Representations of Personal Data
Darshi Shah
The Final Design
For this assignment, I chose to combine the following two data sets about the cab rides I have taken in the year 2023:
1. The city in which the ride is taken
2. The purpose of the ride
I began by brainstorming some unique datasets that I could use for this experiment. These are a few ideas I came up with:
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Calls I make to India (family and friends)
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The number of times I think in Gujarati (my mother tongue)
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The number of internship applications I submit
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The different food I cook
I then realised that I had visited eight cities (both in India and the US) in the year 2023 and taken cabs in almost all of them (I have family in Ahmedabad, a city in India where I didn't have to take any cabs). I decided to represent this data and correlate it to the reason behind taking the cab.
Initially, I thought of mapping my trips on a map, but the map started looking very cluttered as almost all my trips began from one location. Be it my home in Pune, my apartment in Atlanta, or my sister's house in San Francisco. Some of the trips were also along the same path.
This is when I thought of using the maps of the cities and placing cars in different directions indicating whether I was going from home/a hotel to another location or vice versa. But there were instances where I took a cab from and to places that were not home. This looked very cluttered so I thought of simplifying my design further.
I also briefly thought of including the time of day the cab was taken. But sometimes cabs were taken in the morning, evening and afternoon in the same day.
I used records from Lyft, Ola (a leading ride-sharing app in India) and Uber(both in India and the US) to collect data on my rides.
I created a google sheet and manually entered data into it. I had to look into my email for records from Uber India as my current app only showed records from the US. I was able to get info from the Ola app. Lyft let me export my ride details directly in .csv and .pdf format.
I created a google sheet and manually entered data into it. I had to look into my email for records from Uber India as my current app only showed records from the US. I was able to get info from the Ola app. Lyft let me export my ride details directly in .csv and .pdf format.
Next, I started putting this data onto a Figma file. I assigned different shapes to the purpose of the visit and different colours to the city.
I started placing the shapes under the corresponding months the rides were taken in.
After I finished placing all the shapes, I adjusted the spacing and grid to make them look neat.
I decided to convert the data into a design that can be printed on tshirts. I usually like wearing minimalistic prints and the colorful, bright design made me want to put it on a t-shirt that I could wear.
This design still looked scattered, so I iterated on the design to make it look more like a t-shirt design.
By doing so, I subverted some of the best practices recommended for Data Visualisation.
1. Employing Clear Labels
To adapt the design for a t-shirt, I removed all the month labels to enhance its suitability.
2. Using Clear Separators for Data
I removed the spaces that previously separated the rides for different months, aiming to distinctly delineate the data.
Here is a render of how the deisgn would look like on a tshirt: