The Constant Atlas: Mapping Public Data for Individuals and Their Cities

Committee

Ethan Zuckerman
Thesis Supervisor
Associate Professor of the Practice in Media Arts and Sciences
Director of the Center for Civic Media

Sarah Williams
Member, Thesis Committee
Associate Professor of Technology and Urban Planning
Director of the Civic Data Design Lab

Sepandar Kamvar
Member, Thesis Committee

Over the past ten years the ability of institutions and businesses to capture, aggregate, and process individual data has grown significantly as digital technology has increasingly integrated into our daily lives. In the urban informatics context and in computational social science, projects use data collected about our behavior in the urban environment to solve problems from traffic congestion and public safety, to the creation of targeted advertising and the development of entire neighborhoods. Although some projects using aggregate data may ultimately benefit individuals by making improvements to their environment at large, an individual citizen often does not directly engage with the data collected about them.

The research contained in this dissertation explores a series of visualization experiments concerning direct engagement between citizens and public datasets such as the U.S.Census. In order for such visualizations to be effective, they not only have to efficiently communicate data, but must also be intuitive, evocative, and utilize narratives presented from the user’s perspective. In this dissertation I address the question: How can we design visualizations which inform daily interaction between individuals and public data about their environment, in ways that extend beyond efficiency?

To answer this question, the dissertation introduces 4 sets of maps: (1) the “Powers” map which contextualizes Census data by invoking dramatic changes in scale, (2) the “Sightline” and “Cross Section” map which use a person’s physical experiences to orient Census data, (3) the “Filtered Satellite” maps which gives qualitative comparisons of conditions described by Census tables, and (4) the “Personal History” map which leverages a individual’s geospatial history to filter Census data. These 4 map groups share the goal of allowing us, as individuals, to use public data to design our own experiences within our environments and to make use of public data directly on our own behalf.