Event Date: 
05.11.20

Final Public Oral Exam of Mauricio Loyola

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Princeton University School of Architecture

Announces the Final Public Oral Exam of

Mauricio Loyola

 

“A COMPUTATIONAL METHOD FOR QUANTITATIVE POST OCCUPANCY
EVALUATION OF OCCUPANTS’ SPATIAL BEHAVIOR IN BUILDINGS”

 

Monday, May 11, 2020, 8:00 a.m.

Hosted on Zoom

 

Committee

Forrest Meggers (Princeton University, Advisor)

Axel Kilian (Massachusetts Institute of Technology)

Stefana Parascho (Princeton University)

Clayton Miller (National University of Singapore)

 

ABSTRACT:

This dissertation proposes a novel computational method for observing and quantitively describing the spatial behavior of building occupants to be used as a complement to qualitative techniques in post-occupancy evaluations.

The main elements of the proposed method are, first, a comprehensive computational assessment framework of behavioral variables and metrics that describe a wide variety of patterns of presence, movement, and spatial activity; and second, a computer vision algorithm that captures anonymous, high-resolution spatiotemporal data in a more efficient and accurate manner than comparable benchmarks. The proposed method is conceptually grounded in an architecture-oriented redefinition of the notion of spatial behavior, and in a thorough analysis of the limitations of current post-occupancy evaluation protocols.

The method was evaluated and validated in a series of case studies of post-occupancy evaluations of occupants’ spatial behavior in different university buildings. The results demonstrated the robustness, convenience, and applicability of the method, even in challenging situations of complex spatial configurations and privacy-sensitive environments. The method is simple to implement, flexible to adapt to multiple contexts, and cost-efficient, making it potentially competitive for scalable massive applications.

At a more general level, this dissertation offers a broader discussion of the need to use computational methods to enhance the flow of information from occupation to design, and ultimately, develop a data-driven culture in the building industry.

 

Electronic version of the dissertation is available upon request: soaprograms@princeton.edu

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