• Spectral Differentiation of Old-Growth Ponderosa Pine Forest in Southern Arizona

  Marcus, Matthew; Kingsley, Austin (The University of Arizona., 2026)

  Old-growth stands of Rocky Mountain ponderosa pine (Pinus scopulorum) preserve unique forest ecosystems within the mountain ranges of the Sky Islands of southern Arizona. The spatial distribution of intact tracts of these mature forests, however, remains largely undocumented. Accurate field-based age determination, for the purpose of the confirmation of meaningful delineation between mature and regenerating forests, is limited by the associated logistical burden. This study examines the viability of the use of multispectral satellite imagery to predictively differentiate old-growth and new-growth ponderosa pine based on measurable spectral characteristics to focus field-based confirmation. A supervised machine learning classification model using a Random Forest algorithm is trained using Landsat surface reflectance imagery from Metolius Research Natural Area within Deschutes National Forest in Oregon, a designated research area of ponderosa pine forest containing significant known old-growth footprint. Prediction layers for the model are derived from vegetation and moisture indices, spectral reflectance, canopy and organic matter density, and soil exposure. The results of this model are then applied to a selected section of the Coronado National Forest in the vicinity of the Mount Lemmon Wilderness Area to predictively map the spatial extent of old-growth ponderosa pine forests in the region. Field observations in areas of potential old-growth reported by the model are used to confirm the accuracy of the classifications. This study provides a reproducible remote sensing framework for regional identification and monitoring of old-growth ponderosa pine forests within the Sky Island region of Arizona and demonstrates the effectiveness of machine learning ecological classification.
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  SPATIAL ANALYSIS AND HABITAT SUITABILITY MODELING OF CULEX QUINQUEFACIATUS IN TARRANT COUNTY, TEXAS (2020-2022)

  Marcus, Matthew; Salas, Nicholas (The University of Arizona., 2026)

  Mosquito-borne diseases pose a significant public health threat, requiring local agencies to implement control strategies using an Integrated Mosquito Management approach. Given limited resources, identifying high-risk areas is essential for effective environmental control of West Nile virus (WNV). This study evaluates the spatial patterns and environmental predictors of adult female Culex quinquefasciatus abundance in Tarrant County, Texas, from 2020 to 2022. Spatial analysis and Empirical Bayesian Kriging were employed to identify abundance clusters and visualize county-wide distribution. In comparing global and local regression frameworks, Multiscale Geographically Weighted Regression (MGWR) outperformed standard models (Adjusted R-Squared = 0.393; AICc = 349.29). This demonstrated that environmental drivers operate across distinct spatial scales, with average temperature and NDVI emerging as the most significant local drivers, while distance to wetlands and population density exhibited broader influence. Finally, a habitat suitability map was developed using a weighted overlay approach to identify potential breeding areas. These combined analyses improve the understanding of vector distribution and provide a framework for targeted control efforts, resource prioritization, and public outreach in vulnerable communities to reduce WNV risk.
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  Ecological impacts of the Laguna Wildfire using dynamic mapping

  Marcus, Matthew; Haaser, Stephanie (The University of Arizona., 2026)

  In 2009, the Fire Executive Council released an updated guideline that restricted the classification of wildland fires to two categories: prescribed fire and wildland fire. In removing the historic wildland fire category, “Wildland Fire Use,” the Fire Executive Council removed an important wildland fire classification that could help researchers answer questions posed by the September 2022 National Prescribed Fire Program Review. This study demonstrates the importance of using additional wildland fire categories by evaluating the environmental data obtained from a dynamic mapping of a single wildland fire event. In this case study, satellite data was used to parse the 2025 Laguna Wildfire into three categories: Origin, Wildland Fire Use, and Escaped Fire. Satellite data was then employed to evaluate the severity of the wildland fire and the debris flow through the Rio Gallina originating from each category of the fire. A Differenced Normalized Burn Ratio (dNBR) analysis of the Laguna Fire demonstrates that the Laguna Fire burned with a primarily very low to low severity. The Escaped Fire had the highest percentage of moderate severity and severe fire, resulting in the largest impacts to the Rio Gallina watershed.
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  SUITABILITY ANALYSIS FOR A LEVEL II TRAUMA CENTER IN MISSISSIPPI

