Data Storytelling: Making Sense of Our Cravings
A series of data visualizations to help us better understand our cravings to build awareness towards our habits regarding cravings.
Above is a prototype of the interactive click-through site that displays the data visualizations. Click here for the full-screen experience.
Introduction
Design challenge: create a collaborative design project in the field of emerging media
When we went through the various types of emerging media, data storytelling was the one that stood out to me right away. Lately, I’ve been thinking about how powerful data is in today’s world; it plays a huge role in every business, from small independent ones to large corporations. It seems like we can access data on virtually anything, and I wasn’t sure where to begin.
After looking through many different examples of data visualizations, I realized I wanted to focus on more personal data, data that I found relevant on a micro-level. I regularly feel disconnected when I see large-scale data; it comes across as extraneous and cold, whereas I wanted to make something more relatable and human. I was mainly inspired by two data visualizations I came across: “Drawing Feelings” by Ondina Frate and “Shopping Pattern” by Christian Laesser. I found “Drawing Feelings” intriguing as it wasn’t a conventional data viz: the designer recorded her feelings over a period of a month and drew how she felt internally vs. externally. In “Shopping Pattern”, Christian Laesser saved his receipts from the same supermarket and recorded his purchases over a short period of time to see if the data would reveal any hidden shopping patterns.
As I explored my own habits and behavior patterns, I saw that there was a trend in sharing food cravings amongst my friends. While most often occurring during work hours when we happened to be idle and hungry, these cravings that we report reflect a lot about us and have the ability to tell a fascinating story. For the collaborative aspect, I asked four friends with whom I regularly exchange notifications of cravings to participate in the project by submitting their cravings to me through a Google Form that I created. They are responsive and reliable, people whom I can trust to take on the responsibility. If it were my data alone, a lack of comparison would make the data set too dull. The success of the project partially depends on my friends’ partnerships to best find associations.
Research
I looked at a few main areas of research that would inform my design for this project: data storytelling as form of emerging media, the data viz design process, and the science of food cravings. Data storytelling, simply put, is the use of data to create an insightful narrative for specific audiences. Data storytelling utilizes context, narrative, and effective visuals to effectively communicate curated information (Nugit). There is an art behind producing good data stories as it requires an understanding of the relationship between the data content and the audience, and knowing how to best tailor the narrative to engage the audience. The narrative is the most important aspect of data storytelling as “facts simply present data; whereas, a story’s narrative provides context, which augments our understanding and drives valuable insights” (Nugit). The data visualization process is very complex and detail-oriented, but I found two approaches that were helpful: one that is more academic from Ben Fry, and the other aimed for beginners from Ann K. Emery. The steps in Ben Fry’s process appear to be more concentrated on the data analysis with the first four steps dedicated towards it and the latter three involving the visualization (Fry, 2008) (see image below, left). Ann’s steps are easier to understand for beginners, and give a lot of tips (see image below, right).
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As I was collecting data on the cravings, I thought it would be useful to obtain more information on how cravings work so that I can leverage my research with the data. While cravings may derive from both physical and psychological factors, they are most often learned responses, related to “emotion, memory and reward” (Matteo, 2018). They have to do with cultural associations and personal histories. For example, the food I personally crave the most is pizza. Besides the fact that it is delicious, loved by all, and ubiquitous in the US where I’m from, my desire to eat pizza is probably more linked to my family’s tradition of ordering pizza when we are too lazy to cook at home (consequently, not needing to prepare, cook, or clean much). While this is not a unique practice by any means, it differs from person to person – one of my friends who is participating in the project regularly craves fried chicken because her household’s default “lazy food” is fried chicken; in fact, this is a “conditioned response” that is prompted on Fridays when she is expecting that they would order it (Brown, 2019).
While there is a generally negative perception of cravings due to the emotional, impulsive, and over eating that may be triggered from having them, experts propose acknowledging our cravings rather than outright denying them. When we are too restrictive with the way we eat, we may become more deprived: “studies suggest that avoiding certain foods altogether often makes them irresistible” (Mee, 2006). It is important to manage our cravings by being mindful of the role they play in our lives (Cassetty, 2019). By tracking our food cravings and looking at the data, we can understand more about ourselves by discerning what signals the cravings, whether it is physical hunger or emotional causes such as stress or joy. With the information, we can build better habits and reform our reward mindset for eating. While I was not sure of the outcome that the data will present, I was assured that even the act of collecting the data could be beneficial.
Process
After going through a few different processes for creating data visualizations during my research, I established my own to aid with providing structure to my own project:
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Initial ideation/research [Discover]
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Data collection [Discover]
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Data analysis [Define]
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Visual representation [Develop]
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Refining [Deliver]
I framed it using the “double diamond” design process to make sure I follow the systematic design approach in creating the data viz.
1. Initial ideation/research [Discover]
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User research – interviewed classmates/friends what they wanted to find out through documenting their cravings
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Visual research - looked at many different types of data visualizations for inspiration
2. Data collection [Discover]
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When considering different ways to collect the data, I realized that a Google Form would allow me to conveniently access the data as submissions are linked to a spreadsheet and the date is automatically recorded upon submission. I created a Google Form that was tested and revised to track the cravings, and sent an individual one to each of my friends who was participating.
