RSS 1.0UA Graduate and Undergraduate Research
ABOUT THE COLLECTIONS
The graduate and undergraduate research collections share, archive and preserve research from University of Arizona students. Collections include honors theses, master's theses, and dissertations, in addition to capstone and other specialized research and presentation topics.
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If you have questions about items in these collections, or are a faculty member who would like to provide students in your program an opportunity to showcase their research, please contact the Campus Repository team at repository@u.library.arizona.edu with your request. We look forward to working with you.
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IMPACT OF CLIMATE CHANGE ON WHISKY PRODUCTIONClimate change is altering environmental conditions critical to Scotch whisky production, specifically optimal precipitation and temperatures for barley yields. This study employs geospatial analysis to assess climate patterns and agricultural shifts within whisky-producing regions of Scotland. Climate data from the UK Met Office and agricultural reports from the Scottish Government are used to analyze temperature and precipitation trends impacting barley farming. Statistical modeling determines correlations between climate trends and whisky production factors, with choropleth maps and temporal analysis graphs visualizing the findings. The results provide insights into the vulnerabilities of whisky production and inform adaptation strategies for distilleries. Understanding these environmental impacts is crucial for sustaining the industry amid ongoing climate shifts.
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UNDERSTANDING FATAL CRASHES IN PORTLAND: A SPATIAL ANALYSIS OF SOCIOECONOMIC, BUILT ENVIRONMENT AND VEHICLE SPEED FACTORSThis article presents a case study analyzing the opportunities provided by the Portland open data repository on vehicle speed to explore the relationship between vehicle speed and road safety. While the influence of vehicle speed on road safety has been well-documented on highways and freeways, where free flow conditions are generally uninterrupted by pedestrians or bus stops, this study shifts focus to urban core roads, which include arterials and collector roads. These types of roads account for 69% of road fatalities in the U.S. and are characterized by a higher density of diverse road users, making the interaction between vehicle speed and safety more complex. Using Geographically Weighted Poisson Regression (GWPR), the study examines the associations between vehicle speed and fatal road crashes at the block group level. The goal is to assess the significance of vehicle speed in predicting fatal crashes while identifying spatial variability across the city. This analysis aims to provide insights that could inform localized interventions, particularly in ethnically diverse areas that disproportionately bear the burden of road fatalities.
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Increased Frequency and Potential Environmental Impacts from Oil Spills After Hurricane LandfallsThe increased frequency of oil spills, specifically after hurricanes, can have lingering effects throughout the ecosystem and can cause complexities while restorative efforts are underway. Certain environmentally sensitive areas require different restoration techniques to allow for proper removal of oil with minimal disturbance to the habitat. This project aims to compare oil spills immediately after a hurricane and a non-hurricane event, allowing for a visual representation of increased frequency. Displaying environmentally sensitive areas within the reach of oil spills will illustrate potential impacts of protected and vulnerable land. With numerous sources of publicly available data, we can display where and how much sensitive land may be impacted. Analyzing distance from oil spills, focusing on protected habitats, and concentrating on the most vulnerable and sensitive land will give a precise picture of the lasting impacts of a hurricane. This study looked at two different four-day periods. The first one during normal weather events, and the second was immediately after the landfall of Hurricane Ida. Results show that there was a 600 percent increase in pollution events over a four-day period. This project focuses on one specific hurricane event but provides valuable information. With more time and personnel this process can easily be scaled up to each hurricane that makes landfall in the U.S. Understanding increases in pollution events beforehand, knowing which areas are most vulnerable, and ensuring resources can be deployed easier and faster can allow for less impacts on wildlife and the environment.
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Wildland Fire Risk Analysis on the Fort Apache Indian ReservationWildfires pose a significant threat to natural resources, communities, infrastructure like homes on the Fort Apache Indian Reservation. This project developed a GIS- based wildfire risk assessment model utilizing available data and analytical tools in ArcGIS Pro. The analysis incorporated key environmental variables including fuel models from LANDFIRE, topographic features derived from USGS Earth Explorer, and proximity to communities. A weighted overlay approach was applied to classify areas into no risk. low, moderate, high and extreme wildfire risk zones. By adapting methodologies like kriging and weighted overlay, this study has ensured a replicable and objective assessment and framework. The final wildfire risk maps are able to support land managers in prioritizing mitigation efforts and resource allocation for planning and emergency response efforts.
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A Modelbuilder Workflow for Automating Contour Generation from High-Resolution Elevation Data in a Mosaic DatasetThis project streamlines the topographic-contour generation process for the New Mexico Bureau of Geology and Mineral Resources (NMBGMR). Historically, the NMBGMR generated contours internally to leverage access to high-resolution elevation rasters and maintain control over the level of detail and smoothness. Creating contours from elevation data involves a multi-step workflow requiring manual input, mosaicking, reprojecting, clipping, appending data, field calculations, and generalization. This work develops an automated geoprocessing tool using ModelBuilder in ArcGIS Pro, replacing manual steps with a simplified, repeatable process. The model uses a mosaic dataset to efficiently manage the multiple raster tiles used to generate contours. Integrated into the tool is the optional capability of unit conversion, allowing for the creation of contours in either meters or feet, automated clipping to a designated map extent, contour creation at designated intervals, appending to an existing feature class, and attribute calculations. Testing on map areas with steep, mountainous terrain confirmed that the model accurately replicates the original workflow while reducing complexity. The outcome is a user-friendly tool that standardizes contour creation and improves the efficiency of GIS specialists/cartographers when building map kits used by field geologists. This advancement allows for consistent and rapid production of accurate, map-scale appropriate contours to provide topographic context for the overlying geologic data and supports the production of high-quality cartographic layouts.
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Automated Evacuation Routing with ArcGIS Network AnalystEfficient and adaptive evacuation routing is essential for public safety during disasters such as wildfires, floods, and debris flows. Many traditional evacuation planning methods lack real-time adaptability and fail to account for road closures, congestion levels, and network constraints. This project develops an automated evacuation routing model using ArcGIS Network Analyst, integrating geospatial analysis techniques to generate optimized evacuation routes based on user-defined evacuation zones. A web-based application enables emergency managers to define evacuation zones by drawing a polygon, which triggers the routing model to compute optimal evacuation routes in real time. The model incorporates road closures, restricted access roads, and functional classifications to ensure that only available and suitable roads are used for evacuation. By analyzing residential parcel densities within the evacuation zone, the system assigns congestion penalties to road segments, dynamically influencing optimal route selection. The script automatically identifies exit points at the boundary of the evacuation zones where roads provide safe egress, ensuring logical and efficient evacuation paths. The model was tested using a road network dataset for Santa Barbara County to evaluate its effectiveness in real-world scenarios. This framework is scalable and adaptable, allowing emergency managers to tailor evacuation planning for various disaster scenarios and apply the model to different geographic regions and network datasets. By leveraging network analysis, GIS automation, and interactive web mapping, this project enhances disaster preparedness and response efforts, providing a flexible, real-time evacuation planning tool that supports data-driven decision-making and ensures safer and more efficient evacuations.
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Detached from Détaché: Availability of Orchestra Programs in a Subset of Public Arizona District SchoolsSchool orchestra programs provide instruction in the bowed string instruments (violin, viola, cello, double bass). Along with band and choir, orchestras comprise one of three main music performance ensembles found in schools. Despite orchestra’s importance in a well-rounded music education, orchestra is not as accessible as band or choir because orchestra is typically found in large, suburban, higher-income schools with majority White students. This study aimed to determine the availability of orchestra in a subset of public Arizona district schools, as well as attributes of the schools, such as student demographics and free and reduced-price lunch (FRPL) percentages. Public datasets containing enrollment and FRPL information were joined to an Arizona schools spatial dataset. The AZ School Report Cards website, AZ Arts Education Data Explorer, and individual school and district websites provided orchestra offering information. The schools were spatially joined to public school districts in Arizona, and finally descriptive statistics were calculated. Maps show where school orchestra programs are found as well as lacking, highlighting inequalities in access. In Maricopa County, public school districts in the east and northeast had the highest percentages of schools offering orchestra, with less representation in west and southwest public school districts. In Pima County, public school districts in and around Tucson had high percentages of schools offering orchestra.
