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dc.contributor.advisorTakehiko Nagakura.en_US
dc.contributor.authorVillalon, Rachelle B. (Rachelle Bentajado)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Architecture.en_US
dc.date.accessioned2018-05-17T19:06:53Z
dc.date.available2018-05-17T19:06:53Z
dc.date.copyright2016en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/115448
dc.descriptionThesis: Ph. D. in Architecture Design and Computation, Massachusetts Institute of Technology, Department of Architecture, June 2017.en_US
dc.descriptionCataloged from PDF version of thesis. "February 2017."en_US
dc.descriptionIncludes bibliographical references (pages 190-194).en_US
dc.description.abstractWhat can campus WiFi data tell us about life at MIT? What can thousands of images tell us about the way people see and occupy buildings in real-time? What can we learn about the buildings that millions of people snap pictures of and text about over time? Crowdsourcing has triggered a dramatic shift in the traditional forms of producing content. The increasing number of people contributing to the Internet has created big data that has the potential to 1) enhance the traditional forms of spatial information that the design and engineering fields are typically accustomed to; 2) yield further insights about a place or building from discovering relationships between the datasets. In this research, I explore how the Architecture, Engineering, and Construction (AEC) industry can exploit crowdsourced and non-traditional datasets. I describe its possible roles for the following constituents: historian, designer/city administrator, and facilities manager - roles that engage with a building's information in the past, present, and future with different goals. As part of this research, I have developed a complete software pipeline for data mining, analyzing, and visualizing large volumes of crowdsourced unstructured content about MIT and other locations from images, campus WiFi access points, and text in batch/real-time using computer vision, machine learning, and statistical modeling techniques. The software pipeline is used for exploring meaningful statistical patterns from the processed data.en_US
dc.description.statementofresponsibilityby Rachelle B. Villalon.en_US
dc.format.extent194 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectArchitecture.en_US
dc.titleData mining, inference, and predictive analytics for the built environment with images, text, and WiFi dataen_US
dc.typeThesisen_US
dc.description.degreePh. D. in Architecture Design and Computationen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Architecture.en_US
dc.identifier.oclc1035374549en_US


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