For the visualization assignment, I chose to work with NYC Citibike data. The data (so far) is presented as 123 separate csv files spanning 10 years. Some interesting observations while processing the data:
From June 2013 – January 2021, Citi Bike data is stored in the following format:
'tripduration','starttime','stoptime','start station id','start station name','start station latitude','start station longitude','end station id','end station name','end station latitude','end station longitude','bikeid','usertype','birth year','gender'
From February 2021 – July 2023, Citi Bike data is stored in the following format:
ride_id,rideable_type,started_at,ended_at,start_station_name,start_station_id,end_station_name,end_station_id,start_lat,start_lng,end_lat,end_lng,member_casual
Post February 2021, gender, bikeid, and birthyear are no longer stored. Instead rideable_type and ride_id are introduced. The former stores the type of bicycle (electric, classic, docked) while the latter is a unique hash sequence. Unlike bikeid, which can be tracked across records, ride_id is a blackbox that is only useful as an index.
I used Plotly to create the visualizations and MapBox for the maps.
While some of the visualizations were interesting, the way the data was collected, structured, and presented limited what could be visualized.
Data then is an expression of those systems and worldviews and thus must be recognized as intrinsically tied to how the community defines itself.
Cultural Analytics
Jennifer Guiliano and Carolyn Heitman
The data captured is more of a reflection of preferences and priorities of the Citibike system than a true representation of experience. Many of the factors we would consider part of the experience of using a Citibike (let alone biking in NYC) were unaccounted for.
Throughout the process of creating these visualizations, there were multiple levels of abstraction in play. The sheer volume of data, the structure of the dataset, and the resulting visualizations. Each layer was another step removed from experience.
In an effort to make the data more personal, I used the bikeid datapoint to track starting and ending stations over the course of a year. However, much like the bike itself, the data (and visualizations) are tied to the system within which it operates.
Using Citi Bike data is such a cool idea! I think you’re idea that “The data captured is more of a reflection of preferences and priorities of the Citi Bike system than a true representation of experience” is particularly interesting, and I’m wondering and less graphically primitive forms of visualization could convey the experience of using Citi Bikes.
When they’re great, they’re are great, but there I’ve also had so may frustrating experiences with them (e.g.) when they are broken difficult to dock, or uncharged.) Incorporating humanistic methods, as Drucker says, “using graphical methods to convey interpreted phenomena” might be a cool way capture personal accounts of what it’s like to use then.