Matching Items (3)
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- All Subjects: social equity
- All Subjects: South Phoenix
- All Subjects: Spatiotemporal
- Creators: Mack, Elizabeth
Description
Research literature were reviewed regarding the land-use economic theory of bid-rent curves and the modern emergence of polycentric cities. Two independent Geographic Information System (GIS) analyses were completed to test the hypothesis that bid-rent methodology could be used to tease out trends in residential locations, and hence contribute to present-day urban planning efforts. Specifically, these analyses sought to address the relationships between place of work and place of residence in urban areas. A generalizable set of benchmarks for identifying urban employment centers were established for 10 study cities in the United States, and bid-rent curves were calculated under separate monocentric assumptions and polycentric assumptions. The results presented wide variations in real bid-rent curves that a) overall deviated dramatically from the hypothetical distribution of rent, and b) spoke to the unique residential patterns in individual U.S. cities. The implications of these variations were discussed with regard to equitable housing for marginalized groups and access to centers of employment.
ContributorsBochnovic, Michael Andrew (Author) / Mack, Elizabeth (Thesis advisor) / Pfeiffer, Deirdre (Committee member) / Rey, Sergio J (Committee member) / Arizona State University (Publisher)
Created2014
Description
A 1969 report identified South Phoenix as a community that simply needed to "strengthen residential identity" (City of Phoenix), but the decades of blight and decline that followed led to the eventual adoption of the South Phoenix Village Redevelopment Area Plan in 1989. The plan recognized that twelve block area five miles southeast of the heart of Phoenix needed comprehensive revitalization. Many of the programs implemented by the City over the 20 years have been successful, but the plan has not been reevaluated in more than a decade. This research seeks to compile information as a proxy for an update on the current state of South Phoenix Village with a goal of ascertaining whether a comprehensive plan continues to be the best revitalization tool for the neighborhood. Using census tract-level data, housing, social, and economic characteristics were analyzed in an effort to identify the barriers to success that South Phoenix faces as the area continues to be rehabilitated. Some issues that have been rampant in the 1980s continues to plague the area, but others seem to have been mitigated. Following analysis of the data in the context of residential stability and neighborhood health, conclusions concerning the advantages and limitations of the comprehensive plan approach for South Phoenix Village were drawn, and recommendations for future initiatives in the area were made.
ContributorsAagard, Sarah (Author) / Dantico, Marilyn (Thesis director) / Lewis, Paul (Committee member) / Mack, Elizabeth (Committee member) / Barrett, The Honors College (Contributor)
Created2012-05
Description
Traditionally, visualization is one of the most important and commonly used methods of generating insight into large scale data. Particularly for spatiotemporal data, the translation of such data into a visual form allows users to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena that would not be apparent in tabular form. However, several critical challenges arise when visualizing and exploring these large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex. To simplify the visual representations, analytical methods such as clustering and feature extraction are often applied as part of the classification phase. The automatic classification can then be rendered onto a map; however, one common issue in data classification is that items near a classification boundary are often mislabeled.
This thesis explores methods to augment the automated spatial classification by utilizing interactive machine learning as part of the cluster creation step. First, this thesis explores the design space for spatiotemporal analysis through the development of a comprehensive data wrangling and exploratory data analysis platform. Second, this system is augmented with a novel method for evaluating the visual impact of edge cases for multivariate geographic projections. Finally, system features and functionality are demonstrated through a series of case studies, with key features including similarity analysis, multivariate clustering, and novel visual support for cluster comparison.
This thesis explores methods to augment the automated spatial classification by utilizing interactive machine learning as part of the cluster creation step. First, this thesis explores the design space for spatiotemporal analysis through the development of a comprehensive data wrangling and exploratory data analysis platform. Second, this system is augmented with a novel method for evaluating the visual impact of edge cases for multivariate geographic projections. Finally, system features and functionality are demonstrated through a series of case studies, with key features including similarity analysis, multivariate clustering, and novel visual support for cluster comparison.
ContributorsZhang, Yifan (Author) / Maciejewski, Ross (Thesis advisor) / Mack, Elizabeth (Committee member) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2016