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dc.contributor.advisorJoseph Ferreira, Jr.en_US
dc.contributor.authorJiang, Shan, Ph. D. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Urban Studies and Planning.en_US
dc.date.accessioned2016-02-29T15:03:12Z
dc.date.available2016-02-29T15:03:12Z
dc.date.copyright2015en_US
dc.date.issued2015en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/101369
dc.descriptionThesis: Ph. D. in Urban and Regional Planning, Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2015.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 187-200).en_US
dc.description.abstract"Big Data" is in vogue, and the explosion of urban sensors, mobile phone traces, and other windows onto urban activities has generated much hype about the advent of a new 'urban science.' However, translating such Big Data into a planning-relevant understanding of activity patterns and travel behavior presents a number of obstacles. This dissertation examines some of these obstacles and develops data processing pipelines and urban activity modeling techniques that can complement traditional travel surveys and facilitate the development of richer models of activity patterns and land use-transportation interactions. This study develops methods and tests their usefulness by using Singapore metropolitan area as an example, and employing data mining and statistical learning methods to distill useful spatiotemporal information on human activities by people and by place from traditional travel survey data, semantically enriched GIS data, massive and passive call detail records (CDR) data, and Wi-Fi augmented mobile positioning data. I illustrate that regularity and heterogeneity exist in individuals' daily activity patterns in the metropolitan area. I test the hypothesis that by characterizing and clustering individuals' activity profiles, and incorporating them into household decision choice models, we can characterize household lifestyles in ways that enhance our understanding and enable us to predict important decision-making processes within the urban system. I also demonstrate ways of integrating Big Data with traditional data sources in order to identify human mobility patterns, urban structures, and semantic themes of places reflected by human activities. Finally, I discuss how the enriched understanding about cities, human mobility, activity, and behavior choices derived from Big Data can make a difference in land use planning, urban growth management, and transportation policies.en_US
dc.description.statementofresponsibilityby Shan Jiang.en_US
dc.format.extent200 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectUrban Studies and Planning.en_US
dc.titleDeciphering human activities in complex urban systems : mining big data for sustainable urban futureen_US
dc.title.alternativeMining big data for sustainable urban futureen_US
dc.typeThesisen_US
dc.description.degreePh. D. in Urban and Regional Planningen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planning.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planning
dc.identifier.oclc939629559en_US


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