  Marcus, Matthew; Freeseman, Benton (The University of Arizona., 2026)

  As populations expand and age, the demand for rapid and reliable access to trauma care continues to increase, making the placement of trauma facilities critical for saving lives. This project determines the most appropriate locations for a Level II trauma center within the state of Mississippi. The study examines deficiencies in current trauma care coverage and identifies where an additional facility could improve emergency medical response. The analysis integrates spatial data on transportation infrastructure, existing trauma hospitals, population distribution, demographic risk factors, environmental hazards, and land-use constraints to support the site selection process. The research applies both vector and raster spatial methods to evaluate accessibility and potential service demand. Key variables include proximity to major road networks, distance from existing Level I, II, III, and IV trauma hospitals, census tract-level population change, and the spatial distribution of age-related injury risk. The model also incorporates environmental and land-use limitations, including national forests, protected wildlife areas, agricultural land, and flood-risk zones, to remove unsuitable development areas. The study conducts a Boolean suitability model to determine locations that satisfy essential criteria and a weighted suitability model to prioritize candidate sites based on the relative importance of each variable. Results classify regions as most suitable, suitable, low suitability, or unsuitable zones and highlight locations that could strengthen trauma care availability throughout Mississippi.
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  BUILDING A WEBGIS APPLICATION FOR LOCATING PARKS, PRESERVES, AND AMENITIES FOR ST. LUCIE COUNTY, FLORIDA

  Lukinbeal, Chris; Ruffing, Milo Bryce (The University of Arizona., 2026)

  Parks and recreational facilities are an important amenity that most Americans expect their local governments to be able to provide. Having access to parks has been shown to improve physical activity levels, as well as reduce stress and promote social cohesion. However, many residents are unfamiliar with the locations and the types of recreational facilities that are available to them in their communities. As information seeking increasingly moves to an online medium, local governments must do the same, making information about their recreational facilities available in an easy-to-use, accessible format online. The goal of this project is to construct an interactive web application for the parks and recreation of St. Lucie County, Florida. This feature aims to allow residents to learn more information about parks within the county, incorporating features that search by distance, by desired amenity, or simply by viewing the park boundaries on a web map. A combination of orthoimagery and fieldwork is used to confirm and update the features of each park to ensure that the list of amenities is accurate and up to date. ArcGIS Pro is used to update the dataset. ArcGIS Online, ArcGIS Experience Builder, and ArcGIS Solutions are used to construct a web application, and to ensure that it is accessible and user-friendly. This project provides a much-needed update to St. Lucie County’s parks database and supports the county’s ability to provide accurate and accessible information to its constituents in a digital format.
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  STREAMLINING HIGH-RESOLUTION NAIP LAND-COVER CLASSIFICATION USING AUTOMATED PREPROCESSING AND BUILDING FOOTPRINT INTEGRATION

  Marcus, Matthew; Lambert, Brendan Kilcoyne (The University of Arizona., 2026)

  High-resolution land-cover classification using imagery from the National Agriculture Imagery Program (NAIP) presents challenges due to high spatial resolution and resulting spectral heterogeneity. These conditions produce class confusion, particularly for impervious surfaces such as buildings. This study evaluated classification workflows in ArcGIS Pro across four study areas, representing both 30-centimeter and 60-centimeter spatial resolutions, to examine whether automated preprocessing and building footprint integration improve classification performance. Two classification strategies were compared: a baseline workflow, using Red, Green, Blue, and Near-Infrared bands, and an engineered raster stack incorporating spectral indices and texture measures generated through an automated ArcPy preprocessing tool. Building footprint data was integrated as a post-classification step to improve classification accuracy of impervious surfaces. Classification performance was evaluated using spatially independent validation samples and confusion matrices reporting overall (OA), producer’s (PA), and user’s accuracy (UA). Results varied among sites. At the 30-centimeter sites, the engineered workflow and building footprint integration produced minimal change in OA, indicating that baseline spectral information already captured sufficient class separability. At one 60-centimeter site, the baseline workflow performed poorly, with 19.05% OA, while the engineered workflow increased accuracy to 83.33%. Building footprint integration further improved accuracy to 85.71% and increased impervious PA to 100%. At the second 60-centimeter site, the engineered workflow showed only modest improvement. Across all sites, impervious surfaces remained the most difficult class to detect. These results demonstrate that automated preprocessing and building footprint integration can improve classification robustness under varying image conditions, but their effectiveness depends on site-specific imagery characteristics.
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  HELEHANA: ESRI WEB APPLICATION DEVELOPMENT FOR EMPLOYEE TRANSPORTATION SOLUTIONS

