A Constant Atlas: Designing Cumulative Daily Interactions with Urban Data for Individuals and Their Cities
Dissertation Proposal, MIT Media Arts and Sciences
Ethan Zuckerman, Advisor
Sarah Williams, Reader
Sep Kamvar, Reader
The ability of institutions and businesses to capture and process data aggregated from individuals has grown significantly in the past ten years as we increasingly integrate digital technologies into our daily lives. In the urban planning context, computational social science projects use data collected about the urban environment to solve problems from traffic congestion and public safety, to targeted advertising and the development of entire neighborhoods. Although projects using aggregate data ultimately benefit individuals by making improvements on their environment at large, an individual citizen often does not engage with the data collected from them directly nor the decision making process at large.
The proposed research uses a series of design experiments to engage citizens directly with publicly available data, giving residents the ability to use their physical location over time as a lens to understand aggregate data of their environment. In order for such tools to be effective, they not only have to efficiently communicate data, but also be intuitive enough to encourage repeat use, and cumulatively build personal narratives from the user’s perspective. This research addresses two key questions. The first is: Can we design effective visualization tools for daily interactions between individuals and data about their city? The second question is: How can the potential interactions enabled by the understanding of urban data in the context of daily experiences influence a user’s perception and usage of the city in ways beyond efficiency?
In order to answer these questions, I propose to build the “Constant Atlas” platform for users to dynamically generate unique atlases of publicly available data(from the Census and other sources) based on their movement in the city. These atlases combine interactive visualizations with the principle of self quantification to contextualize a user’s daily behavior within larger datasets. By visualizing users’ movements over time, the atlases provide a site for self reflection. The completed platform uses public data to communicate context to the user about the places they frequent, places that are just beyond their routines, and implications of the self-imposed boundaries etched by their daily movements.