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Spatial disorientation is the singular most common factor in human-error aviation accidents, and over ninety percent of those accidents are fatal. Despite advances in aviation over the past one hundred years in both technology and training, spatial disorientation mishaps continue at a steady pace, even though other incidents declining in frequency. Because spatial disorientation is a highly complex phenomena that involves the vestibular system, the visual system, and cognitive factors such as workload and attention, predicting spatial disorientation is extremely difficult. Likewise, exactly replicating spatial disorientation for training purposes is challenging as well as extremely dangerous and costly. The goal of this study was twofold: to understand if innate abilities can predict propensity for spatial disorientation, and to investigate the efficacy of using story-based vignettes – narratives – to train spatial disorientation to increase schematic learning in pilots. Results demonstrated that performance on a spatial orientation task such as the Direction Orientation Task (DOT) is not a reliable predictor for spatial disorientation recognition based on self-report spatial disorientation frequency. In addition, though story-based vignettes demonstrated potential for increased cue recognition over a control training event, significant differences were not found in novel spatial disorientation recognition, critical cue identification, or confidence. These findings indicate that spatial disorientation could be a completely perceptual (bottom-up) task rather than one that is both top-down and bottom-up and implies future research into the ways we describe and measure spatial disorientation in order to understand it as well as train for it.

The study proceeded as follows: The distribution of Q. garryana was mapped through field reconnaissance. Annual rings from fifteen trees were counted in order to establish age versus trunk diameter ratios. This greatly simplified the dating of trees. Transects were then run through individual groves to determine population age structures. These age structures, along with Q. garryana's distribution patterns, were graphically compared with historic temperature, precipitation, and growing season data. The effect of livestock grazing on oak reproduction was examined. Potential vagility of the species was evaluated by estimating the potential for acorn dispersion by airfall, streams, vertebrates, and the human (aboriginal) population. Finally, pollen evidence was scrutinized in conjunction with acorn dispersal rates in order to determine the longer term history of Q. garryana in this portion cf its range.

The purpose of the study was to determine the degree and intensity of the factors affecting the spatial distribution of substandard housing in Bowling Green, Kentucky. A stepwise regression model revealed that straightline distance from the CBD, overcrowded units, average number of rooms per dwelling unit, renter occupancy and non-white occupancy accounted for only 37 percent of total explained variation. A filtering process based on blocks exceeding ten percent Black population was utilized to divide the universe. New analyses on Black and White sectors within the city did not increase the coefficient of determination. They did, however, reveal a great disparity in the overall housing situation between Black and White families In Bowling Green. A residual analysis revealed that locations adjacent to diseconomies may be a significant factor in helping to explain the problem distribution.

Modern Geoweb-enabled PPGIS methodologies incorporate interactive map applications as the main driver for public engagement and data collection. However, little research explores exactly how the public interact with these applications to produce spatial data, a fact that contributes to criticisms of final data quality. Usability evaluation offers a solution for developing better PPGIS data production systems by identifying problems in the application interface for which the public engage. Drawing on a case study example of a PPGIS application developed to collect socio-spatial data from members of a random public, this paper addresses usability in a three-stage approach. First, controlled experimentation methods capture performance, preference, and data production metrics. Second, visual and statistical analysis of the captured usability data identify problems in the interface. Results indicate that users learned, became efficient, and were generally satisfied with the application, but also committed errors that may have affected data quality. Third, a solution-oriented critique of the application interface suggests new design options to mitigate future problems in similar applications. The paper ends by providing a conceptual framework for usability as it relates to PPGIS data production and incorporates it into an informed discussion on data quality and future research needs for maintaining the viability of PPGIS projects.

This thesis is a case study of a small agricultural community located along the eastern edge of Everglades National Park, The purpose of this study was to document the way land use decisions have been made and how these decisions have affected this community and the Everglades ecosystem. This research demonstrated that decisions made by the involved agencies have negatively affected both the community of Pariah, Florida and the Everglades ecosystem. Research methods included extensive document research, participant observation and formal and informal interviews. It appears that public concern over “saving the Everglades” has been used to provide a legitimating framework for the achievement of a plurality of personal goals and unstated agency agendas that have little or nothing to do with either the Everglades or the environment in general.

