Visualizing Patterns

Communication Studio Project 3

November 4

After looking over the data, I’m seeing an opportunities to ask questions about food access as compared to neighborhood characteristics, such as percentage of renters, age distribution, income level, and health indicators. There could be interesting insights into how food access is different between rural and urban areas, and how having a vehicle changes food access or food security?

November 9

As a team, here are the research questions we developed:

  1. What is the demographics of these locations?
  2. How many low-income groups are there in urban and rural ares?
  3. How food accessible are urban v/s rural areas?
  4. Is there a relationship between low-income groups and concentration of particular food assets in Allegheny County?
  5. How many people are food insecure in Pittsburgh?
  6. How many people access food assistance?
  7. How many people are eligible for food assistance?
  8. What are the social characteristics of food deserts?
  9. How diverse are low-income areas? How does the diversity differ from non-low-income areas?
  10. Does low-income area automatically mean low accessibility to food?
  11. What is the geographic and demographic division of WIC families?
  12. Is enough food accessible to WIC families?
  13. How many people are deemed food insecure?
  14. How many people who are food insecure are elligible for federal food assistance?
  15. How many people food assistance?
  16. Who does the data undercount or underrepresent?

November 11

I’m going to organize my data around the a question: How might we paint a better picture of food insecurity in the United States? Implicit in this question is the idea that the current picture of food insecure is somehow incomplete or insufficient. The “better picture” that I’m aiming to create is not a more accurate metric to track food insecurity in the US. That’s a matter better left to statisticians and social scientists. The improvement I’m aiming to make to the perception of food insecurity is to combine data with the lived experience of food insecurity. I’m curious about how I can illustrate the lived experience that lies behind a number like “29 million Americans are food insecure.” How do we contextualize that data? How do we illustrate the heartbreaking experience of food insecurity for each one of those 29 million lives?

Photos from a brainstorming session on Tuesday

November 16

For class, I put together a top-level user flow for the digital data tool that I’m aiming to create. It will attempt to combine macro-level data on food insecurity from the Census with personal stories about food insecurity, pulled from Feeding America and local food banks. First, the user will encounter the macro level data, then an interaction will trigger the personal stories to come to the surface. Finally, the user will be able to see the details of the story and, with it, statistics and pieces of data that relate to it. For example, if the story concerns a person on SNAP, there may be a statistic about the number of Americans who receive SNAP benefits.

November 18

As a group, we’ve coalesced around a few key metrics. For each Census tract in the Pittsburgh metro area, we’ll be looking at food accessibility (percentage of people who are 1/2 mile or 1 mile from a food source in an urban area) and food accessibility considering vehicle access. For each geographic area that users view, they will also be able to filter rate of food accessibility amongst certain populations including total population, seniors, children (>18 years old), and SNAP recipients.

November 23

Ah, where to begin since last we spoke. All the progress I’ve made has been on my individual project, so I’ll only be discussing that in this post. I’ve decided to look at eight stories of food insecurity pulled from various food banks across the country. For each story, I’ll visualize three pieces of data for the Census tract that the subject of the story belongs to. So, for example, if the story was of a person in Pittsburgh, the data would be related to the census tract in Pittsburgh. The three pieces of data for each story are (1) prevalence of food insecurity, (2) percentage of food insecure individuals who do not qualify for federal nutrition aid because their income is too high, and (3) percentage of food insecurity amongst children.

A screenshot of the central spreadsheet I’m using to organize my data collection. All data is sourced from the Current Population Survey’s Food Security Supplement (CPS-FSS).

Novemeber 30

Over break, I explored some additional options for the four states of the data visualization. I’m still planning on using the layout discussed in my last post but wanted to explore other ways to visualize each of the three categories of data when a user hovers over the statistic.

December 2

Exploring Pattern and Meaning

In class on Tuesday, Stacie pushed me to explore connections between the visualization and the topic of food insecurity. Food insecurity brings to mind feelings uncertainty, unknowing, shame, worry, and (obviously) insecurity. Stacie mentioned that to accurately represent these ideas, it may be helpful to think about visualizing absence instead of presence. How might the visuals prompt the user to think about absence the lack of something?

Pattern ideating
The particle effect is intended to communicate the idea of uncertainty or insecurity

December 8

Project Takeaways

The delivery of the project is the project. I had the experience this week of two final presentations for CD and IxD Studios. For IxD, we took a generous amount of time as we wrapped up the project to prepare the presentation and our remarks for the presentation. For CD I was, up until the last minute, fine tuning the visuals and the ideas of the project, continually pushing and pushing the presentation back further. This meant that I had two very different presentation experiences this week, and I strongly prefer the IxD version. With the more thoughtfully prepared presentation, I felt that it truly reflected the time and effort I had put into the project. With the CD presentation, I felt as though the presentation did a disservice to the work that I had done during the project. I need to make the mental shift toward thinking about how the work will be presentation at a much earlier stage. Fair or not, the presentation of the work is the biggest factor in how we evaluate our design. I need to give it the thought and time it deserves.

Masters candidate in Interaction Design at Carnegie Mellon University