  Marcus, Matthew; Kuroki, Kayla E. (The University of Arizona., 2026)

  This project aimed to develop a web application to optimize employee transit between parking areas and workspaces. The study location references a strip of hotels on Maui’s west coast. The application employs GIS tools, such as network analysis and tables, to analyze spatial data, generate transit routes, reduce travel time, and improve communication. The system incorporates real-time processing to accommodate dynamic conditions using curated General Transit Feed Specification data and builds a model within a live ArcGIS web application. Key outcomes included improved efficiency, streamlined transit coordination, and a more organized workplace layout. This application demonstrates the potential of GIS tools to solve complex logistical problems in organizational environments, thereby increasing productivity and employee satisfaction.
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  Greensboro Fire Department Battalion Analysis

  Marcus, Matthew; Budzinski, Rachel Claire (The University of Arizona., 2026)

  The Greensboro Fire Department currently operates twenty-seven fire stations and plans to add six more over the next decade, increasing the demand on its command structure. The City of Greensboro has five battalions, which are already at the maximum span of control for personnel, requiring the addition of a sixth battalion chief to support both existing operations and future expansion. This project uses geographic information system service-area analysis to identify optimal locations for battalion redistricting and the station assignments for those battalion chiefs. To evaluate suitable placements, the study applies multiple spatial analysis methods, including drive-time service areas, hot spot analysis, kernel density estimation, and spatial workload balancing. These techniques are used to examine station coverage, call volume distribution, and the geographic alignment of organizational boundaries, ensuring that high‑demand areas remain adequately protected and that battalion workloads are evenly distributed. The analysis identified Stations 52, 63, 61, 43, 17, and 4 as the most effective locations for battalion chief assignment based on spatial distribution, workload balance, and citywide coverage. These recommendations support efficient coverage, effective personnel management, and long-term operational stability as new stations open. These locations provide strong coverage across the City of Greensboro. This study demonstrates how spatial analysis can inform organizational structure and operational planning in fire service management.
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  MAPPING LEGENDS: A CARTOGRAPHIC NARRATIVE AND DEDUCTIVE GIS ANALYSIS OF THE PADRES’ LOST SILVER STORY

  Lukinbeal, Chris; Mason, Jennifer; Collier, Siana Alexandrya (The University of Arizona., 2026)

  This study examines the Padres’ Lost Silver legend as a cartographic narrative and evaluates its geographic plausibility through GIS-based modeling of eighteenth-century mule transport across the American Southwest. The narrative describes a mule train traveling north through canyon systems, crossing the Mogollon highlands, and reaching the vicinity of the San Francisco Peaks before concealing a cache of silver. Rather than attempting historical verification, this study tests whether the described movement is consistent with environmental constraints. A raster-based cost surface was developed using slope derived from 30-meter digital elevation models and distance to hydrologic features, weighted to reflect mule transport limitations. Cost-distance and least-cost path analyses were used to simulate movement toward Santa Fe, NM and identify terrain-constrained travel corridors. Results indicate that movement across the region is highly constrained, with travel funneled into a limited number of low-cost corridors controlled by terrain and water availability, particularly along the Mogollon Rim. These corridors align with the narrative’s reference to a “pass of the Mogollones” and canyon-based movement toward the San Francisco Peaks, where the cache was hidden en route to Santa Fe, NM. When additional constraints are applied, the model identifies a small number of concentrated zones south of the peaks as the most plausible areas for the Spanish silver cache. These findings demonstrate that GIS can be used to evaluate narrative plausibility as a spatial problem.
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  ASSESSING LAND SUITABILITY FOR THE COSMIC EXPLORER OBSERVATORY NEAR BEND, OREGON USING BOOLEAN SPATIAL ANALYSIS