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Predicting Areas of Debris Flows in Recently Burned Areas Using Precipitation ForecastsDebris flows are all too common in Southern California, especially in burned areas after a wildfire. This project predicts and informs of potential areas where a debris flow is likely to happen in the 10 counties of Southern California. Using digital elevation model data from the United States Geological Survey (USGS) and water catchment data from the United States Geological Survey’s Elevation Derivatives for National Applications (EDNA) database, the average slope was found in each water catchment, as debris flows follow the flow direction in the catchment. Using the National Interagency Fire Center’s fire perimeter data, recently burned areas were identified, as they are more susceptible to debris flows with the lack of vegetation holding the soil in place. Additionally, the National Weather Service’s National Digital Forecast Database (NDFD) was used to track predicted rainfall accumulation over 72 hours. This live data updates in 6-hour intervals, closely monitoring how much precipitation is expected. A model was created to analyze the data and make predictions on what is considered a no risk, low risk, medium risk, and high-risk area. An ArcGIS Dashboard was created to publicize the data, which provides information about what areas are prone to a debris flow, to help evacuate people and help utility crews with prevention and cleanup measures.
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Developing an Interactive Tool for Identifying Flood-Prone Areas Based on the Topographic Wetness IndexDetermining areas at risk of flooding is critical to minimizing the damage and losses that floods can cause. Geographic Information Systems can map regions of general flood vulnerability through more rapid methods than complex hydrological models. This project aimed to develop an easily operated tool to locate potentially flood-prone landscape areas by applying the Topographic Wetness Index to measure how terrain influences water runoff and accumulation. The result was an interactive Jupyter Notebook that provided detailed steps on using Python code blocks to perform index calculations. Functions of the tool included automating the downloading process for Digital Elevation Models based on user-provided coordinates and performing raster analysis to determine the input parameters of flow directions, accumulated flow, and slope gradients. A set of optional steps could process the index results according to the needs of the user through low-pass filtering, range scaling, and custom symbolization. The project provided an example of how the tool performs by comparing index values from Digital Elevation Models at differing resolutions to descriptions of localized flooding risks in Lake County, Oregon. The Topographic Wetness Index tool effectively demonstrated a practical approach for land use planning purposes that uses minimal inputs to identify areas susceptible to flooding.
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Investigating Trends, Causes, and Patterns of Idaho WildfiresWildfires have become increasingly more common in much of the West, posing a large threat to both human and natural resources. This study uses a variety of spatial statistical methods to investigate the patterns, trends, and causes of wildfires across the state of Idaho. Leveraging tools such as the Global Moran’s I and Hot Spot Analysis to identify significant clusters and emerging trends of fire patterns, Standard Deviational Ellipses were also employed to show the dispersion and orientation of wildfire occurrences. Regression analysis, including ordinary least squares (OLS) and geographically weighted regression (GWR), was used to examine how fire management practices influence the spatial patterns of wildfire incident sizes. The results indicate that human and natural-caused fires exhibit reverse patterns: human-caused fires tend to be smaller but more costly, whereas natural-caused fires tend to be larger but less costly. Unsurprisingly, human fires tend to be concentrated around higher population densities, while natural fires tend to occur in more remote areas. These findings highlight the importance of more localized and data-driven approaches to wildfire management and policy decision-making.
