Drag queening men, typically gay men who perform femininities for entertainment, use makeup, padding, injections and other tools to change their bodies for performance. I focus on the backstage activities of drag performers in Baton Rouge, Louisiana, rather than conventional observations from the audience, to explore the negotiation, construction and implications of these bodies, both physically and discursively. Through autoethnographic accounts and participating in my own drag performance, I highlight the often unseen, less frequently discussed aspects of drag queening in order to lessen the distance between the efforts of performers and the stage. Drag queening mens bodies are a queer assemblage that contests the heteronormativity of the male body. Drag performers corporeal crafting suggests the body is a multitude of coexisting processes that can be transgressive, but may still reify racist structures and heteronormative gender politics they seek to combat.

Severe weather events can have a significant impact on transportation networks. Many previous studies tried to analyze and explore the tremendous impact of extreme weather events on traffic behavior, speed, travel time and capacity. The purpose of this research was to analyze and discuss the impact of precipitation, temperature, visibility and wind speed on hourly weekday traffic flow volume in Atlanta, Georgia. This study focused on investigating which weather variables affect traffic volume, developing a machine learning based predictive technique to derive weather-traffic volume decision rules, and building a decision support tool. The correlation between extreme weather events and traffic volume was investigated by comparing traffic volume between a base case scenario and an extreme weather scenario. This research used 2 main data sources: hourly traffic data as recorded by 50 Automatic Traffic Recorder (ATR) sites around Atlanta and hourly precipitation data from 4 climate stations retrieved from the Integrated Surface Hourly (ISH) weather data archives of the National Centers of Environmental Information (NCEI). The statistical analysis and spatiotemporal relationships between traffic volume and weather variables were analyzed individually and evaluated using statistical tests. A machine learning technique was also used to simultaneously examines weather variables and hours of days to predict leading variables that greatly contributed towards a reduction in traffic volume. According to the results of this analysis, there were significant impacts of visibility, precipitation and temperature on traffic volumes especially during certain hours of the day. Extensive statistical analysis proved that during certain hours of the day, individual weather elements such as precipitation, minimum temperature, visibility and wind can have statistically significant individual impact in reducing traffic volume. Machine learning techniques helped derive models that can be used to predict conditions resulting in a decrease in traffic volume. A decision support tool was also developed to visualize traffic volume and weather interactions.

Based on the CTPP data 1990-2010, this research analyzes the temporal variability of commuting patterns and efficiency (in both distance and time) in Baton Rouge, Louisiana. It proposes a simulation-based method to measure commuting by simulating individual resident workers, jobs, and trips between them, in order to mitigate the aggregation error and scale effect that are commonly encountered in existing studies. Specifically, the Monte Carlo simulation approach is adopted to simulate individual resident workers and jobs that were consistent with their spatial distributions across the areal unit (e.g., census tract), and then simulate individual trips that were proportional to the existing journey-to-work trip flows. The results indicate that average commute distance kept climbing between 1990 and 2010, whereas average commute time increased between 1990 and 2000 but then slightly dropped toward 2010. As commuting is a trip linking ones residence to employment, this research follows the long tradition of using the urban land use pattern, namely the spatial separation between residential housing and job location, to explain the observed commuting pattern. Three land use measures are used: distance from the CBD, jobs to resident workers ratio, and a gravity-based job proximity index. The research finds that these land use measures remained a good predictor of commuting pattern in Baton Rouge over time, and the best model explained up to 90 percent of mean commute distance and about 30 percent of mean commute time. Furthermore, nonspatial factors such as a workers socioeconomic attributes also influence commuting. Foremost, income plays an important role in ones residential choices and thus commuting. This research focuses on the role of wage rates of resident workers in commuting pattern. It is reported that commuting behaviors varied across areas of different wage rates, captured by a convex shape. Initially workers living in more affluent neighborhoods tended to commute more, but those in areas with the highest wage rates retreated for less commuting. This trend remained relatively stable over time. Wasteful (excess) commuting is also examined as the overall commuting efficiency metric for the study area. Wasteful commuting is measured as the proportion of actual commute that is over minimum (optimal) commute when assuming that people could freely swap their homes and jobs in a city. This research identifies two contributors resulting in the miscalculation of wasteful commuting: reporting errors and the use of aggregate zonal data. The former tended to overstate the actual commuting length and led to overestimate wasteful commuting; and the latter (especially the use of large areal unit) led to underestimate wasteful commuting. This research indicates that the percentage of wasteful commuting increased significantly between 1990 and 2000 and stabilized afterward.

