abstract: Economic inequality is always presented as how economic metrics vary amongst individuals in a group, amongst groups in a population, or amongst some regions. Economic inequality can substantially impact the social environment, socioeconomics as well as human living standard. Since economic inequality always plays an important role in our social environment, its study has attracted much attention from scholars in various research fields, such as development economics, sociology and political science. On the other hand, economic inequality can result from many factors, phenomena, and complex procedures, including policy, ethnic, education, globalization and etc. However, the spatial dimension in economic inequality research did not draw much attention from scholars until early 2000s. Spatial dependency, perform key roles in economic inequality analysis. The spatial econometric methods do not merely convey a consequence of the characters of the data exclusively. More importantly, they also respect and quantify the spatial effects in the economic inequality. As aforementioned, although regional economic inequality starts to attract scholars' attention in both economy and regional science domains, corresponding methodologies to examine such regional inequality remain in their preliminary phase, which need substantial further exploration. My thesis aims at contributing to the body of knowledge in the method development to support economic inequality studies by exploring the feasibility of a set of new analytical methods in use of regional inequality analysis. These methods include Theil's T statistic, geographical rank Markov and new methods applying graph theory. The thesis will also leverage these methods to compare the inequality between China and US, two large economic entities in the world, because of the long history of economic development as well as the corresponding evolution of inequality in US; the rapid economic development and consequent high variation of economic inequality in China. / Dissertation/Thesis / Masters Thesis Geography 2016

Analysis of the alignment of geologic features and the use of GPS strain measurements are very different approaches to understanding crustal deformation histories and crustal and upper mantle properties. In this dissertation, two study areas with markedly different environments are evaluated using these approaches. The first study area includes the Guinea Plateau offshore West Africa that is part of a complex passive-margin system formed during two phases of rifting during the Jurassic and Cretaceous. Circular features revealed in two 3D seismic reflection surveys are interpreted to be extrusive volcanic features or vents emplaced after the cessation of Cretaceous rifting and opening of the Equatorial Atlantic Ocean. Statistically significant alignments of the vents implies that their distribution was influenced by faults or fractures not obvious in the seismic data alone. The existence of inferred alignments provides additional information about possible structures in the area of the volcanic vents that can be compared to more regional structures, giving better insight to magma migration and extrusion and structure of the Guinea Plateau. The alignment in one of the 3D survey areas is sub-parallel to oceanic fracture zones and continental lineaments that may extend into the survey and could have influenced the distribution of the volcanic features. The alignment in a separate 3D survey area is sub-parallel to the shelf-break and thought be related to inferred oceanward crustal thinning. Employing a different approach to the analysis of deformation, the second study area focuses on the Southern Basin and Range and Colorado Plateau, a weakly deforming area that is still capable of producing large earthquakes such as the 1887 Mw 7.5 Sonoran earthquake. To better constrain crustal motions and investigate the distribution of strain rates several hundred kilometers from the Pacific-North American plate boundary, an expanded GPS network of 34 sites was installed to complement existing continuous and campaign networks. Coseismic and postseismic deformation associated with earthquakes outside the study area, including the 4 April 2010 Mw 7.2 El Mayor–Cucapah, affected the GPS time series resulting in time-varying crustal surface velocities that obscured the background tectonic deformation. Through a deformation model, viscosities of the lower crust and upper mantle are estimated and the effects of earthquakes dating back to 1887 are removed from the time series to yield a time-independent or background secular velocity. A total velocity uncertainty is calculated that includes uncertainty of the time-independent velocities related to uncertainty in the viscosity estimates. Displacement histories are used to illustrate how earthquakes along the Pacific-North American plate boundary can temporarily impede extension in the Southern Basin and Range, particularly in southwestern Arizona. The time-independent velocities are used to calculate strain rates using latitudinal and longitudinal velocity profiles on one-degree increments. On a statistically significant basis, the velocity profiles are modeled with two linear segments rather than a single linear segment. Using the break points dividing the segments, the study area can be separated into a relatively lower-strain-rate eastern domain and a relatively higher-strain-rate western domain. The break points are interpreted to signify a boundary zone approximately 1000 km in length that overlaps tectonic and deformational boundaries described in previous studies. Comparing the time-invariant velocities with cumulative extensional slip rates of Quaternary faults across the area reveals a discrepancy in a portion of the area that is difficult to explain, but may be related to the time-varying velocities resulting from earthquakes on the Pacific-North American plate boundary.

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Data semantic and schematic heterogeneity is a major obstacle to the reuse and sharing of geospatial data. This research focuses on developing an ontology-based methodology to logically integrate heterogeneous geographic data in a cross-border context Three main obstacles hindering data integration are semantic, schematic, and syntactic heterogeneity. Approaches to overcome these obstacles in previous research are reviewed. Among the different approaches, an ontology-based approach is selected for horizontal geospatial data integration in the context of cross-border applications. The integration methodology includes the extraction of application schemas and application ontologies, ontology integration, the creation of a reference model (or ontologies), schema matching and integration, and the creation of usable integrated datasets. The methodology is conceptual and integrates geospatial data based on the semantic content and so is not tied to specific data formats, geometric representations, or feature locations. In order to facilitate the integration procedure, four semantic relationships are used: refer-to, semantic equivalence, semantic generalization, and semantic aggregation. A hybrid ontology approach is employed in order to facilitate the addition of new geospatial data sources to the integration process. As such, three levels of ontologies are developed and illustrated within a MS ACCESS database: application, domain, and a reference model. Furthermore, a working integration prototype is designed to facilitate the integration of geospatial data in the North American context given the semantic and schema heterogeneities in international Canadian-US geospatial datasets. The methodology and prototype provide users with the ability to freely query and retrieve data without knowledge of the heterogeneous data ontologies and schemas. This is illustrated via a case study identifying critical infrastructure around the Ambassador Bridge international border crossing. The methodology and prototype are compared and evaluated with other GDI approaches and by criteria introduced by Buccella et al. (2009). Specific challenges unique to GDI were uncovered and include geographic discrepancies, scale compatibility and temporal issues.