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REDLINING AND EDUCATION: INVESTIGATING THE LONG-LASTING IMPACT OF HISTORICAL HOUSING POLICIES IN HENNEPIN COUNTYThe U.S. government created the Home Owners' Loan Corporation (HOLC) as part of the New Deal under President Franklin D. Roosevelt. HOLC refinanced home mortgages to prevent foreclosures and stabilize the housing market. However, its practices, particularly redlining, had long-lasting negative effects on many minority communities. HOLC's grading system, which relied on discriminatory criteria based on racial composition and economic status, marginalized communities of color. The legacy of these practices continues to shape socioeconomic conditions today, as redlined neighborhoods still struggle with lower property values, limited access to resources, and persistent disparities in education, wealth, and healthcare. These enduring effects underscore the long-term consequences of institutional discrimination, which continue to impact affected communities across generations. This study examines the lasting effects of redlining on educational attainment in Hennepin County, part of the Twin Cities in Minnesota, by analyzing decennial census and American Community Survey data. The analysis focuses on key factors at the census tract level, including socioeconomic variables such as housing, employment, and income; demographic factors like race and disability; and access to resources, including technology and transportation. Using regression models, the study identifies significant relationships between these variables and low educational attainment. The results reveal that both low educational attainment and the associated variables vary spatially across Hennepin County.
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Geospatial Interpretation of MARSIS Data: A GIS Approach to Martian Subsurface AnalysisSubsurface water on Mars is key to understanding its geological evolution, climate history, and potential habitability. This study examines 1.5 billion radar signals from the Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS), a low-frequency radar sounder aboard the European Space Agency’s Mars Express orbiter, focusing on the strongest 5% of signals to detect subsurface structures. All 1.5 billion points were evaluated using clustering and statistical methods to detect large subsurface structures up to five kilometers deep, with more than 424 million points forming significant clusters. Using advanced signal processing (e.g., clutter suppression, noise filtering) and geospatial analyses (e.g., spatial clustering, autocorrelation, hotspot analysis, and cross-partition connectivity), the study identifies significant subsurface structures. The findings are validated by NASA’s Shallow Radar (SHARAD) data confirming known shallow ice deposits in mid-latitude regions like Utopia Planitia. Integrated into a GIS framework, these visualizations advance planetary science, astrobiology, astrogeology, and Mars exploration by enabling in-situ resource utilization (ISRU) and informing landing site selection. This study pioneers a GIS-based approach to globally map Martian subsurface water, integrating MARSIS and SHARAD data.
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Not in Kansas Anymore: Analyzing the Shift of Tornadoes in the United StatesTornadoes are a potential threat to life no matter how much one prepares. Many people in high-risk areas can become desensitized to this threat, but tornadoes are significant everywhere and can occur with zero notice. These high-risk areas rely on infrastructure and resource management to best deal with the consequences of tornadoes. Identifying the areas at high risk of tornadoes can better equip officials with the knowledge to aid residents with recovery more efficiently. Geographic Information Systems can be utilized to leverage historical data to identify these high-risk areas and predict which areas will see an increase in frequency and intensity based on future weather pattern predictions. To accomplish this task, a suitability analysis model is appropriate with an accompanying time series map that highlights how tornadoes have shifted geographically over time. This is centralized into three study areas: The Great Plains, Tornado Alley, and Dixie Alley. The results show which general study area has increased in intensity, as determined by the Enhanced Fujita Scale and overall frequency. Ultimately, tornadoes have shifted in frequency from the Great Plains and historical Tornado Alley to Dixie Alley, but everywhere has increased in intensity, with the past five years showing an increase in violent tornadoes.
