Walk Deserts

This thesis describes a new methodology to identify, measure, and understand “Walk Deserts.” This methodology comprises a system for identifying, mapping, and visualizing areas that are ostensibly highly walkable places according to traditional criteria and indicators, but that are also plagued with invisible environmental factors that impede walkability and threaten public health. This research has two principal aims: (1) to better understand and begin to address blind spots in walkability indicators as well as perception-based measurements (that are dicult to quantify and subject to bias), and (2) to present a greater range of environmental data associated with walkability and negative health outcomes in publicly accessible ways in order to facilitate community engagement. Two key contributions emerged from this research: (1) a theoretical re-definition of the concept of “walk deserts” to highlight typically overlooked aspects of walkability, and (2) a creative and technical contribution that focuses on finding “walkable deserts in the City” and visualizing these deserts in immersive ways. Boston’s Chinatown district serves as a case study site, a “walk desert” hidden in plain sight. With the presence of greenways surrounded by highways, it appears to be a seemingly walkable and even heavily touristed neighborhood with dramatically poor health outcomes. Digital photogrammetry is used to explore how merging photorealistic, three-dimensional spatial models with environmental data can produce immersive and interactive data visualizations, including a web application, an augmented reality interface, and an interactive installation. These interfaces expose the “walk desert” hidden in Chinatown, and provide a mechanism to engage members of the community, as well as researchers and policy-makers, in the process of transforming degraded urban spaces into healthier and move vibrant ones.

Name thesis:degree_name

Master

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

2022

Author dc:creator

• Blinder, Justin

Advisor dc:contributor.advisor

• Larson, Kent

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151974

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151974