Architectural epidemiology : a computational framework

Architecture affects our health, especially in hospitals. However, our ability to learn from existing hospitals to design buildings that improve patient outcomes is limited. If we want to leverage large datasets of health outcomes to build knowledge about how architecture affects health, then we need new methods for analyzing spatial data and health data jointly. In this thesis, I present several steps toward the goal of developing a computational model of architectural epidemiology that aims to leverage both human and machine intelligence to do so. First, I outline the need for structured architectural datasets that capture spatial information in schemas that current drawing formats do not allow. These datasets need to be wide to capture multifaceted and qualitative aspects of the built environment, and so we need new methods to generate this data. Finally, we need strategies for surfacing insight from these datasets by involving both humans and machines in the process.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Architecture

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Peraino, Jim.

Advisor dc:contributor.advisor

• Takehiko Nagakura.

• Architecture.
• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127877

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127877