(Georgia Institute of Technology, 2013-02-28)
Scoppa, Martin Dennis
This dissertation tests the proposition that the spatial structure of street networks affects the distribution of urban land use. Specifically, it examines patterns of commercial land use utilizing parcel based data on retail and service businesses location. While previous studies report a correlation between spatial structure and patterns of commercial land use, these studies do not typically control for the effect of key variables likely to contribute to the spatial distribution of retail and service establishments. In order to redress this balance, and using the City of Buenos Aires as a case study, this dissertation studies the correlation between commercial land use frontage and street connectivity measures, while controlling for street widths, density of population and employment, interstore externalities, zoning regulations, and distance to transit stations. Buenos Aires is chosen for its regular plan radiating from a well-defined CBD, a plan which would be expected to conform to standard urban attraction models of retail location. Results of multiple regression models indicate that, after controlling for these variables, measures of street connectivity account for key aspects of the distribution of retail, including linear distributions along major radial and peripheral streets at a distance from the CBD. Thus, the dissertation supports the thesis that "urban attraction" should not be conceptualized in terms of distances from a unique central location, or a number of central locations, but rather in terms of a model of distributed centrality governed by the structure of street networks.
(Georgia Institute of Technology, 2009-10-21)
Sahar, Liora
Extraction of buildings from remote sensing sources is an important GIS application and has been the subject of extensive research over the last three decades. An accurate building inventory is required for applications such as GIS database maintenance and revision; impervious surfaces mapping; storm water management; hazard mitigation and risk assessment. Despite all the progress within the fields of photogrammetry and image processing, the problem of automated feature extraction is still unresolved.
A methodology for automatic building extraction that integrates remote sensing sources and GIS data was proposed. The methodology consists of a series of image processing and spatial analysis techniques. It incorporates initial simplification procedure and multiple feature analysis components. The extraction process was implemented and tested on three distinct types of buildings including commercial, residential and high-rise. Aerial imagery and GIS data from Shelby County, Tennessee were identified for the testing and validation of the results. The contribution of each component to the overall methodology was quantitatively evaluated as relates to each type of building. The automatic process was compared to manual building extraction and provided means to alleviate the manual procedure effort.
A separate module was implemented to identify the 2D shape of a building. Indices for two specific shapes were developed based on the moment theory. The indices were tested and evaluated on multiple feature segments and proved to be successful.
The research identifies the successful building extraction scenarios as well as the challenges, difficulties and drawbacks of the process. Recommendations are provided based on the testing and evaluation for future extraction projects.