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Using the information I gathered from the user research, I came up with the questions I wanted to answer through the data:
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When did most cravings occur (for the individual)?
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What were the most often craved foods?
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What were the most common causes of cravings?
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How often did the individual fulfill her craving?
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I formed my questionnaire for the Google Form based on those questions:
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What are you craving?
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Why? - this question asks the participants to reflect on the reason for their cravings
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Later, I adjusted the form to include the date/time as the participants will not always remember to submit their cravings at the time the cravings occur
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What day of the week is it? - I wanted to see if there was a correlation between the amount of cravings and the day of the week, but later realized that you can automatically produce this based on the date so it was unnecessary to include
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I also asked the participants to message me if they later fulfilled a certain craving. This was tricky because I couldn't figure out a better way to do this as Google Forms didn't allow you to change any responses from an older submission. It was problematic for 2 reasons: 1. The participants had to use a different platform to notify me. 2. I had to note this additionally, and it was not always convenient to do so when I received the messages. At the end, I had the participants double-check if I marked the fulfilled cravings correctly.
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At this point, we were already a few weeks into the semester, but I wanted enough data to work with so I set the period of data collection to be roughy two months, from mid-October to mid-December.
3. Data analysis [Define] - What is the story?
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This stage was incredibly challenging as I wasn't sure how to handle the data, even though I already had the questions that I wanted answers to. Another issue was that I could not set anything until the end of the data collection period. In hindsight, this was poor planning as I should have realized with such a short period of time, it would have been much more feasible to work with existing data.
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I took the reasons that the participants listed (I left it as a fill-in response vs. a multiple choice because I wanted to give them the freedom to easily determine a cause for the cravings) and looked through what were the common reasons people had submitted. I then categorized them based on what I thought made sense, such as if someone said the cause of a craving was due to it coming up in conversation, I marked it as "situational."
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I saw an opportunity to gain insight regarding the category of food most often craved so I added a column to include that. Once again, I grouped the cravings into categories I saw fitting based on what was submitted by everyone.
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I separated Date and Time, because while the date wasn't really relevant, the hour was as the time of the day could be of interest for when cravings occurred.
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I also added an extra column to indicate whether a craving was fulfilled or not.
Before: raw data
After: analyzed data
4. Visual representation [Develop]
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This stage was equally challenging as I was unfamiliar with the tools for data visualization. I began by sketching ideas based on the research I did for existing data stories and data visualizations, as Ann Emery advised to start by sketching before diving into software.
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Onto the digital: I researched several tools and ended up choosing Tableau. It seemed like all data visualization software had a steep learning curve, and this was no exception. I spent a lot of time playing around with the incomplete data I had and decided on a type of graph to visually represent the answers to each of the questions.
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With the complete data, I created the graphs on Tableau, and eliminated the unnecessary elements. I produced a graph for each person for each question, as well as one showing everyone's combined data.
5. Refining [Deliver]
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At this stage, I had to think about how to best present the data visualizations. I decided to make a click-through site prototype using Adobe XD as it would be a better experience compared to simply scrolling through a bunch of images of graphs.
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I wanted to display 3 components: a short bio of the participants, the data from the spreadsheets, and the final data visualizations.
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For the graphs themselves, I wanted the aesthetic of something that would fit this type of small-scale, personal data. I liked the way that my initial sketches looked and wanted to capture the same feeling through the final ones. I took all the graphs and painted digitally over them using a watercolor-like brush to mimic that hand-drawn/hand-painted look.
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Next, I chose to use the same color palette and typography from my similar-themed project for DM7903 for the click-through site.
Color Guide
Typography
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Adobe XD was intuitive and for this particular click-through site, the interactions were really simple to prototype.
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Finally, I showed the prototype to the participants to see if they were able to successfully make sense of their cravings. Here are some of their insights from seeing the data visualizations (I also included my own):
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Yitian: Initially, I was surprised that the largest chunk of my cravings occurred on Fridays, as I tend to be less restrictive with my diet and food purchases on the weekends. But thinking about it, I usually made my plans on Saturdays and stayed in to do coursework or freelance, meaning I would've been more prone to cravings. I knew my cravings would be mostly clustered in the evening as I tend to get hungry close to dinner time and in my attempt to lose weight, I've been cutting back on my portion size for dinner meals and craved more as I was still hungry afterwards. It was certainly no surprise that my cravings were mostly for Asian food or junk food, those are my comfort foods and what I think of most often when I want something to relieve stress. I believe that normally, I would have fulfilled more cravings but because I was recording everything, I was more intentional about not giving in to my cravings. This has definitely been a good practice of self-control to record my cravings, and I see value in doing so for dieting purposes especially.
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Marian: Most of my cravings are spaced out - either the beginning or end of the week, and occasionally on Wednesdays. Cravings are either in the morning or after lunch, probably because in the morning I'm thinking about what I could be eating later in the day and after lunch I'm just still hungry. Cravings are typically inspired by seeing things online or from conversations.
