Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01k643b130h
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dc.contributor.authorPalmer, John RBen_US
dc.contributor.otherPublic and International Affairs Departmenten_US
dc.date.accessioned2013-12-06T14:15:54Z-
dc.date.available2013-12-06T14:15:54Z-
dc.date.issued2013en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01k643b130h-
dc.description.abstractThis dissertation offers a new theoretical and methodological framework for understanding segregation in spatio-temporal terms as a separation of activity spaces, the spaces people move through as they go about their daily activities. The framework is a set of indexes with which multiple dimensions of activity-space segregation can be measured and compared across cities. The indexes are designed to capture socially relevant information about differences in the places people frequent, the people with whom they come into contact, and the nature of their movement. In designing the indexes, the dissertation extends the existing areal unit indexes of residential segregation, draws on an early formulation of White's spatial proximity index, and identifies existing measures of individual activity space from the geography and ecology literature. For each index, the dissertation develops an estimator that may be used to draw inferences from sample data, and it evaluates the performance of the estimator at different sample sizes and under different geographic and demographic conditions. To do this, it relies on a combination of high resolution activity-space trajectories collected from volunteers all over the world through a mobile phone application, and simulated trajectories of the full populations of two U.S. cities. The dissertation concludes that the proposed areal unit measures of activity-space segregation may be estimated with minimal bias using coarse trajectory data but require large samples of people and the implementation of a bootstrap bias correction technique. The proposed extension of White's spatial proximity index, on the other hand, may be estimated without bias using coarse trajectory data from relatively small samples of people. The proposed measures of individual activity spaces require high resolution trajectory data, but may be estimated without bias using relatively small samples of people.en_US
dc.language.isoenen_US
dc.publisherPrinceton, NJ : Princeton Universityen_US
dc.relation.isformatofThe Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the <a href=http://catalog.princeton.edu> library's main catalog </a>en_US
dc.subjectactivity spaceen_US
dc.subjectdemographyen_US
dc.subjectimmigrationen_US
dc.subjectinequalityen_US
dc.subjectraceen_US
dc.subjectsegregationen_US
dc.subject.classificationDemographyen_US
dc.subject.classificationSociologyen_US
dc.subject.classificationPublic policyen_US
dc.titleActivity-Space Segregation: Understanding Social Divisions in Space and Timeen_US
pu.projectgrantnumber690-2143en_US
Appears in Collections:Public and International Affairs

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