This dissertation introduced a method to construct a new measure for trade flows within a region using nighttime lights. After analyzing the relation between lights data and other proxies of economic human activity, I employed light data and econometric techniques to estimate the bilateral trade between any two regions around the world. Using these estimations, I estimated the overall internal trade volume for all countries. Moreover, I estimated the effect of internal trade within a state of the United States on the state’s income. The first essay proposed nighttime lights as an alternative proxy for economic activity to be used in gravity regressions. Due to the well-known problems in the measurement of gross domestic or regional products, gravity regressions based on both international and intranational trade data suffer from potential biases. At both international and intranational levels, log nighttime lights positively and significantly enter the gravity regressions (with a coefficient of roughly one) that explain at least about half of the variance in exports. The results were shown to be robust to the inclusion of several control variables and the consideration of predicted trade flows. Trade within a nation and internal distance are variables known to play key roles in explaining home bias and the distance puzzle in international trade literature, but data on these measures are limited to only a few countries. To address this problem, in the second essay, I constructed micro-founded measures of internal trade and internal distances from satellite data on nighttime lights. By estimating the gravity equation coefficients using the simulated method of distance estimation, I constructed the bilateral trade flow at subnational scale and aggregated it to overall internal trade. I found my internal trade measure is highly correlated with its benchmark, the difference between GDP and total exports; however, I showed it has more information and is a more precise measure for developed countries, which have a large amount of non-tradable services included in their national income account data. The internal distance measure is generated as the lights-weighted average distances between the states within a country. While my internal distance measure is largely correlated with its alternative, which is constructed based on city-level population data, it does not suffer from the uncertainty surrounding population data. Correlation between trade and income cannot identify the effect of trade because of the endogeneity problem. The third essay examined this relationship at subnational level and by focusing on instrumenting trade via time varying geographic factors. Proximity and economic size are determinants of trade that are uncorrelated with other income determinants. This experiment not only confirmed the effect of interregional trade, but also provided evidence that intraregional trade has a large and statistically significant impact on income. I found, however, that the effect of both trade measures is statistically similar; a one percentage point increase in the interregional and intraregional trade ratio increases income per person by 2 to 4 percent.

Due to increasing water treatment costs and conservation needs, traditional water loss analysis and acoustic leak detection methods are becoming heavily scrutinized by water utilities. This study explores water loss in Johnson City, Tennessee and how geospatial data analysis techniques improve water loss mitigation. This project uses sample water system pressure data and ordinary kriging spatial interpolation methods to identify leakage areas for further investigation. Analysis of existing geographic information system (GIS) water utility datasets with interpolated hydraulic grade values at sample water pressure points produce manageable survey areas that pinpoint areas with possible water leakage. Field detection methods, including ground-penetrating radar (GPR) and traditional acoustic methods, are employed to verify leakage predictions. Ten leakage areas are identified and verified using traditional acoustic detection methods, work order research, and GPR. The resulting data show that spatial analysis coupled with geospatial analysis of field pressure information improves water loss mitigation.

Understanding human mobility patterns is important for severe weather warning since these patterns can help identify where people are in time and in space when flash floods, tornados, high winds and hurricanes are occurring or are predicted to occur. A GIS (Geographic Information Science) data model was proposed to describe the spatial-temporal human activity. Based on this model, a metric was designed to represent the spatial-temporal activity intensity of human mobility, and an index was generated to quantitatively describe the change in human activities. By analyzing high-resolution human mobility data, the paper verified that human daily mobility patterns could be clearly described with the proposed methods. This research was part of a National Science Foundation grant on next generation severe weather warning systems. Data was collected from a specialized mobile app for severe weather warning, called CASA Alerts, which is being used to analyze different aspects of human behavior in response to severe weather warnings. The data set for this research uses GPS location data from more than 300 APP users during a 14 month period (location was reported at 2 minutes interval, or at based on a 100m change in location). A targeted weather warning strategy was proposed as a result of this research, and future research questions were discussed.

Conservation managers and planners need the ‘best available science’ to support robust and defensible decisions, ensuring that public resources are appropriately allocated. Spatial planning products and decision-support tools developed for this purpose should enable partner organizations to achieve focus, coordination, and increased effectiveness in their investments and actions. Whereas conservation partnerships have historically created distinct planning tools, there is increasing interest for improved coordination, communication, and unifying biological datasets to improve the cohesiveness of regional management activities. We sought to inform spatial conservation planning efforts in the southeastern United States through the development of species distribution models for focal avian species to prioritize open pine habitat management and a methodological framework to assess alignment between spatial planning products. We offer insight that can help managers maximize data-informed decision making and contribute to more synergistic and effective conservation actions in the future.