Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Are Houston's Land Use Relationships Unique?(2021) Dorney, Christopher Leh; Knaap, Gerrit J; Urban and Regional Planning and Design; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The city of Houston, Texas has been at the heart of a long-running debate in the United States on government’s proper role in the land development process. As the only large American city that never adopted a city-wide zoning ordinance, Houston is often cited as an example for why more or less government planning is needed. Some authors claim that Houston is an outlier when it comes to land use relationships, with strange land use juxtapositions quite prevalent. Other authors argue that zoning is largely redundant to market forces and that Houston’s land use relationships are not all that different from zoned cities. The purpose of this study is to inform this ongoing debate by undertaking a quantitative analysis of land use relationships across large American cities to determine if Houston’s are distinctive. The study develops several metrics to quantify land use relationships and uses principal component analysis to determine if Houston is an outlier. The findings indicate that Houston’s land use relationships are not substantially different from those of zoned cities.Item USING SOCIALLY SENSED BIG DATA TO MODEL PATTERNS AND GEOGRAPHIC CONTEXT OF HUMAN ACTIVITIES IN CITIES(2018) Fu, Cheng; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Understanding dynamic interactions between human activities and land-use structure in a city is a key lens to explore the city as a complex system. This dissertation contributes to understanding the complexity of urban dynamics by gaining knowledge of the interactions between human activities and city land-use structures by utilizing free-accessible socially sensed data sources, and building upon recent research trend and technologies in geographical information science, urban study, and computer science. This dissertation addresses three main questions related to human dynamics: 1) how human activities in an urban environment are shaped by socioeconomic status and the intra-city land-use structure, and how in turn, the knowledge of socioeconomic status-activity relationships can contribute to understanding the social landscape of a city; 2) how different types of activities are located in space and time in three U.S. cities and how the spatiotemporal activity patterns in these cities characterize the activity profile of different neighborhoods in the cities; and 3) how recent socially sensed information on human activities can be integrated with widely-used remotely sensed geographical data to create a novel approach for discovering patterns of land use in cities that are otherwise lacking in up to date land use information. This dissertation models the associations between socioeconomics and mobility in the Washington, D.C. metropolitan area as a case study and applies the learned associations for inferring geographical patterns of socioeconomic status (SES) solely using the socially sensed data. This dissertation also implements a semi-automated workflow to retrieve activity details from socially sensed Twitter data in Washington, D.C., the City of Baltimore, and New York City. The dissertation integrates remotely-sensed imagery and socially sensed data to model the dynamics associated with changing land-use types in the Washington, D.C.-Baltimore metropolitan area over time.