help me focus the various modules, I asked everyone to write up some sticky notes indicating what they wanted to learn from this course. Certainly there is an aspect of “you don’t know what you don’t know”, but the exercise is still valuable for me. These goals line up very nicely with the syllabus I have planned!

Here is the typed-up list:

working with unstructured data
image design
data -> story
data visualization tools
what defines effective data presentation and how to achieve it
telling a compelling story
data cleaning tools
use-cases and lessons learning
implementation in community (experience or) how to
technical and design visualization skills
what is valuable data?
how to better use the data as a narrative
best ways to visualize data in different contexts
where to go for data
develop me creative thinking skills
what is an effective visualization?
the process of going from an idea to implementation and how you know if you are successful or if you need to revise
what makes effective visualization (tools / concepts)
when to use what type of presentation
statistics! what does the data mean
data storytelling, digital tools ( for cleaning, collecting, presenting)
filtering data
different ways to present (apart from video or graphs)
techniques on storytelling
ethical data visualization
turning quantitative -> qualitative (w/out losing meaning)
making websites beautiful
overview of all methods to do data projects
data processing tools
how to see patterns and interesting (?) in numbers
techniques for effectively representing data for social justice
new tools and strategies
narration building / story boarding
(internet-based) data mining skills!
meaningful info from data
what makes people change minds
have a chance to apply good design principles
how to make data beautiful!
how to make data viz visually appealing / design language
related fields / areas of work and theory
how can you make complex ideas accessible without losing clarity
background overview
databases / data cleaning
ways to engage different communities with data
effective ways to visualize data (design focus)
types of visualization (ex. beyond basic charts / plots)
how to most effectively use context
hands on experience with data and compare different visualizations
how to leave a lasting impression

Learning from Each Other

One of our first activities was to assess what skill sets people had in the room. I am particularly interested in opportunities for us to all learn from each other, so we spent some time having folks indicate whether they were a novice or an expert on the following topics:

data munging
graphic design
statistical analysis
visualization
writing

Of course this type of work needs more skills than that, but these are some keys ones. It turned out that we have a good distribution of skills levels on all these topics!

Here are the big papers we used, so you can see for yourself!