Increasing PM2.5 (particulate matter smaller than 2.5 micrometers) poses a significant health risk to people. Understanding variables critical to PM2.5 spatial and temporal variation is a first step towards protecting vulnerable populations from exposure. Previous studies investigate variables responsible for PM2.5 variation but have a limited temporal span. Moreover, although land-use classes are often taken into account, the vertical environment's influence (e.g., buildings, trees) on PM2.5 concentrations is often ignored and on-road circle buffers are used. To understand variables most critical to PM2.5 concentration variation, an air pollution sensor and GPS unit were affixed to a bicycle to sample for variables over three seasons (spring, summer, fall). Samples were taken on a route during the weekdays at four targeted hours (7AM, 11AM, 3PM, and 7PM) and joined with meteorological data. 3D morphology was assessed using LiDAR data and novel wind-based buffers. Wind speed only, wind direction only, and wind speed and direction buffers were computed and compared for their performance at capturing micro-scale urban morphological variables. Zonal statistics were used to compute morphological indicators under different wind assumptions in seasonal ordinary least squares regression models. A comprehensive wind and buffer performance analysis compares statistical significance for spatial and temporal variation …

Texas is the leading producer of renewable energy in the U.S, and Dallas-Fort Worth (DFW) is the largest metropolitan area in the state. Texas has a deregulated energy market, with three types of providers: privatized, public-owned, and co-operatives. Privatized providers compete in the deregulated market, and consumers choose between hundreds of electricity retailers. Public-owned providers are owned by the municipality, and electricity consumers that live within the city limits must use the municipal provider. Electric co-operatives operate similarly where customers within the region must use the co-operative, but instead of being owned by the city, co-ops are owned by the members (customers). To date, the availability, cost, accessibility, and outreach of renewable electricity among these provider types remains unclear. For this reason, my research examines the renewable energy market in DFW by asking: (1) Who has access to renewable energy and how do they understand it? (2) How do electricity retailers distribute and make renewable energy available? and (3) If consumers can choose their provider, why do they select certain electricity plans over others? My findings suggest that while many consumers want or are open to using renewable energy, uncertainties surrounding how to find or choose a provider, price, and …

The Dallas-Forth Worth Metroplex (DFW) serves as a diverse resettlement location for globally displaced refugees. While research examines how the nation impacts refugee resettlement, studies that examine the role of the city and community in placemaking are still lacking. In city resettlement investigations, research often focuses broadly on advocacy and political movements rather than the impacts of local-level structures and policies. In this paper, I develop an evaluation model using Jenny Phillimore's categories for successful refugee resettlement that examines how structural barriers, community interactions, and resource accessibility affect space and place for refugee populations. Through an ethnography of Chin and Rohingya refugee communities in DFW, I explore the differences between community-settled and state-settled refugee groups and the idea of an integrated resettlement program. Additionally, I argue that refugees who choose their settlement location in the United States are empowered and thus have a stronger connection to their host community than state-settled refugees. For example, in interviews, the Chin emphasized their ownership of Lewisville and feelings of home, while the Rohingya expressed feelings of placelessness and dispossession in Dallas. As governments push towards an entirely privatized system of refugee resettlement, this research argues for an integrated method that draws upon federal …

Mall after mall was built in American cities, exhaustively emulated by developers often working in concert with civic governments. In service of capital, neoliberal urban governance engages in the risky subsidization of spatio-spectacle production, working together with private business entities to bolster tax revenue and aid in private capital accumulation. The extensive replication of malls in close geographic proximity to one another across the American landscape, erected through the neoliberal partnerships of civic governments and private business interests, has greatly contributed to mall decline and mall death. There is now, however, a new spatio-spectacle that has arisen to take the place of the "great American shopping mall"—the luxury mixed-use development. These luxury mixed-use projects have been adopted as a new trend within urban development following the reality of sweeping mall decline and are proliferating across the (sub)urban landscape. Luxury mixed-use developments, I argue, are merely a continuation of late capitalism's problematic spectacle fetish. Moreover, this process is revealed to be inextricably entangled with gentrification, driven by cities' neoliberal desires to become/maintain status as global, "world-class" cities, performed through the spatialized ideology of neoliberal multiculturalism.

This thesis demonstrates a queer perspective stemming from a qualitative analysis of data gathered in interviews with LGBTQ+ people to analyze a transforming ethos of gayborhoods and queer desires. In particular, the research focuses on the interactive relationship between self-identified lesbian, gay, bisexual, transgender, and queer (LGBTQ+) participants; the cyberspace(s) of LGBTQ+ mobile-dating applications (apps); and tangible urban places. The topic of gayborhood demise and whether such places are worth saving has been debated by scholars and journalists for the last decade. The demise of gayborhoods is often thought to be a symptom of neoliberal urban processes such as gentrification within the context of the post-gay era and broader societal acceptance of homosexuality. This means the question of "if the gayborhood is worth saving" is inherently imbedded in an assumption that homosexuality is not viewed or treated as different or lesser than heterosexuality. In this imagined post-gay era, gayborhoods are declining because the dangers posed to the LGBTQ+ population are purported to no longer exist, so there is no longer a need for designated queer and/or safe places. This research destabilizes the assumptions embedded within the conception of the post-gay era by asking whether the gayborhood meets the needs and …

Low-cost sensors have the potential to create dense air monitoring networks that help enhance our understanding of pollution exposure and variability at the individual and neighborhood-level; however, sensors can be easily influenced by environmental conditions, resulting in performance inconsistencies across monitoring settings. During summer 2020, 20 low-cost particulate sensors were deployed with a reference PM2.5 monitor in Denton, Texas in preparation for calibration. However, from mid to late-summer, dust transported by the Saharan Air Layer moved through the North Texas region periodically, influencing the typical monitoring pattern exhibited between low-cost sensors and reference instruments. Traditional modeling strategies were adapted to develop a new approach to calibrating low-cost particulate sensors. In this study, data collected by sensors was split according to a novel dust filter into dust and non-dust subsets prior to modeling. This approach was compared with building a single model from the data, as is typically done in other studies. Random forest and multiple linear regression algorithms were used to train models for both strategies. The best performing split-model strategy, the multiple linear regression models split according to dust and non-dust subsets (combined R2 = 0.65), outperformed the best performing single-model strategy, a random forest model (R2 = 0.49). …