In the late 1970s, the African American Jazz Coalition responded to the marginalization of black vendors at the New Orleans Jazz & Heritage Festival by partnering with the Festival to create the Koindu Marketplace, now known as Congo Square. Whereas much public representation of the Festival suggests a transcendence of racial boundaries inside Festival grounds, the content and structure of contemporary interviews with the activists reflect continued racial tensions, power dynamics, and resentment. This thesis analyzes oral histories with the founders of the Afrikan American Jazz Coalition stored at the New Orleans Jazz & Heritage Foundation Archive. Critical discourse analysis of these interviews focuses on linguistic structures and inherent frames of worldview. Juxtaposing interviewers intentions regarding the Festivals mission with what the activists consider a continued marginalization of black culture, I highlight moments of both conflict and heightened self-awareness amongst the participants.

Ground level ozone (O3) is a pollutant of great public health concern. Spatial interpolation techniques provide powerful tools in estimating O3 exposure, but many fall short when predicting O3 on complex surfaces, especially given the high local variability typically associated with O3 data. Like most other locations, the Baton Rouge, Louisiana, O3 non-attainment zone (BRNZ) is plagued by a sparse density of O3 monitoring stations. This research explores land use regression (LUR) as an alternative spatial prediction method in and around the BRNZ. Multiple years of data are used to partially compensate for the small sample of spatial points. To better associate O3 measurements with the localized land cover, deviations-from-the-regional mean (devRM) are utilized rather than direct observations (DO). Land cover data used did not perform well in predicting the daily maximum O3 but performed moderately well for longer averaging periods. A model using the monthly mean O3 maxima averaged over a three-year period was able to explain 42.04% of the variance in devRM data. Predicted devRM using this model accounts for 4.55% of the variance in DO, the regional mean accounts for 88.65% of the variance, and when summed, the regional mean and modeled devRM account for 93.50% of variance in DO O3 data. These results are useful for future refinement of LUR models and will be useful to environmental planners and epidemiologists as they evaluate and mitigate the effects of O3 in Louisiana.

Considerable attention has been given to the measureable differences that exist between different human populations in the size and shape of the pelvis, with Africans having a narrower pelvis than Europeans. By collecting data on sacral breadth from a South African skeletal population, this study (1) tests the hypothesis that African blacks possess a narrower sacrum, and by inference pelvis, than whites and (2) considers whether the size variation between blacks and whites is due to nutritional, historical and social differences, to a genetic basis related to climatic adaptation, or to both. White South Africans were found to possess a significantly wider sacrum and longer costal processes for S1 than black South Africans. Two possible interpretations of the results were addressed, size variation is due to: (1) nutritional differences related to socioeconomic status, or (2) climatic adaptation based on thermoregulatory principles.

In this thesis, I examine the production, use, and decoration of ceramic vessels at the Early Horizon center of Caylán (800-1 B. C. E.) on the Peruvian North Central-Coast. Pottery sherds are artifacts ubiquitously recovered at archaeological sites, especially in sedentary agrarian societies. Most studies of ceramics in Peru have focused on typological sequences. Recently investigators in the Nepeña Valley have focused on ceramic pastes and technologies. The materials examined in this thesis were excavated from Caylán. Caylán is an incipient urban center, which developed during the Early Horizon (900-200 B. C. E.). Cayláns dense urban core consists in 43 residential, walled compounds built around communal plazas. A monumental mound sits in front of a large public area. Archaeologists from the Proyecto de Investigación Arqueológica Caylán conducted excavations in 2009 and 2010. Six excavation units and sixteen test pits were placed throughout the site to sample the diversity of architecture and material remains. I conducted a macroscopic analysis of a sample of pottery sherds (n=11,270) from three excavation units in the Main Mound, Plaza-A, and Compound-E. My analysis was conducted at the Museo Regional de Casma Max Uhle where the Cayláns materials are stored. The analysis was a visual identification and classification of raw materials used to produce pottery. Early Horizon sherds were classified into paste and ware groups based on their features and mineral inclusions. I identified ten Early Horizon ware groups based on the minerals visible in the analyzed sherds. Ware Group 6 is the most common. It was associated with all units, all vessel forms, and all surface finishes and decorations The mixed sand inclusions were likely from deposits close to the ancient settlement where pots were produced, used, broken, and discarded. Paste groups, divisions within ware groups, varied in size from very coarse to fine. Inclusions were likely screened before being added to the clay. Comparative studies indicate that Caylán wares align with ceramics recovered from the coeval sites Huambacho and Samanco. This study provides a first glimpse into the composition of Early Horizon ceramics and their making, opening up future avenues for other compositional analyses.