  Lukinbeal, Chris; Guzman, Alyssa Maria (The University of Arizona., 2026)

  This study evaluated the suitability of locations near Bend, Oregon, analyzing approximately 36,269 km2 of land, for the construction of the 40 km by 40 km Cosmic Explorer, a next generation gravitational wave observatory, using a Boolean analysis. Site selection prioritized areas with flat, geologically stable terrain with minimal environmental risk and low human disturbance. Terrain suitability was assessed using Local Moran’s I spatial autocorrelation with 40 nearest neighbors to identify clusters of similar slope values, while the Topographic Position Index classified landforms representing valleys, ridges and flat areas. Raster reclassification was applied to the land cover raster data. Buffer analyses were used for active oil wells, buildings, major roads, railroads, hydrological flowlines, wind farms, mines and national historic places. Rasterization was applied to pipelines, FEMA flood zones, and protected wildlife areas. Each analysis produced a Boolean layer with values of 1 for locations meeting suitability criteria and 0 for locations considered inadequate. All Boolean layers were combined using raster math to produce a consolidated suitability map. The final analysis determined that approximately 24,826 km2 (~70% of the study area) met all criteria for potential Cosmic Explorer construction. These results provide a reproducible framework for identifying potential locations for large-scale observatories, combining terrain, infrastructure, hydrology, and cultural data to address scientific, environmental, and societal considerations.
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  FROM STATIC TO DYNAMIC: DEVELOPING AN INTERACTIVE COMMUNITY RISK ASSESSMENT FRAMEWORK TO SUSTAIN FIRE SERVICE ACCREDITATION IN DENTON, TX

  Lukinbeal, Chris; Cox, Brian (The University of Arizona., 2026)

  Traditional Community Risk Assessments (CRAs) and Standards of Cover (SOCs) enable fire departments to evaluate hazards, allocate resources, and support accreditation and strategic planning, but as static reports, they limit spatial analysis, obscure localized risk variations, and quickly become outdated as community conditions change; potentially hindering timely responses and equitable resource distribution. This project develops an interactive, web-based CRA framework for the Denton Fire Department in Denton, Texas, transforming static reporting into a dynamic geospatial decision-support tool. Leveraging Geographic Information Systems (GIS), it integrates historical fire incident data, Esri Business Analyst-derived demographic and socioeconomic indicators (including population density, housing characteristics, and vulnerability factors), land use data, and building information to map spatial risk patterns. Employing spatial analysis techniques such as density mapping, overlay analysis, and exploratory spatial data analysis, the framework identifies geographic concentrations of risk and potential service gaps. Results are delivered through an interactive web GIS application that allows users to visualize risk layers, explore community characteristics, query data, and support real-time, data-driven planning decisions. By evolving a conventional planning document into a living geospatial platform, this approach enhances transparency, promotes proactive community risk reduction, improves resource targeting, and strengthens overall fire service planning and accreditation efforts.
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  Automated Wildfire Hazard Modeling Using Google Earth Engine

  Marcus, Matthew; Robbins, Heather Marie (The University of Arizona., 2026)

  Timely wildfire hazard assessments are critical for disaster preparedness and response. Current approaches to susceptibility modeling are tedious, labor and data-intensive, and often reliant on proprietary software. These limitations can lead to inconsistent workflows, reduced reproducibility, and delays in delivering actionable information for mitigation strategies. This project delivers an automated, open-source workflow designed to estimate wildfire hazard for any study area within the United States. Using Google Earth Engine, the model integrates meteorological, vegetation, and terrain variables obtained from publicly accessible geospatial data. Users can modify the area of interest, define start and end dates for the analysis period, and adjust coefficient weights for model variables to better reflect environmental and temporal dynamics surrounding wildfire seasons. The workflow is tested using the 2024 Line Fire in the San Bernardino National Forest. The primary result of the project is a comprehensive script that generates a Wildfire Hazard Index raster. Built-in helper functions provide flexibility and adaptation for different regions and timeframes, supporting resource managers and emergency planners. By automating laborious tasks of data acquisition and pre-processing, the workflow allows producing consistent and repeatable hazard assessments that can support better decision-making.
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  Community Partnership Portal: A Geospatial Decision-Support System for St. Theresa Catholic School

