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dc.contributor.advisorAndrea Chegut.en_US
dc.contributor.authorHoltzman, Elisabeth Phoebe Kamineen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Urban Studies and Planning.en_US
dc.coverage.spatialn-us-nyen_US
dc.date.accessioned2018-09-28T20:58:30Z
dc.date.available2018-09-28T20:58:30Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/118245
dc.descriptionThesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 103-105).en_US
dc.description.abstractNeighborhoods are complex and dynamic. An attempt to tease out how and why neighborhoods change requires interdisciplinary study that reflects the layers of interrelated people, places and things that make up an urban neighborhood. Urban data science aims to measure neighborhood change, yet it is challenging to quantify how a place changes over time, space and people. Moreover, these measures are important because planning and economic development policy that relies on these measures impacts future place-making and community development. To understand neighborhood change at a granular scale that can be useful to decision makers, I conduct a data-driven ethnography in which I assemble, analyze, and integrate over 45 urban planning and real estate datasets to develop quantitative metrics that measure the rate of change for the 1817 to 2017 period for Block 800 in New York City. Quantitatively, long-run metrics on rates of neighborhood change were previously unable to identify. In this way, I was able to document that change is always happening to a building, property, person or price, but its positive and or negative trends are often very slow to articulate in datasets or statistical models. The quantitative results suggest that, on average, buildings move slowly by netting 0.01 buildings per annum over the 1817 to 2017 period, properties more rapidly at 0.45 per annum and people even more rapidly at a projected rate of 1400 people per annum. In addition, not all changes are equal in speed or impact, where change can accelerate at so-called inflection points where technological progress in society is meeting the built environment and the people operating within. At these points, the speed of a neighborhood can increase rapidly causing displacement and gentrification and at other times, progress is absent with long periods of decay. Importantly, calculating rates of change could not be done without data-driven ethnographic methods that allows for integrating and not aggregating data. Integrated place data are intricately linked to retell a long, wide, and big data neighborhood story. These methods can now be replicated at a larger scale with the proliferation of city science to drive decision-making in cities at new scales.en_US
dc.description.statementofresponsibilityby Elisabeth Phoebe Kamine Holtzman.en_US
dc.format.extent105 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.subjectUrban Studies and Planning.en_US
dc.titleDeconstructing places : a data-driven ethnography of neighborhood change for one New York city blocken_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.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planning
dc.identifier.oclc1054103890en_US


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