A considerable amount of research indicates that relative income is related to health, or more specifically, that greater disparities in income are related to poorer health. Referred to as the relative income hypothesis, the causal mechanisms, social implications and general nature of this relationship continue to stimulate interesting and important research across several disciplines. One dimension of the association between income inequality and health that has yet to be explored or considered fully is the geographical. In an attempt to elucidate and evaluate how geography is implicated with the relative income hypothesis, the role and significance of geographic spillovers, regional effects and diffusion are theorized and explored. This research uses a geographic framework, spatial analytic techniques, and current and imputed data to revisit and re-examine the linkage between relative income and health at the global scale. Results indicate that the association between income inequality and health is somewhat tempered when examined within an explicitly spatial framework, and that regional and geographic effects need to be considered and treated carefully in cross-sectional, international studies of health.

Air pollution is directly linked to death. In December 2020, a UK coroner ruled that air pollution was the cause of a fatal asthma attack that led to the 2013 death of nine-year-old Ella Adoo-Kissi Debrah who lived adjacent to a busy motorway (BBC News, 2022). The assignment of air pollution as the official cause of death on a death certificate was the first of its kind in the world (Reynolds, 2020). Though this was the first official assignment of air pollution as a cause of death, there are numerous studies linking air pollution exposure with mortality all over the world. Before the COVID-19 pandemic, the air pollutant PM 2.5 was identified as the “largest environmental risk factor in the United States” (Goodkind et al. 2019, p. 8780) and the cause of more annual premature deaths than traffic accidents and homicides combined (Goodkind et al. 2019).

With the onset of the COVID-19 pandemic, researchers began assessing the impact of air pollution exposure on COVID-19 incidence and death. In a widely received, nationwide study linking air pollution exposure to COVID-19 mortality, Harvard T.H. Chan School of Public Health researchers, Wu et al., produced significant findings linking the impact of long term exposure to PM 2.5 to COVID-19 mortality across the contiguous United States. This 2020 study, published in ScienceAdvances, has been cited over 600 times, covered by 131 news outlets and downloaded over 15,000 times. Georeferenced data is routinely used in public health research such as this, however, the substantive influence of geography in the relationship between the treatment and outcome variable is often not considered in the model specifications, research design, nor the sampling strategy (Goldhagen et al., 2005; Matisziw, Grubesic, and Wei 2008). Additionally, the mechanism of data aggregation to an administrative unit may spatially misrepresent the data (Delmelle et al., 2022). As air pollution is a local, regional, and transboundary phenomenon (Nordenstam et. al, 1998; Goodkind, 2019), spatial autocorrelation, or spatially similar values, in the long term exposure to PM 2.5 among U.S. counties is likely. Despite the inclusion of maps indicating strong spatial trends in the long term exposure to PM 2.5 and COVID-19 mortality, the possible presence of spatial autocorrelation at the local level or spatial heterogeneity at the regional level was not investigated by the authors.

Epidemiological studies invoking large, areal units may misrepresent the underlying, spatial processes of environmental health-hazards and produce unreliable treatment effect estimates when relating air pollution exposure to disease (Fotheringham and Wong, 1991; Kolak and Anselin, 2019). In this thesis, the fragility of the Wu et al. treatment effect estimate to unobserved confounding is assessed utilizing an alternative sensitivity analysis framework. This framework revealed that the estimate derived by Wu et al. (2020) is much more fragile to confounding than reported by the authors. Spatial analysis was then applied to investigate the possibility of spatial regimes (e.g. hotspots) in the treatment and outcome variables which may contribute to biased or inefficient treatment effect estimates. Strong levels of spatial autocorrelation and regional spatial heterogeneity in the long term exposure to PM 2.5, and to a lesser extent in the COVID-19 mortality rate, were confirmed by both computational and exploratory spatial data analysis. The highly variable associations between long term exposure to PM 2.5 and COVID-19 Mortality per U.S. Census Region or EPA Climatically Consistent Region delivered the expected result that the relationship between the treatment and outcome variable changes with changes in the sub-National definition of place. An understanding of the geography of the ubiquitous, locally variable and far-reaching PM 2.5, and its related health-hazard risks can contribute to an uncovering of the politics, power relations, and socioenvironments that coproduce differential access to clean air and the resulting uneven health burdens experienced by Black, LatinX, Asian-American, and immigrant communities. This is an essential step towards disentangling the relationships rendering clean air no longer an “open-access good” (V�ron, 2006).

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This dissertation explores how maps and architectural landscape renderings function as rhetorical devices in support of changing understandings of urban-nature relations. Arguments around these changing relations to urban nature are investigated through a comparison of visual materials, specifically maps and renderings of Los Angeles River projects from the early 20th century through the early 21st century. These materials include engineering maps of flood control infrastructure, diagrammatic maps produced by river advocates, and landscape architectural renderings used in river revitalization master plans. The different qualities of these materials support a shifting social relation to the river, in addition to arguments made for constructing a flood control channel, water conservation infrastructure, or riverfront bicycle paths. I developed an infrastructure typology that delineates three different ways of understanding the role of the Los Angeles River over time: 1) as primarily a flood control channel, 2) as a multi-purpose channel within a watershed ecology, and 3) as the site of a waterfront linear park running throughout the city. Archival research in the collection of the Friends of the Los Angeles River (FoLAR) papers and semi-structured interviews with several key river revitalization and environmental advocates deepen the understanding of the work that maps did in shifting social relations to the river. Maps and visualizations of the Los Angeles River are chosen because they advocate for substantially different functions of the river (arguing to concretize it for flood control or remove concrete for habitat preservation, for example) prior to any changes on the ground. In this way, visualizations serve as rhetorical devices rather than representations of what is on the ground.

As geographic information systems (GIS) technology continues to mature and as university instruction progressively shifts to online learning environments, it is the responsibility of geography educators and the academy more broadly to develop and refine practices for teaching and learning GIS in online environments. Educators of all specializations teaching online today are confronted not only with the challenges and uncertainties endemic to online learning environments, but also with exciting opportunities for implementing data-driven optimization of instructional design and student learning outcomes using educational “big data” -- detailed and automatically captured records of actions taken by students within Web-based learning management system (LMS) environments. This dissertation presents three stand-alone research articles that reside at the nexus connecting GIS education to online education research, and most notably to the emerging field of learning analytics. Each article contains a case study built around educational ``big data'' collected within an online introductory GIS course. The first research article, which is focused upon instructional technology specific to GIS education, assesses the suitability of open source GIS software for supporting introductory online education in GIS. The two remaining research articles relate to computational analysis of data derived from Web-based LMS software. The second research article introduces descriptive and inferential analytics, as well as accompanying and contextualizing data visualizations, that can be derived from the processing and analysis of raw LMS student activity log data. The third research article uses LMS data as a vehicle for both (1) proposing new methodology for approximating student behavior, including procrastination and productivity, and (2) exploring the many relationships that exist between student behavior and student performance in online courses. An additional methodological chapter introduces a new LMS log analysis software tool originally developed to perform the data analysis and visualization for this dissertation; a technical overview of the software's functionality is presented therein to serve as a guide for online educators who wish to begin working with LMS log analysis.

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