Perspectives in Design: Feral and feminist data visualization

A sample process blog of the assigned project. Cathryn Ploehn, MDes

Cathryn Ploehn, MDes
5 min readFeb 3, 2021

What follows is a sample process blog for my module as a guest lecturer in The Perspectives in Design, a UT Austin course instructed by Kate Canales and Gray Garmon.

0. Situate

I begin this project by situating my perspective as a designer. I am a white (of Mexican and European + descent) woman who grew up in the suburbs. I am currently living in Texas, specifically the blackland prairie ecoregion. Here, little bluestem grass, switchgrass, asters, pecan trees, sycamore trees, yucca, make this place I call home livable.

In taking walks over the course of the late summer, fall and winter this year, I’ve noticed several interesting organisms that make this home healthy and beautiful.

Bald cypress (left), Oxalis (center), and Pecan trees (right)

II. Purpose

For this project, I decided to visualize my Fitbit sleep data from the last half of March, 2020. As the pandemic began to hit the USA during this time, things felt quite uncertain. At the time, I started wearing a Fitbit for an art project, which collected the hours of sleep I got each night. The data covers sleep data from March 12th through April 1st, with the following parameters:

  • Sleep start data/time and end data/time
  • Minutes asleep and awake
  • Number of awakenings
  • Time in bed
  • Minutes of REM sleep
  • Minutes of light sleep
  • Minutes of deep sleep

These parameters are actually calculated by Fitbit based on a heartbeat measurement. By and large, REM and deep sleep often indicate the quality of sleep. Data comes from an interest in measuring sleep quality, based on the notion of tracking your sleep through measuring heart rate with this device. The quantified self.

Alternatively, this data might be able to bring others into the experience of my sleep during this fraught and anxious transition into our current social-distanced world. That might be the north star for this project of visualizing data.

III. Attunement

As the person this data is derived from, I admit the reality behind this dataset is my own stress, fear, and potential sleeplessness of an emerging global pandemic. Because the dataset is based on the experience of sleeping, any visualization I design should bring this feeling to life.

The flow of feelings that might bring someone into this world of sleep through data might include:

Important here is the slow transition between these bodily states: drifting, resting, and waking. Also key is the cyclical (and aggregate effect) a sleep pattern has.

IV. Relationship

In revealing this snippet of data, I’m exercising agency over my own data. Though, I would imagine someone else handling this data might be sensitive to the intimate kind of information contained within this data. Though visualizing this very slim time frame might not give a hint to any harmful medical condition, there is a personal aspect to understanding how someone rests.

Next, I am fairly aware of my sleep pattern, but it’s still hard to remember how sleeping felt nearly a year ago. I can only use my own experience of resting as a lens for visualizing this data.

Finally, this data visualization might develop a relationship of understanding and/or empathy between the audience and the sleeper (me) — particularly interesting considering it’s context of collection.

V. Manifest

Focusing on one parameter

Restfulness here is key, so I’ll use REM for each night as the parameter I’ll bring to life in a data visualization.

Movement metaphors

Because my data is centered on energy and rest, I might focus on movement as a tangible, visceral way to show this.

Nearly every day this past fall, I noticed paper wasps buzzing around the eaves of my house (out of the window). Apparently paper wasps become more active in feeding in the fall, as they prep for the more dormant winter.

I centered my metaphor around paper wasps searching for food because they provide a tangible, visual display of activity and coordination levels.

Mapping variables

I imagine mapping the parameters of my dataset to the following emergent properties of wasp movement:

Low REM mins / lethargic activity → High REM mins / active

Possible ways to build this would include boids, possibly programming the following mechanics into a generative system/simulation:

  • influence of wasps on one another (wasps can “tell” one another of a food source and flock there together)
  • max distance from which they can detect a food source
  • how fast they move

To embody the cyclical aggregation of rest over days, perhaps these mechanics could be altered day by day, also aggregating. Also, it’s possible that some of these mechanics might change, as I research more about paper wasps and how they might live.

Meaningful thresholds

This Fitbit blog argues that REM totaling around 25% of our sleep is healthy. It might make sense to begin here as a meaningful threshold for my metaphor — this could be the point at which the wasps effectively coordinate and get food effectively. In other words, 25% REM is a “sweet spot” in the system.

Restful Wasps: a data visualization representing how restful my night’s sleep is through wasp movement.

In visualizing daily REM data from Fitbit through movement, this data visualization imagines both as a viable representation of the feeling of restfulness. Paper wasps, native and vital residents of the Blackland Prarie, situate this data visualization in my home state.

Paper wasps are particularly active as they feed during the fall in preparation for winter. In this data visualization, wasps are more active and coordinated in a search for food the more REM minutes of sleep per day. Further, the effect of one day carries through into the following days.



Cathryn Ploehn, MDes

Data viz, computational design, interaction design / Professor at UT Austin / MDes Carnegie Mellon