  Lukinbeal, Chris; Serna, Eric (The University of Arizona., 2026)

  Geographic Information Systems (GIS) are often associated with large organizations. However, they are equally vital for community-based entities. These systems enable the visualization of disparate databases and improve resource assignments. Currently, St. Theresa Catholic School faces a significant challenge: the lack of a centralized, strategic approach to managing and leveraging community assets. Instead, the school operates using abstract lists and fragmented data sources. This impedes its ability to systematically identify, connect with, and engage potential partners for institutional development. There is no efficient mechanism for translating available community resources into actionable opportunities. As a result, the school experiences administrative inefficiencies and unrealized partnership potential. This project addresses the problem by employing a systematic, multi-stage methodology. ArcGIS Business Analyst is used to query targeted NAICS codes within a 10-mile radius of Sugar Land, Texas. ArcGIS Hub and Survey123 are integrated to construct a referral engine that aligns school needs with the capabilities of local nonprofits and corporations. The approach involves building a verified geospatial database of at least 50 viable partners. The project also develops a fully centralized Partnership Portal that automates spatial data mining and eliminates manual web scraping. By shifting from a deficit-based model to a strengths-based perspective, this case study offers educational leaders a replicable framework. The primary objective is to reduce bureaucratic obstacles and promote planned growth. The tool aims to break down traditional barriers between the campus and local corporate and nonprofit sectors. It fosters a unified network of shared community value. A successful portal can serve as a model for other parochial and independent schools. These schools could maximize their local impact.
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  Automating Military Terrain Analysis: Design and Implementation of an ArcGIS ModelBuilder Toolbox for Terrain Analysis and Modified Combined Obstacle Overlay (MCOO) Generation

  Lukinbeal, Chris; McMullen, Brian J. (The University of Arizona., 2026)

  Terrain analysis is a key component of military planning and operational decision-making. Military terrain analysis still relies on manual geographic information system (GIS) workflows that depend on analyst experience. This project develops an automated ArcGIS toolbox built in ModelBuilder to support terrain analysis for Intelligence Preparation of the Operational Environment (IPOE) by generating repeatable terrain products derived from Observation and Fields of Fire, Avenues of Approach, Key Terrain, Obstacles, and Cover and Concealment (OAKOC) analysis for a selected area of interest. The workflow uses common geospatial datasets, including digital elevation models, land cover data, hydrological layers, and transportation networks. These datasets are processed through raster analysis tools that calculate terrain characteristics such as slope, visibility, and movement difficulty. The model generates terrain outputs associated with OAKOC analysis and identifies potential avenues of approach. The study tested the workflow by running the model multiple times and applying it to two case study areas in Europe and South America. In testing, the automated workflow completed terrain analysis in approximately fifteen minutes per run, while the equivalent manual workflow required several hours of analyst interaction. The model also allows analysts to apply the same process to new locations without having to rebuild the analysis. The results show that automated geospatial workflows can strengthen military terrain analysis by providing faster, repeatable results that support military planning while allowing analysts to apply professional judgment when interpreting terrain effects.
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  Supervised Classification of Stinknet in Maricopa County, Arizona

  Korgaonkar, Yoganand; Caulkins, Corey (The University of Arizona., 2025)

