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dc.contributor.advisorAndrea Chegut.en_US
dc.contributor.authorRoyall, Emily Bineten_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Urban Studies and Planning.en_US
dc.date.accessioned2016-06-22T17:53:46Z
dc.date.available2016-06-22T17:53:46Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/103263
dc.descriptionThesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2016.en_US
dc.descriptionVita. Cataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractGentrification is viewed as both as a tool and a force--as a systematized vehicle for classbased oppression and racism, and an empirical force of change based on social, environmental and economic interactions. This complexity makes it challenging for researchers to study the impact of gentrification, for planners to anticipate the effects of gentrification with planning policy, and for developers to foresee investment outcomes. Current planning policy addresses the symptoms of gentrification, without defining the underlying construct of the process. This thesis examines latent constructs of gentrification through a data-driven process that identifies emergent states of change and assigns them to a Markov process, i.e. a process that assigns probabilities to potential "state" changes over time. For census block groups in four boroughs of New York City, this model takes three steps: 1) cluster census block groups into latent states defined by ACS socioeconomic and demographic data, 2) derive a Markov model by tracking transitions between states over time, and 3) validate the model by testing predictions against historic data and qualitative documentation. Using this process I was able to find emergent typologies of urban change, locate gentrifying neighborhoods without any spatial input, and uncover sequences of patterns that reliably predict socioeconomic outcomes at the census block group level. Through the design of a machine learning framework for gentrification I reflect on the importance of using algorithms that learn rather than reproduce assumptions, value of distilling large and complex data relationships into nuanced intuitions, and challenges of embedding computational modeling in political frameworks.en_US
dc.description.statementofresponsibilityby Emily Binet Royall.en_US
dc.format.extent160 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.titleTowards an epidemiology of gentrification : modeling urban change as a probabilistic process using k-means clustering and Markov modelsen_US
dc.title.alternativeModeling urban change as a probabilistic process using k-means clustering and Markov modelsen_US
dc.typeThesisen_US
dc.description.degreeM.C.P.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planning.en_US
dc.identifier.oclc951680817en_US


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