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Victoria: N/A (not enough recorded data)
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Steph: Seems like overall, most cravings come around 2-3pm for me, which makes sense because that's when I get bored or sleepy at work and start thinking about what might be good for dinner or what I should have had for lunch instead. I'm also pretty evenly spread in what I was able to eat from what I craved, which makes sense because I usually end up trying to get what I crave for lunch or dinner within the week. The most common reason for cravings for me were only 2 categories, situational or social media, mainly because of talking to friends about cravings and the amount of food-centric social media accounts I follow.
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Susanna: According to the graphs, I most often crave food on Sundays and Mondays, which happen to be times of greatest distress in relation to my job and outside responsibilities (Life Group on Monday nights). I also learned that the cravings happen most frequently around meal times, suggesting that I’ve become very accustomed to my schedule and expect and begin to think very strongly of food at 11:00/12:00pm and 6:00pm. Lastly, one of the charts showed that, surprisingly, I did not indulge in as many cravings as I thought I did. Upon reflection, I think I let cravings go when I have any form of food and my need (of comfort) is fulfilled.
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Reflection
This project has been a huge learning experience for me. I went in wanting to explore data storytelling as a new media, and falsely expected that I would be able to make something much more elaborate than what I was capable of. Naturally, as a complete beginner, I had to start with the basic graphs. I lacked the understanding to make the stunning data visualizations that really "wow," which was disappointing. I also made a few poor decisions, one of which I mentioned earlier about collecting my own data in such a short period of time. Besides that, there were just too many uncontrolled variables, such as quantity of data (dependent on the amount of cravings submitted). For example, one of my friends didn't happen to have many cravings at all, which meant there was insufficient data to analyze.
If I were to repeat the data collection phase of the project (and had funding to carry it out properly and on a grand scale), I would collect data over a much longer period of time, say 6 months to a year. I would interview and carefully choose a more diverse pool of paid participants, whom would have to sign contracts and agree to diligently track their cravings.
The collaborative process for this project required my management of the participants for data collection and their ability to create insights from the product. However, for a future project, it may be more valuable to collaborate in design practice with people like my classmates.
Looking back, I am still happy to have been able to dip my toes into the data visualization world and try out Tableau, which I had been curious about. I also appreciated taking part in a project without a definite solution, a true challenge. I do think, however, that I did accomplish my goal of wanting to make sense of our cravings through data visualizations and did experience how tracking my cravings helped control them.
References
Brown, J. (2019) Why you shouldn’t trust your food cravings. BBC Future. Available at: https://www.bbc.com/future/article/20190524-food-cravings-are-they-a-sign-of-nutritional-deficit. [Accessed 12 October 2019].
Camoes, J. (2011) Data Visualization Hierarchy of Needs. Excelcharts. Available at: https://excelcharts.com/data-visualization-hierarchy-of-needs/. [Accessed 26 October 2019].
Cassetty, S. (2019) Why we have food cravings and what to do about them. NBC News. Available at: https://www.nbcnews.com/better/lifestyle/why-we-have-food-cravings-what-do-about-them-ncna985606. [Accessed 12 October 2019].
Dykes, B. (2016) Data Storytelling: The Essential Data Science Skill Everyone Needs. Forbes. Available at: https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/#1d5d807252ad. [Accessed 20 November 2019].
Emery, A. (2014) The Data Visualization Design Process: A Step-by-Step Guide for Beginners. Depict Data Studio. Available at: https://depictdatastudio.com/data-visualization-design-process-step-by-step-guide-for-beginners/. [Accessed 13 October 2019].
Frate, O. (2017) Drawing Feelings. Available at: http://www.drawingfeelings.com/. [Accessed 8 October 2019].
Fry, B. (2008) Visualizing Data. Sebastopol: O’Reilly Media, Inc.
Korkishko, I. (2018) The UX design pyramid with the user needs. Syndicode. Available at: https://syndicode.com/2018/12/26/the-ux-design-pyramid-with-the-user-needs/. [Accessed 26 October 2019].
Laesser, C. (2018) Shopping pattern. Available at: https://projects.christianlaesser.com/shopping-pattern/. [Accessed 8 October 2019].
Matteo, A. (2018) Food cravings: they’re all in your brain. Learning English. Available at: https://learningenglish.voanews.com/a/health-lifestyle-food-cravings/4184716.html. [Accessed 15 October 2019].
Mee, P. (2006) Sometimes it’s better to acknowledge your cravings. The Irish Times. Available at: https://www.irishtimes.com/news/health/sometimes-it-s-better-to-acknowledge-your-cravings-1.1018587. [Accessed 12 October 2019].
Nugit. What is Data Storytelling? Available at: https://www.nugit.co/what-is-data-storytelling/. [Accessed 10 October 2019].
Waisberg, D. (2014) Tell a Meaningful Story With Data. Think with Google. Available at: https://www.thinkwithgoogle.com/marketing-resources/data-measurement/tell-meaningful-stories-with-data/. [Accessed 10 October 2019].