  Stinknet (Oncosiphon pilulifer) is an invasive plant species in Arizona that has rapidly expanded across Maricopa County since 2016, becoming a significant noxious weed. Management efforts have combined fieldwork with remote sensing techniques. Among these, supervised classification of high-resolution drone imagery using machine learning has proven effective; Maricopa County has applied drone-based classification since 2023 to guide its treatment program. Satellite imagery has also shown promise, though its coarser spatial resolution has led to more limited use. This study evaluates the prediction skill of satellite and aircraft-based stinknet classification in Cave Creek Regional Park, comparing two imagery sources—National Agriculture Imagery Program (NAIP) and PlanetScope—against existing drone-based classification results from spring 2023. NAIP imagery offers sub-meter resolution but is only captured after the optimal flowering period of stinknet, while PlanetScope provides coarser three-meter resolution imagery available year-round. Classification was performed in ArcGIS Pro for each imagery source using both the random forest and support vector machine methods. A confusion matrix comparing each classification to the drone-derived dataset was generated to assess relative accuracy. Both NAIP and PlanetScope classifiers demonstrated statistically significant prediction skill, and PlanetScope classifiers achieved significantly more accurate results than NAIP classifiers.
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  A HABITAT SUITABILITY ANALYSIS OF THE KIT FOX IN COLORADO

  Korgaonkar, Yoganand; Shepherd, Channing (The University of Arizona., 2025)

  As human population continues to increase, cities will keep growing and more critical wildlife areas will be transformed for urban development. As this progresses and more wildlife species become endangered, conservationists will play a vital component in the future development expansions and help to maintain ecosystems. One of those already endangered species is the Kit Fox (Vulpes macrotis), which plays an important role in keeping Colorado’s ecosystem in balance. Due to urban development as well as other human factors such as roads/driving, the Kit Fox’s population continues to dwindle, placing them on the Colorado Threatened and Endangered List. This project conducts a suitability analysis within the state of Colorado to determine suitable locations for Kit Fox populations to be moved to for conservation efforts. Using a variety of different factors that pertain to the Kit Fox’s survival needs as inputs, both a binary and a weighted suitability method were conducted to find alternative suitable locations for the Kit Fox to thrive in within Colorado. Future conservationists and scientists can use the results from this study to assist with recommendations to move Kit Fox populations to restore their population numbers, remove them from the Colorado Threatened and Endangered List, and keep the Kit Fox from vanishing in Colorado.
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  Echoes of Fire: Geospatial Analysis of Terrain, Legacy Burns, and Wind-Aligned Severity During the 2025 Forsyth Fire, Pine Valley, Utah

  Korgaonkar, Yoganand; Farner, Jennifer (The University of Arizona., 2025)

  This study investigated how topography, vegetation moisture, and legacy fire perimeters influenced burn severity and directional spread during the 2025 Forsyth Fire in Pine Valley, Utah. The analysis integrated remote-sensing, topographic, and meteorological data within ArcGIS Pro 3.5.3 to quantify spatial controls on fire behavior. Differenced and relativized Normalized Burn Ratio (dNBR, RdNBR) indices derived from pre- and post-fire Landsat 8 OLI composites were used to map burn severity. In contrast, Normalized Difference Vegetation and Moisture Indices (NDVI, NDMI) were used to assess pre-fire fuel conditions and post-fire canopy moisture changes. Topographic derivatives from the 10 m National Elevation Dataset—slope, aspect, and elevation—were summarized using zonal statistics. Mean burn severity increased from reburn to control zones, with dNBR ranging from 88 to 174. Steeper slopes (20.18 ± 0.95°) and south-facing aspects (southness = –0.14 ± 0.08) were associated with higher dNBR (r = 0.57) and greater moisture loss (r = –0.69), confirming the influence of solar exposure and terrain inclination. Wind-alignment analysis indicated that approximately 70 percent of perimeter expansion occurred within 30° of the dominant southwest-to-northeast wind vector (mean azimuth = 41.1° ENE), linking burn severity to directional spread. Overall, the Forsyth Fire exhibited enhanced intensity along steep, south-facing ridges intersecting the 2016 Saddle Fire scar, suggesting that residual fuels and terrain channeling amplified severity toward the Pine Valley community. The results demonstrate that integrated geospatial modeling effectively characterizes the interactions among terrain, wind, and fuel that control wildfire behavior.
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  Stratospheric Balloon Landing Suitability Analysis in Arizona, USA

