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.

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.

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.

Energy consumption in buildings, accounting for 41% of 2010 primary energy consumption in the United States (US), is particularly vulnerable to climate change due to the direct relationship between space heating/cooling and temperature. Past studies have assessed the impact of climate change on long-term mean and/or peak energy demands. However, these studies usually neglected spatial variations in the “balance point” temperature, population distribution effects, air-conditioner (AC) saturation, and the extremes at smaller spatiotemporal scales, making the implications of local-scale vulnerability incomplete. Here I develop empirical relationships between building energy consumption and temperature to explore the impact of climate change on long-term mean and extremes of energy demand, and test the sensitivity of these impacts to various factors. I find increases in summertime electricity demand exceeding 50% and decreases in wintertime non-electric energy demand of more than 40% in some states by the end of the century. The occurrence of the most extreme (appearing once-per-56-years) electricity demand increases more than 2600 fold, while the occurrence of the once per year extreme events increases more than 70 fold by the end of this century. If the changes in population and AC saturation are also accounted for, the impact of climate change on building energy demand will be exacerbated.

Using the individual building energy simulation approach, I also estimate the impact of climate change to different building types at over 900 US locations. Large increases in building energy consumption are found in the summer, especially during the daytime (e.g., >100% increase for warehouses, 5-6 pm). Large variation of impact is also found within climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations.

As a result of climate change, the building energy expenditures increase in some states (as much as $3 billion/year) while in others, costs decline (as much as $1.4 billion/year). Integrated across the contiguous US, these variations result in a net savings of roughly $4.7 billion/year. However, this must be weighed against the cost (exceeding $19 billion) of adding electricity generation capacity in order to maintain the electricity grid’s reliability in summer.

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.

Decades ago in the U.S., clear lines delineated which neighborhoods were acceptable for certain people and which were not. Techniques such as steering and biased mortgage practices continue to perpetuate a segregated outcome for many residents. In contrast, ethnic enclaves and age restricted communities are viewed as voluntary segregation based on cultural and social amenities. This diversity surrounding the causes of segregation are not just region-wide characteristics, but can vary within a region. Local segregation analysis aims to uncover this local variation, and hence open the door to policy solutions not visible at the global scale. The centralization index, originally introduced as a global measure of segregation focused on spatial concentration of two population groups relative a region's urban center, has lost relevancy in recent decades as regions have become polycentric, and the index's magnitude is sensitive to the particular point chosen as the center. These attributes, which make it a poor global measure, are leveraged here to repurpose the index as a local measure. The index's ability to differentiate minority from majority segregation, and its focus on a particular location within a region make it an ideal local segregation index. Based on the local centralization index for two groups, a local multigroup variation is defined, and a local space-time redistribution index is presented capturing change in concentration of a single population group over two time periods. Permutation based inference approaches are used to test the statistical significance of measured index values. Applications to the Phoenix, Arizona metropolitan area show persistent cores of black and white segregation over the years 1990, 2000 and 2010, and a trend of white segregated neighborhoods increasing at a faster rate than black. An analysis of the Phoenix area's recently opened light rail system shows that its 28 stations are located in areas of significant white, black and Hispanic segregation, and there is a clear concentration of renters over owners around most stations. There is little indication of statistically significant change in segregation or population concentration around the stations, indicating a lack of near term impact of light rail on the region's overall demographics.