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GEOSPATIAL AND TEMPORAL ANALYSIS OF AGGRAVATED ASSAULT HOTSPOTS IN PHOENIXInvestigating aggravated assaults can be challenging for law enforcement officers. Crime analysts can make the process more efficient by uncovering valuable insights from data. Using only data from calls for service in 2024, this project analyzes crime patterns in Phoenix, Arizona. This study examines the distribution of aggravated assaults across the city of Phoenix using 2024 calls for service data to identify crime hotspots and peak times for these incidents. The study employed Excel and ArcGIS Pro for data preparation, filtering, and geocoding to isolate aggravated assaults. Methods for geospatial analysis, like Kernel Density Estimation and Hot Spot Analysis, were applied to locate areas with high incident concentrations. A temporal analysis, using the time and date fields in the data, was conducted to identify patterns related to when assaults occurred more frequently. These patterns were at specific times and days of the week. There was a significant concentration of aggravated assaults in certain areas in Phoenix. Aggravated assaults peaked on weekends, showing a clear temporal pattern. During high-risk periods, law enforcement should increase patrols in identified hotspots. In addition, the analysis offers recommendations for confronting the factors or issues that contribute to these crimes. This project demonstrates how public safety data can improve crime prevention strategies by leveraging spatial and temporal analysis. As a result, law enforcement can allocate resources more efficiently.
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Development of Automated Main Replacement at South Jersey IndustriesWithin the utilities industry, a simple question is always pondered: When is the right time to replace old assets? Well, in the natural gas industry, that question is not something that can be seen nor heard, but only smelt. In the form of Mercaptan, the compound that makes natural gas smell and alerts of a leak in the gas system. This chemical compound has saved lives and prevented countless amounts of damage. Now, with the power of GIS, these leak points can be mapped like never before with incredible accuracy. However, with all the data, there must be a way to summarize and justify the cost benefits of leak locations related to sections of the Gas Main. This is where Automated Main Replacement (AMR) works to allow end users to justify and develop future projects and use GIS data to collect the necessary information needed to create a replacement project. This project will detail the workflow and concepts used to develop datasets and web applications that were synonymous with the business's current AMR workflow using GIS technologies.
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Flood Impact Analysis of Typhoon Yagi in VietnamOn September 7, 2024, Typhoon Yagi struck Northern Vietnam, severely affecting the coastal city of Hai Phong and neighboring Quang Ninh Province. The typhoon caused extensive flooding, significant infrastructure damage, and substantial economic and human losses. This research integrates remote sensing and GIS-based spatial analysis to evaluate the flood impacts and predict flood-prone areas. Sentinel-1 satellite data, processed using the Sentinel Application Platform (SNAP), was employed to extract flood-affected regions through pre- and post-crisis imagery differencing. Flood extent was identified by subtracting pre-crisis from post-crisis imagery, isolating inundated areas. Additionally, a predictive flood risk analysis was conducted using the weighted sum tool in ArcGIS Pro, incorporating seven key variables: DEM, terrain slope, drainage density, LULC derived from Sentinel-2 data, monthly rainfall data for September 2024 from PERSIANN-CCS, distance to roads, and distance to rivers. Each raster variable was reclassified into a standardized scale ranging from 1 (very low risk) to 5 (very high risk) to ensure comparability, followed by the assignment of weighted contributions to each variable based on its influence on flooding. The resulting analysis produced a flood risk map for the study area, highlighting the interplay of topography, hydrology, and infrastructure in flood dynamics. These findings offer critical insights for flood risk assessment, disaster response, and the development of mitigation strategies tailored to Hai Phong and Quang Ninh.
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Training and Assessment of a Damage Classification Deep Learning Model for the 2025 Palisades Fires in Southern CaliforniaProviding preliminary damage reports is essential to residents of post-disaster zones who need this information while planning their return to their property. As fire size, severity and frequency increase, it may become harder for local authorities to assess the amount of damage caused by these fires in a timely manner. High resolution satellite imagery of the 2025 Palisades Fire’s post disaster zone was used to train a deep learning model in ArcGIS Pro that classifies building footprint as damaged or undamaged. The model performed with high scores on several accuracy metrics, showing that off the shelf deep learning models can be applied to new data and trained to near perfect agreement, even on less powerful computers. With deep learning tools becoming more accessible, it may be wise to incorporate them as part of post disaster measures to maintain the public informed with real-time and accurate information. However, while these tools can be used alongside other demographic data to form relevant and informative damage reports, they suffer from accessibility issues like high imagery prices, high computing requirements, and expensive licensing that could make it difficult to apply this emerging technology in a broad range of scenarios.