  Korgaonkar, Yoganand; Park, Jay (The University of Arizona., 2025)

  Stratospheric balloons are increasingly important platforms for atmospheric research, remote sensing, and testing space-bound technologies. A critical operational challenge for high-altitude balloon operators is rapidly identifying safe and viable landing zones, especially in emergencies. This study aims to (1) quantify which geographic, demographic, and regulatory factors should most affect landing zone suitability across Arizona, and (2) produce a decision-support map to enable high-altitude balloon operators to quickly select safe landing areas. To achieve this, the study utilizes the Analytical Hierarchy Process (AHP) to assign weights to multiple criteria – including population density, controlled airspace, powerline proximity, and Gap Analysis Project (GAP) Status Codes – based on expert/operator judgements. The study then applies these weights to spatial data within a GIS framework to generate a high-resolution suitability map, classifying areas from “highly suitable” to “not suitable.” The resulting output provides a tool that operators can reference in real time to reduce decision-making time and enhance public safety.
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  PROTECTING DRINKING WATER RESOURCES: A CONSERVATION PRIORITY INDEX FOR VULNERABLE SOURCE WATER AREAS IN NEW HAMPSHIRE

  Korgaonkar, Yoganand; Rueda, Corine (The University of Arizona., 2025)

  Growing development pressure and increasingly heavy rainfall events threaten New Hampshire's surface waters and the drinking water supplies they support. While Source Water Protection Areas have been designated around public water supplies, many lack formal conservation protection or planning. This project uses geospatial analysis to identify and prioritize unprotected areas for conservation based on vulnerability and landscape sensitivity. Using ArcGIS Pro, statewide datasets were integrated to assess multiple risk factors: impaired waterways, land cover-based runoff potential, FEMA flood hazard zones, and terrain-derived hydrological characteristics. These factors were standardized to construct a Conservation Priority Index identifying where water resources face the greatest cumulative threats. Results demonstrate that many Source Water Protection Areas remain unprotected and overlap with impaired waterways or areas with elevated runoff potential. High-priority zones are concentrated in central and southeastern regions, where land development pressure intersects with complex topography. The Conservation Priority Index provides a spatial support tool to guide conservation strategies that safeguard public water supplies. By identifying where protection efforts would yield the greatest benefit, this index helps target limited conservation resources more effectively. As development continues and climate change intensifies precipitation events, strategic protection of Source Water Protection Areas will become essential for ensuring safe drinking water and maintaining resilience across New Hampshire.
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  SPATIOTEMPORAL PATTERNS IN BEHAVIORAL & OPPORTUNISTIC CRIME IN DENVER, COLORADO (2020–2025)

  Mason, Jennifer; Schoenfelder, Brittany (The University of Arizona., 2025)

  Understanding how different types of crime respond to major societal disruptions is essential for effective public safety planning and resource allocation. This study explores the spatiotemporal patterns of reported crime in Denver, Colorado, from 2020 to 2025, with a focus on changes in crime type and frequency across phases of the COVID-19 pandemic. Denver’s consolidated city-county jurisdiction ensures consistent geographic coverage and data reliability. Crimes are grouped into two broad categories: behavioral offenses, such as assault and domestic violence; and opportunistic offenses, such as burglary and theft. Comparing these classifications provides insight into how offender motivations may vary in response to external pressures such as lockdowns, economic instability, and diminished public activity. Using geocoded incident data aggregated at the census tract level, this study applies geographic information systems (GIS) and interactive visualizations in Tableau Public to identify emerging trends and spatial shifts over time. The project adopts an exploratory approach, allowing users to filter and compare crime patterns across pandemic phases and neighborhoods. Preliminary findings suggest that behavioral and opportunistic crimes responded differently to pandemic-related disruptions. This dashboard-driven model highlights how large-scale societal changes can influence criminal behavior and underscores the necessity for adaptable, data-informed public safety strategies. The framework is adaptable to other cities, temporal contexts, or forms of disruption, demonstrating the utility of exploratory geovisualization tools in understanding complex urban dynamics.

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