Bibliography: pages 173-178. / Classical terrestrial (horizontal and vertical) networks and Doppler satellite derived networks are combined in a three dimensional transformation adjustment by solving for the external bias parameters using any of the three standard seven parameter similarity transformation models, namely the Bursa, Molodensky and Veis models. The object of this combination may be merely to merge the systems or networks, but may additionally involve an attempt to assign physical meaning to the estimated bias parameters. These two aspects, and the influence of the a priori Variance-Covariance matrix of the observables on the parameters and their interpretation is studied in detail. An in-depth conceptual, mathematical and numerical comparative assessment of the three standard models is made. The homogeneity of the classical terrestrial South African networks is investigated by comparing the transformation parameter sets derived for different regions and sub-regions of the country.

Hurricane evacuation refers to an individual’s or household’s response to a hurricane threat by temporarily relocating somewhere safe. However, it is rarely the case that everyone will comply when he/she is ordered to evacuate by authorities. Understanding why people fail to heed evacuation orders is vital in order to minimize non‐compliance. In the past, factors contributing to hurricane evacuation behavior have been studied mainly using survey instruments. The most significant variables include official evacuation orders, housing type, perceived risks, environmental cues, and social cues. Factors such as information sources, experience, and demographic characteristics have weaker impacts on evacuation decisions or are significant in only a limited number of studies. In this study, many of the literature‐documented factors that contribute to evacuation behavior were examined using survey data collected in Charlotte County, Florida, before and after Hurricane Irma (2017). Additional environmental factors, some of which were not included in a majority of the previous hurricane evacuation studies, were examined together with the survey‐based, socioeconomic, and risk‐perception variables. Environmental factors examined less frequently include variables such as the structural characteristics of people’s homes, storm surge zone, wind zone, and distance to shelters. Environmental variables examined in the previous literature include elevation, flood zone, and distance to the coastline. These data were collected through publicly available data sources and linked with the survey respondents’ home locations. The literature review points to the importance of risk perception on people’s hurricane evacuation decision‐making, so factors that may have contributed to people’s hurricane risk vi perception were also examined. These factors include both the survey‐based socioeconomic variables and the GIS‐derived environmental factors described above. Risk perception in this study was measured through the respondent’s answers to a question regarding their sense of safety in a hypothetical Category 3 hurricane. Since an individual’s perception of safety is an overall measure of whether people think they are threatened by particular hurricane‐induced risks (such as flood damage, wind damage, and power loss), the term perceived vulnerability was adopted and used throughout the entire thesis. In this study, perceived vulnerability refers to a respondent’s overall sense of safety when they consider all the potential threats associated with a Category 3 hurricane. Descriptive statistics, chi‐square test, and independent samples t‐test were performed to assess for associations between independent variables and the two dependent variables. One of the dependent variables is the evacuation decisions made by the survey respondents in Charlotte County in response to Hurricane Irma. The other dependent variable is perceived vulnerability. Logistic Regressions were performed for each dependent variable with only significant factors (according to the chi‐square or t‐test analysis) input as the independent variables. The results showed: 1) For survey respondents in Charlotte County living within evacuation zones A and B (both were under mandatory evacuation orders during Hurricane Irma), education level, perceived hurricane evacuation zone, perceived vulnerability, and distance of residence to the nearest coastline were statistically significant factors associated with the evacuation decisions made during Hurricane Irma. 2) According to Logistic Regression, perceived vulnerability and the distance from the subject’s home to the nearest coastline were vii the most significant factors influencing the evacuation decisions made during Hurricane Irma. 3) For survey respondents living across Charlotte County regardless of evacuation zones, many socioeconomic, risk perception, and environmental factors were statistically significantly associated with their perceived vulnerability. 4) When these factors were included in the Logistic Regression analysis, the respondent’s storm surge zone and their belief that their houses were located in a low‐lying area, were the factors found to be most significantly associated with perceived vulnerability. This study contributes to the assessment of people’s hurricane risk perception and evacuation decisions. It also demonstrates the benefit of including environmental variables in hurricane risk perception and evacuation decision‐making research. Many factors, including less‐researched environmental variables, were integrated into this comprehensive study. Perceived vulnerability, a measure of overall perceived risks, was found to be the most significant factor associated with people’s hurricane evacuation decisions. It was found more important than whether people believe they heard evacuation orders or what type of evacuation order they heard. Evaluating what determines people’s perceived vulnerability is a relatively new research direction in studies examining hurricane evacuation behavior. In this study, many of the environmental variables (such as elevation, the distance of the home to the nearest coastline, evacuation zone, and storm surge zone) were found to be significantly associated with people’s perceived vulnerability. For the subjects in this study, fear of flooding was identified through the two significant variables standing out in the Logistic Regression analysis – respondent’s storm surge zone (an environmental factor) and respondent’s belief that their home is located viii in a low‐lying area (a specific risk perception factor). This finding may be useful to government officials when they communicate with the public prior to a hurricane making landfall. Including information such as flooding risks may motivate residents to comply with evacuation orders. / A Thesis submitted to the Department of Geography in partial fulfillment of the requirements for the degree of Master of Science. / 2019 / November 13, 2019. / GIS, Hurricane Evacuation, Perceived Vulnerability, Risk Perception / Includes bibliographical references. / Tingting Zhao, Professor Directing Thesis; James Elsner, Committee Member; Sandy Wong, Committee Member; Earl Jay Baker, Committee Member.

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