Assignment #1, link to assignment in syllabus
For my visualization critique, I'm writing about the MIT Senseable City Lab's HubCab project in which the project team created an interactive data visualization that allows users to visualize 170 million taxi trips in NYC. The key value that this web based interactive visualization offers is that it communicates how many trips could be saved in New York City if people were willing to go a few minutes out of their way to share a taxi cab (sounds a lot like Uber Pool and Lyft's ride sharing service)
The visualization is effective because allows people to see both to and from where people have traveled from and the number of people that could have shared that same exact trip. By seeing exactly how many people are traveling to and from the exact places I would travel in NYC as well as how much CO2 I might save, for example, it makes me conscious about the types of positive benefits that ride sharing might bring to improving urban life - e.g. reducing traffic and congestion, reducing environmental and health impacts of vehicle travel, and more.
This visualization project is map based and uses colored dots to indicate a taxi pick up or drop off. At higher zoom levels, the street segments are sliced into 50m segments that are colored yellow (pick up) or blue (drop off) depending on the ratio of whether more pick ups or drop offs occured at that particular location. Of course this can be problematic because these ratios are time dependent - sometimes there may be more pick ups than drop offs and vice versa - but it is still and effective way of communicating and overview of the dataset. When zooming in to lower levels, the colored dots appear; they are randomly placed within the 50m road segment.
The primary mode of interaction with the piece is that a user can place markers on the map for starting and ending location and visualize scaled arrows showing the number of how many people have traveled between those two locations. Users can also change the temporal scale which affects the view for how many people traveled between the pick up and drop off location; the map is unaffected.
The data from this visualization comes from the New York Medallion Cab service and analyzed by the Senseable City Lab team. Over one trillion different combinations of pickup and drop offs were precomputed to make this visualization possible.
The visualization was published in 2013 at and around the time when ride sharing services were beginning to emerge as major players in the sharing economy for mobility in cities. As we've seen the potential for ride sharing has proven successful, but still there are many unanswered questions about the actual benefits of these services, especially for reducing road congestion and emissions reductions. Furthermore, the social impacts of these services is also to be determined. What starts to happen when mobility services can begin to exclude or restrict access to certain groups of people? How are private industry politics and motivations involved in shaping the types of clientele are able to access their services? and how do these differ from the roles that state or federally sponsored mobility options operate in terms of accessibility or policing? Also what kinds of rights do be relinquish or gain when using private services versus those provided by the local transport authorities?
- MIT Senseable City Lab Hubcab project: http://hubcab.org/