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POTENTIAL EFFECTS OF METHANE EMISSION REDUCTION ON CALIFORNIA LEAST TERN BREEDING HABITATEffective climate change mitigation is complicated by economic factors, as renewable energy projects require significant upfront investment. Recently, the Bitcoin mining industry has begun investing in renewable energy, particularly projects powered by methane waste byproducts from other industries. These projects are promising for their low infrastructure needs and ability to reduce methane emissions that would otherwise be released into the atmosphere. Programs like the Endangered Species Act’s Cooperative Endangered Species Conservation Fund provide funding for conservation projects benefiting endangered species. These programs could support methane-fueled Bitcoin mining projects if a link between methane reduction and improved habitat for endangered species is established. This study used GIS to assess the impact of methane reduction on the breeding habitat of the California least tern, an endangered bird species. Using data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate modeling project, potential future habitat in 2050 was mapped under three projected climate scenarios developed by CMIP6 scientists. Alternative scenarios with 10%, 20%, and 50% reductions in methane emissions were then modeled using the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC) climate model for comparison with the unaltered versions. In two of the three scenarios, reducing methane emissions expanded the likely breeding habitat of the California least tern. However, in the third scenario, habitat suitability showed minimal change. This suggests that methane-fueled Bitcoin mining could be an effective climate change mitigation strategy, potentially improving habitat for endangered species, but further research is needed to confirm these findings.
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A Suitability Analysis of Dona Ana and El Paso Phase Settlement Patterns in the Jornada Mogollon Region of New MexicoThe goal of this project is to use raster-based GIS analytical methods, specifically suitability analysis, to identify and model landscape characteristics associated with archaeological site placement in southeastern New Mexico. This analysis focuses particularly on the environmental variables influencing the transition from mobile hunter-gatherer lifeways in the Jornada Mogollon/Doña Ana phase to increasingly sedentary agricultural settlements typical of the later El Paso phase. By integrating factors such as proximity to streams, slope, aspect, precipitation, soils, and landcover into a suitability model, this research aims to better understand how these variables influenced settlement decisions. During the Late Formative (AD 1000 – 1450), Jornada Mogollon populations adopted a mixed subsistence strategy characterized primarily by horticulture focused on indigenous plant species (e.g., chenopods), supplemented by maize agriculture. This adaptive approach would have had important implications for where communities established their settlements, likely emphasizing areas suitable for small-scale agriculture and resource-rich environments. Identifying landscape characteristics associated with semi-sedentary to sedentary residential sites will help clarify the environmental criteria guiding site selection and reveal broader patterns in prehistoric settlement decisions. This research offers insights into how environmental constraints shaped cultural adaptations and landscape utilization in the Jornada Mogollon region.
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Assessing the Relationship between Urban Heat and Vegetation in Albuquerque, New MexicoThe urban heat island effect poses a significant challenge for cities and urbanized areas, particularly those in warm climates. Vegetation, such as urban trees and parks, plays a crucial role in mitigating the effects of urban heat by helping to reduce land surface temperatures. This study explores the relationship between urban heat and vegetation in Albuquerque, New Mexico using remote sensing. To calculate land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) and identify patterns of heat distribution and vegetation density across the city, the study used Google Earth Engine (GEE) to process Landsat 8 imagery, and ArcGIS Pro to integrate social vulnerability data and help determine what areas would be most impacted by new vegetation. Additionally, this study assessed the accessibility of this methodology for local governments, emphasizing its potential as a cost-effective approach to urban heat mitigation. By demonstrating the utility of GEE and freely available satellite data, this study provides a framework for municipalities to make informed decisions in combating urban heat and enhancing climate resilience. Results showed that there is a positive relationship between urban heat and areas of low vegetation, and GEE is a valuable tool to help government agencies tackle urban heat.
