Geographic Information System (GIS) skills are increasingly marketable across a wide range of industries, subject areas and specialized fields. As a result, GIS courses draw students from a plethora of disciplines beyond geography, including business, social sciences, agriculture, geology, natural resources and computer science. At advanced teaching levels, this disciplinary diversity generates questions about how complex GIS skills are being taught to students who do not necessarily have a background in geography or the spatial sciences. This study compares undergraduate and graduate course curricula to determine what topics, techniques and theory are being addressed and prioritized in GIS courses across pedagogical levels. We surveyed 1,698 courses being taught at 126 research universities in the United States. Our findings point towards a shift in pedagogy that favours technique-or application-centred lessons at advanced levels that require less background knowledge in geography than more theory-centred teaching methods. Our findings also suggest that a large number of graduate courses, even introductory courses ones, fail to address many fundamental GIS concepts and theories. Together, the results suggest a need for standards to ensure that students enrolled in GIS classes are properly trained to enter the workforce with appropriate skills.
Spatial interpolation methods translate sediment contamination point data into informative area-based visualizations. Lake Erie was first sampled in 1971 based on a survey grid of 263 locations. Due to procedural costs, the 2014 survey was reduced to 34 sampling locations mostly located in deep offshore regions of the lake. Using the 1971 dataset, this study identifies the minimum sampling density at which statistically valid, and spatially accurate predictions can be made using ordinary kriging. Randomly down-sampled subsets at 10% intervals of the 1971 survey were created to include at least one set of data points with a smaller sample size than that of the 2014 dataset. Regression analyses of predicted contamination values assessed spatial autocorrelation between kriged surfaces created from the down-sampled subsets and the original dataset. Subsets at 10% and 20% of the original data density accurately predicted 51% and 75% (respectively) of the original dataset's predictions. Subsets representing 70%, 80% and 90% of the original data density accurately predicted 88%, 90% and 97% of the original dataset's predictions. Although all subsets proved to be statistically valid, sampling densities below 0.002 locations/km2 are likely to create very generalized contamination maps from which environmental decisions might not be justified.
A wealth of resources for economic prosperity have driven development along the shorelines of the Great Lakes for over 150 years. The rapid growth of industrial, agricultural, and residential land use has degraded many natural components of lake ecosystems, including sediments and water quality. In this dissertation, spatiotemporal patterns of non-essential heavy metal sediment contamination in Lake Erie will be examined from historic and contemporary sediment surveys. Three inter-related studies explore innovative methods for improving the validity and overall usefulness of sediment contamination maps that could be used by a variety of stakeholders in pollution control efforts throughout the Lake Erie basin. First, sediment survey designs are analyzed for their utility in creating valid interpolated surfaces from which spatiotemporal comparisons of mercury sediment contamination can be compared over time. The next study explores how ancillary sediment variables and contamination categorization methods can support interpolated maps of cadmium sediment contamination from low-density sediment surveys. The final study introduces a novel method of three-dimensional geovisualization to enhance the geographic representation of lead sediment contamination patterns throughout the Lake Erie basin. Innovative research methodologies designed for this dissertation may be applied to sediment contamination studies in other Great Lakes. The visualization techniques employed in mapping sediment contamination patterns provide strong scientific evidence for spatiotemporal change in non-essential heavy metal pollution throughout Lake Erie. Combined, the research findings and maps produced throughout this dissertation can contribute to the growing body of knowledge used in environmental decision making for pollution control in the Great Lakes basin
A wealth of resources for economic prosperity have driven development along the shorelines of the Great Lakes for over 150 years. The rapid growth of industrial, agricultural, and residential land use has degraded many natural components of lake ecosystems, including sediments and water quality. In this dissertation, spatiotemporal patterns of non-essential heavy metal sediment contamination in Lake Erie will be examined from historic and contemporary sediment surveys. Three inter-related studies explore innovative methods for improving the validity and overall usefulness of sediment contamination maps that could be used by a variety of stakeholders in pollution control efforts throughout the Lake Erie basin. First, sediment survey designs are analyzed for their utility in creating valid interpolated surfaces from which spatiotemporal comparisons of mercury sediment contamination can be compared over time. The next study explores how ancillary sediment variables and contamination categorization methods can support interpolated maps of cadmium sediment contamination from low-density sediment surveys. The final study introduces a novel method of three-dimensional geovisualization to enhance the geographic representation of lead sediment contamination patterns throughout the Lake Erie basin. Innovative research methodologies designed for this dissertation may be applied to sediment contamination studies in other Great Lakes. The visualization techniques employed in mapping sediment contamination patterns provide strong scientific evidence for spatiotemporal change in non-essential heavy metal pollution throughout Lake Erie. Combined, the research findings and maps produced throughout this dissertation can contribute to the growing body of knowledge used in environmental decision making for pollution control in the Great Lakes basin
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