ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Statistics
- Creators: Rey, Sergio J
Description
Gerrymandering is a central problem for many representative democracies. Formally, gerrymandering is the manipulation of spatial boundaries to provide political advantage to a particular group (Warf, 2006). The term often refers to political district design, where the boundaries of political districts are “unnaturally” manipulated by redistricting officials to generate durable advantages for one group or party. Since free and fair elections are possibly the critical part of representative democracy, it is important for this cresting tide to have scientifically validated tools. This dissertation supports a current wave of reform by developing a general inferential technique to “localize” inferential bias measures, generating a new type of district-level score. The new method relies on the statistical intuition behind jackknife methods to construct relative local indicators. I find that existing statewide indicators of partisan bias can be localized using this technique, providing an estimate of how strongly a district impacts statewide partisan bias over an entire decade. When compared to measures of shape compactness (a common gerrymandering detection statistic), I find that weirdly-shaped districts have no consistent relationship with impact in many states during the 2000 and 2010 redistricting plan. To ensure that this work is valid, I examine existing seats-votes modeling strategies and develop a novel method for constructing seats-votes curves. I find that, while the empirical structure of electoral swing shows significant spatial dependence (even in the face of spatial heterogeneity), existing seats-votes specifications are more robust than anticipated to spatial dependence. Centrally, this dissertation contributes to the much larger social aim to resist electoral manipulation: that individuals & organizations suffer no undue burden on political access from partisan gerrymandering.
ContributorsWolf, Levi (Author) / Rey, Sergio J (Thesis advisor) / Anselin, Luc (Committee member) / Fotheringham, A. Stewart (Committee member) / Tam Cho, Wendy K (Committee member) / Arizona State University (Publisher)
Created2017
Description
Urban growth, from regional sprawl to global urbanization, is the most rapid, drastic, and irreversible form of human modification to the natural environment. Extensive land cover modifications during urban growth have altered the local energy balance, causing the city warmer than its surrounding rural environment, a phenomenon known as an urban heat island (UHI). How are the seasonal and diurnal surface temperatures related to the land surface characteristics, and what land cover types and/or patterns are desirable for ameliorating climate in a fast growing desert city? This dissertation scrutinizes these questions and seeks to address them using a combination of satellite remote sensing, geographical information science, and spatial statistical modeling techniques.
This dissertation includes two main parts. The first part proposes to employ the continuous, pixel-based landscape gradient models in comparison to the discrete, patch-based mosaic models and evaluates model efficiency in two empirical contexts: urban landscape pattern mapping and land cover dynamics monitoring. The second part formalizes a novel statistical model called spatially filtered ridge regression (SFRR) that ensures accurate and stable statistical estimation despite the existence of multicollinearity and the inherent spatial effect.
Results highlight the strong potential of local indicators of spatial dependence in landscape pattern mapping across various geographical scales. This is based on evidence from a sequence of exploratory comparative analyses and a time series study of land cover dynamics over Phoenix, AZ. The newly proposed SFRR method is capable of producing reliable estimates when analyzing statistical relationships involving geographic data and highly correlated predictor variables. An empirical application of the SFRR over Phoenix suggests that urban cooling can be achieved not only by altering the land cover abundance, but also by optimizing the spatial arrangements of urban land cover features. Considering the limited water supply, rapid urban expansion, and the continuously warming climate, judicious design and planning of urban land cover features is of increasing importance for conserving resources and enhancing quality of life.
This dissertation includes two main parts. The first part proposes to employ the continuous, pixel-based landscape gradient models in comparison to the discrete, patch-based mosaic models and evaluates model efficiency in two empirical contexts: urban landscape pattern mapping and land cover dynamics monitoring. The second part formalizes a novel statistical model called spatially filtered ridge regression (SFRR) that ensures accurate and stable statistical estimation despite the existence of multicollinearity and the inherent spatial effect.
Results highlight the strong potential of local indicators of spatial dependence in landscape pattern mapping across various geographical scales. This is based on evidence from a sequence of exploratory comparative analyses and a time series study of land cover dynamics over Phoenix, AZ. The newly proposed SFRR method is capable of producing reliable estimates when analyzing statistical relationships involving geographic data and highly correlated predictor variables. An empirical application of the SFRR over Phoenix suggests that urban cooling can be achieved not only by altering the land cover abundance, but also by optimizing the spatial arrangements of urban land cover features. Considering the limited water supply, rapid urban expansion, and the continuously warming climate, judicious design and planning of urban land cover features is of increasing importance for conserving resources and enhancing quality of life.
ContributorsFan, Chao (Author) / Myint, Soe W (Thesis advisor) / Li, Wenwen (Committee member) / Rey, Sergio J (Committee member) / Arizona State University (Publisher)
Created2016