The incidence of harmful algal blooms (HABs) caused by blue-green algae has been increasing in coastal and freshwater ecosystems of the United States in recent years, and has had great influence on ecosystem, economic, and public health. This thesis aims at testing the feasibility of using machine learning methods in comparison to traditional regression models to detect and map the blue-green algae distribution in low-medium biomass waters (Chl-a < approx. 20 μg/L) from a Landsat 8 image with the support of some in situ Chl-a measurements in Harsha Lake, Ohio. Two algorithms were compared: one is the conventional empirical method Stepwise Multiple Linear Regression to see if there is a strong linear relationship between measured Chl-a concentrations and the Landsat 8 spectral data in the study area, and the other is one of the most popular machine learning methodsRandom Forests. Major findings include: (1) both a conventional linear regression model and a Random Forests model worked well in mapping the extent and biomass of blue-green algae in Harsha Lake on September 21, 2015, but the Random Forests model outperformed the linear regression model; (2) the prediction surface from the Random Forests method illustrated that 89.30% of Harsha Lakes area had Chl-a values less than 10 µg/L on the sampling date, while only 10.70% of the entire study area had Chl-a concentrations between 10 µg/L and 20 µg/L. Higher Chl-a values (especially for Chl-a larger than 10 µg/L) were mostly distributed in the mouths of rivers or streams, which might be caused by the influx of nutrients from agricultural or urban land use by rivers and streams. The results show the utility of the Random Forests approach based on Landsat 8 imagery in detecting and quantitatively mapping low biomass HABs, which is considered to be a challenging task.

Spatial and temporal trends in temperature and precipitation extremes were investigated for the Southeast United States for the period 1948 to 2012 using 27 extreme indices developed by Working Groups headed by the World Meteorological Organization. Results show region-wide warming in extreme minimum temperatures and cooling in extreme maximum temperatures. As a result, diurnal temperature ranges are decreasing for most stations. The intensity and magnitude of extreme precipitation events appear to be rising overall, though eastern sites are experiencing increasing dryness in some indices. Seasonal trends suggest that warming in minimum temperatures is most pronounced in summer and least pronounced in winter. Fall is becoming significantly wetter, while spring and summer are getting drier, on average. Principal component analysis (PCA) was used to develop a regionalization of extremes for the Southeast. Results based on temperature extreme indices divided the Southeast into roughly equal western and eastern regions, suggesting that western and eastern stations tend to covary but in opposite directions. This likely reflects synoptic scale weather patterns that frequently affect the region throughout the year. A PCA based on precipitation extreme indices resulted in a greater number of small groups exhibiting similar modes of variability. A seasonality of extremes was further characterized for the Southeast. Extreme seasons tend to follow traditional 3-month definitions of seasons. An extended winter season may be defined as November to March, while summer occurs from June to August, peaking in July. Based on analysis of state and local planning and policy from six case study sites across the Southeast, this research suggests that many existing efforts may contribute to climate mitigation and adaptation. Similarities appear in sector-based planning, largely in response to federal mandates, though levels of engagement differ between sites. Threats from changing temperature and precipitation extremes are addressed only to a limited extent. Leadership priorities, federal actions, wealth, population, and experience with hazards seem to influence state and local actions. Recommendations are offered to guide future climate planning and policy. Findings can benefit planners, policy analysts, decision makers, and hazards specialists engaged in climate adaptation and hazard mitigation in the Southeast and beyond.

Electrical resistivity was used at the Livonia Mound site (16PC1) to identify construction breaks, possible human burials, and other cultural activity below the surface. Resistivity transects traveled across the mound and the level surface directly south of the mound; this latter section was called Area A. Four transects stretched across the north, south, west, and east slopes of the mound; as a result, four vertical profiles were created from the apparent resistivity (âa) values. The standard deviation of each transect was computed using the âa values from the four pseudo-sections to establish the base-line for analysis. âa values for Area A were figured separately because of the differences in temperature at the times the surveys were taken which impact the moisture within the soil. Four areas of high âa were identified; these anomalies could represent human burials or other cultural activity beneath the surface. Area A and the west transect produced anomalies hinting at cultural activity below the surface, although no definitive evidence of human burials was found. The vertical profiles from the east and west transect show evidence that the top 3.0 m were deposited in a single construction episode. High âa anomalies in the north and south transects distort the profiles; thus there was no conclusive evidence to support or refute a single-phase construction episode for the top 3.0 m of the remaining mound.