The constant atlas : mapping public data for individuals and their cities

Over the past ten years the ability of institutions and businesses to capture, aggregate, and process an individual's 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 including traffic congestion and public safety, the creation of targeted advertising, and the development of entire neighborhoods. Some projects using aggregate data may ultimately benefit individuals by making improvements to their environment at large. Although individuals are the source of aggregate information, 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? To answer this question, the dissertation introduces 4 sets of maps: (1) the Powers Map and Scopes Map contextualizes Census data(American Community Survey) by invoking changes in scale, (2) the Sightline Map and Cross Section Map use a person's physical experiences to orient Census data, (3) the Filtered Satellite Maps give qualitative comparisons of conditions described by Census tables, and (4) the Personal History Map leverages an 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.

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Zhang, Jia, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Ethan Zuckerman.

• Program in Media Arts and Sciences ()

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119075

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119075