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- Creators: Mack, Elizabeth
- Creators: Barrett, The Honors College
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
Partial differential equation (PDE) models are widely used for modeling processes in the physical sciences, economics, and sociology, but are otherwise new to the realm of social media. They allow researchers to construct a single spatiotemporal mathematical model to predict, in the case of this study, the level of information saturation at particular points in space at specific times. Utilizing data from the popular social network Twitter, this study presents a preliminary work looking into the effects of aggregating spatial data on such a PDE model. In other literature, the source of analytical and statistical bias that results from arbitrary spatial aggregation is known as the modifiable areal unit problem (MAUP). We use a previously-studied dataset from the 2011 Egyptian revolution for simulation, and group data points using several distance metrics based on geographical location and geo-cultural similarity. This paper will attempt to show that a PDE model, necessarily dependent upon aggregating data, is subject to significant bias when said data are arbitrarily organized and grouped for simulation. We look primarily into the zoning problem, which amounts to maintaining a fixed number of regions located in different areas across the globe, but make note of the scale problem, an inherent issue in PDE modeling that results from aggregating data points into increasingly larger regions. From looking at specific values from each simulation, this study shows that such a model is not free from the MAUP and that consideration of how data are aggregated needs to be made for future studies. In addition, it also suggests that geo-political and geo-cultural spatial metrics generate better diffusive patterns for tweet propagation than do simple geographical proximity metrics.
ContributorsRaymond, Ross Edward Scott (Author) / Kwon, Kyounghee Hazel (Thesis director) / Gruber, Diane (Committee member) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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